1
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Weissenow K, Rost B. Rendering protein mutation movies with MutAmore. BMC Bioinformatics 2023; 24:469. [PMID: 38087198 PMCID: PMC10714560 DOI: 10.1186/s12859-023-05610-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 12/08/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND The success of AlphaFold2 in reliable protein three-dimensional (3D) structure prediction, assists the move of structural biology toward studies of protein dynamics and mutational impact on structure and function. This transition needs tools that qualitatively assess alternative 3D conformations. RESULTS We introduce MutAmore, a bioinformatics tool that renders individual images of protein 3D structures for, e.g., sequence mutations into a visually intuitive movie format. MutAmore streamlines a pipeline casting single amino-acid variations (SAVs) into a dynamic 3D mutation movie providing a qualitative perspective on the mutational landscape of a protein. By default, the tool first generates all possible variants of the sequence reachable through SAVs (L*19 for proteins with L residues). Next, it predicts the structural conformation for all L*19 variants using state-of-the-art models. Finally, it visualizes the mutation matrix and produces a color-coded 3D animation. Alternatively, users can input other types of variants, e.g., from experimental structures. CONCLUSION MutAmore samples alternative protein configurations to study the dynamical space accessible from SAVs in the post-AlphaFold2 era of structural biology. As the field shifts towards the exploration of alternative conformations of proteins, MutAmore aids in the understanding of the structural impact of mutations by providing a flexible pipeline for the generation of protein mutation movies using current and future structure prediction models.
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Affiliation(s)
- Konstantin Weissenow
- Department of Informatics, Bioinformatics and Computational Biology i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching, Munich, Germany.
- TUM Graduate School, Center of Doctoral Studies in Informatics and Its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany.
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching, Munich, Germany
- Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748, Garching, Munich, Germany
- TUM School of Life Sciences Weihenstephan (WZW), Alte Akademie 8, Freising, Germany
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2
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Abakarova M, Marquet C, Rera M, Rost B, Laine E. Alignment-based Protein Mutational Landscape Prediction: Doing More with Less. Genome Biol Evol 2023; 15:evad201. [PMID: 37936309 PMCID: PMC10653582 DOI: 10.1093/gbe/evad201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 10/27/2023] [Accepted: 11/01/2023] [Indexed: 11/09/2023] Open
Abstract
The wealth of genomic data has boosted the development of computational methods predicting the phenotypic outcomes of missense variants. The most accurate ones exploit multiple sequence alignments, which can be costly to generate. Recent efforts for democratizing protein structure prediction have overcome this bottleneck by leveraging the fast homology search of MMseqs2. Here, we show the usefulness of this strategy for mutational outcome prediction through a large-scale assessment of 1.5M missense variants across 72 protein families. Our study demonstrates the feasibility of producing alignment-based mutational landscape predictions that are both high-quality and compute-efficient for entire proteomes. We provide the community with the whole human proteome mutational landscape and simplified access to our predictive pipeline.
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Affiliation(s)
- Marina Abakarova
- CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), Sorbonne Université, UMR 7238, Paris 75005, France
- Université Paris Cité, INSERM UMR U1284, 75004 Paris, France
| | - Céline Marquet
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748 Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748 Garching, Germany
| | - Michael Rera
- Université Paris Cité, INSERM UMR U1284, 75004 Paris, France
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748 Munich, Germany
- Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, Garching, 85748 Munich, Germany
- TUM School of Life Sciences Weihenstephan (TUM-WZW), Alte Akademie 8, Freising, Germany
| | - Elodie Laine
- CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), Sorbonne Université, UMR 7238, Paris 75005, France
- Institut universitaire de France (IUF)
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3
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Koludarov I, Velasque M, Senoner T, Timm T, Greve C, Hamadou AB, Gupta DK, Lochnit G, Heinzinger M, Vilcinskas A, Gloag R, Harpur BA, Podsiadlowski L, Rost B, Jackson TNW, Dutertre S, Stolle E, von Reumont BM. Prevalent bee venom genes evolved before the aculeate stinger and eusociality. BMC Biol 2023; 21:229. [PMID: 37867198 PMCID: PMC10591384 DOI: 10.1186/s12915-023-01656-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 06/29/2023] [Indexed: 10/24/2023] Open
Abstract
BACKGROUND Venoms, which have evolved numerous times in animals, are ideal models of convergent trait evolution. However, detailed genomic studies of toxin-encoding genes exist for only a few animal groups. The hyper-diverse hymenopteran insects are the most speciose venomous clade, but investigation of the origin of their venom genes has been largely neglected. RESULTS Utilizing a combination of genomic and proteo-transcriptomic data, we investigated the origin of 11 toxin genes in 29 published and 3 new hymenopteran genomes and compiled an up-to-date list of prevalent bee venom proteins. Observed patterns indicate that bee venom genes predominantly originate through single gene co-option with gene duplication contributing to subsequent diversification. CONCLUSIONS Most Hymenoptera venom genes are shared by all members of the clade and only melittin and the new venom protein family anthophilin1 appear unique to the bee lineage. Most venom proteins thus predate the mega-radiation of hymenopterans and the evolution of the aculeate stinger.
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Affiliation(s)
- Ivan Koludarov
- Justus Liebig University of Gießen, Institute for Insect Biotechnology, Heinrich-Buff-Ring 58, 35392, Giessen, Germany.
- Department of Informatics, Bioinformatics and Computational Biology, i12, Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany.
| | - Mariana Velasque
- Genomics & Regulatory Systems Unit, Okinawa Institute of Science & Technology, Tancha, Okinawa, 1919, Japan
| | - Tobias Senoner
- Department of Informatics, Bioinformatics and Computational Biology, i12, Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
| | - Thomas Timm
- Protein Analytics, Institute of Biochemistry, Justus Liebig University, Friedrichstrasse 24, 35392, Giessen, Germany
| | - Carola Greve
- LOEWE Centre for Translational Biodiversity Genomics (TBG), Senckenberganlage 25, 60325, Frankfurt, Germany
| | - Alexander Ben Hamadou
- LOEWE Centre for Translational Biodiversity Genomics (TBG), Senckenberganlage 25, 60325, Frankfurt, Germany
| | - Deepak Kumar Gupta
- LOEWE Centre for Translational Biodiversity Genomics (TBG), Senckenberganlage 25, 60325, Frankfurt, Germany
| | - Günter Lochnit
- Protein Analytics, Institute of Biochemistry, Justus Liebig University, Friedrichstrasse 24, 35392, Giessen, Germany
| | - Michael Heinzinger
- Department of Informatics, Bioinformatics and Computational Biology, i12, Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
| | - Andreas Vilcinskas
- Justus Liebig University of Gießen, Institute for Insect Biotechnology, Heinrich-Buff-Ring 58, 35392, Giessen, Germany
- Fraunhofer Institute for Molecular Biology and Applied Ecology, Department of Bioresources, Ohlebergsweg 12, 35392, Giessen, Germany
| | - Rosalyn Gloag
- Rosalyn Gloag - School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Brock A Harpur
- Brock A. Harpur - Department of Entomology, Purdue University, 901 W. State Street, West Lafayette, IN, 47907, USA
| | - Lars Podsiadlowski
- Leibniz Institute for the Analysis of Biodiversity Change, Zoological Research Museum Alexander Koenig, Centre of Molecular Biodiversity Research, Adenauerallee 160, 53113, Bonn, Germany
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology, i12, Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
| | - Timothy N W Jackson
- Australian Venom Research Unit, Department of Biochemistry and Pharmacology, University of Melbourne, Grattan Street, Parkville, Viktoria, 3010, Australia
| | | | - Eckart Stolle
- Leibniz Institute for the Analysis of Biodiversity Change, Zoological Research Museum Alexander Koenig, Centre of Molecular Biodiversity Research, Adenauerallee 160, 53113, Bonn, Germany
| | - Björn M von Reumont
- LOEWE Centre for Translational Biodiversity Genomics (TBG), Senckenberganlage 25, 60325, Frankfurt, Germany.
- Faculty of Biological Sciences, Group of Applied Bioinformatics, Goethe University Frankfurt, Max-Von-Laue Str. 13, 60438, Frankfurt, Germany.
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4
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Llorián-Salvador Ó, Akhgar J, Pigorsch S, Borm K, Münch S, Bernhardt D, Rost B, Andrade-Navarro MA, Combs SE, Peeken JC. The importance of planning CT-based imaging features for machine learning-based prediction of pain response. Sci Rep 2023; 13:17427. [PMID: 37833283 PMCID: PMC10576053 DOI: 10.1038/s41598-023-43768-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
Patients suffering from painful spinal bone metastases (PSBMs) often undergo palliative radiation therapy (RT), with an efficacy of approximately two thirds of patients. In this exploratory investigation, we assessed the effectiveness of machine learning (ML) models trained on radiomics, semantic and clinical features to estimate complete pain response. Gross tumour volumes (GTV) and clinical target volumes (CTV) of 261 PSBMs were segmented on planning computed tomography (CT) scans. Radiomics, semantic and clinical features were collected for all patients. Random forest (RFC) and support vector machine (SVM) classifiers were compared using repeated nested cross-validation. The best radiomics classifier was trained on CTV with an area under the receiver-operator curve (AUROC) of 0.62 ± 0.01 (RFC; 95% confidence interval). The semantic model achieved a comparable AUROC of 0.63 ± 0.01 (RFC), significantly below the clinical model (SVM, AUROC: 0.80 ± 0.01); and slightly lower than the spinal instability neoplastic score (SINS; LR, AUROC: 0.65 ± 0.01). A combined model did not improve performance (AUROC: 0,74 ± 0,01). We could demonstrate that radiomics and semantic analyses of planning CTs allowed for limited prediction of therapy response to palliative RT. ML predictions based on established clinical parameters achieved the best results.
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Affiliation(s)
- Óscar Llorián-Salvador
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany
- Department for Bioinformatics and Computational Biology, Informatik 12, Technical University of Munich (TUM), Boltzmannstraße 3, 85748, Garching, Germany
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Hanns-Dieter-Hüsch-Weg 15, 55128, Mainz, Germany
| | - Joachim Akhgar
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany
| | - Steffi Pigorsch
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany
| | - Kai Borm
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany
| | - Stefan Münch
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany
- Department of Radiation Sciences (DRS), Institute of Radiation Medicine (IRM), Helmholtz Zentrum, 85764, München, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, 69120, Heidelberg, Germany
| | - Burkhard Rost
- Department for Bioinformatics and Computational Biology, Informatik 12, Technical University of Munich (TUM), Boltzmannstraße 3, 85748, Garching, Germany
| | - Miguel A Andrade-Navarro
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Hanns-Dieter-Hüsch-Weg 15, 55128, Mainz, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany
- Department of Radiation Sciences (DRS), Institute of Radiation Medicine (IRM), Helmholtz Zentrum, 85764, München, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, 69120, Heidelberg, Germany
| | - Jan C Peeken
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany.
- Department of Radiation Sciences (DRS), Institute of Radiation Medicine (IRM), Helmholtz Zentrum, 85764, München, Germany.
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, 69120, Heidelberg, Germany.
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5
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Koludarov I, Senoner T, Jackson TNW, Dashevsky D, Heinzinger M, Aird SD, Rost B. Domain loss enabled evolution of novel functions in the snake three-finger toxin gene superfamily. Nat Commun 2023; 14:4861. [PMID: 37567881 PMCID: PMC10421932 DOI: 10.1038/s41467-023-40550-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 07/28/2023] [Indexed: 08/13/2023] Open
Abstract
Three-finger toxins (3FTXs) are a functionally diverse family of toxins, apparently unique to venoms of caenophidian snakes. Although the ancestral function of 3FTXs is antagonism of nicotinic acetylcholine receptors, redundancy conferred by the accumulation of duplicate genes has facilitated extensive neofunctionalization, such that derived members of the family interact with a range of targets. 3FTXs are members of the LY6/UPAR family, but their non-toxin ancestor remains unknown. Combining traditional phylogenetic approaches, manual synteny analysis, and machine learning techniques (including AlphaFold2 and ProtT5), we have reconstructed a detailed evolutionary history of 3FTXs. We identify their immediate ancestor as a non-secretory LY6, unique to squamate reptiles, and propose that changes in molecular ecology resulting from loss of a membrane-anchoring domain and changes in gene expression, paved the way for the evolution of one of the most important families of snake toxins.
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Affiliation(s)
- Ivan Koludarov
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology-i12, Boltzmannstr. 3, 85748, Garching/Munich, Germany.
| | - Tobias Senoner
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology-i12, Boltzmannstr. 3, 85748, Garching/Munich, Germany
| | - Timothy N W Jackson
- Australian Venom Research Unit, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, VIC, Australia
| | - Daniel Dashevsky
- Australian National Insect Collection, Commonwealth Scientific & Industrial Research Organisation, Canberra, ACT, Australia
| | - Michael Heinzinger
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology-i12, Boltzmannstr. 3, 85748, Garching/Munich, Germany
| | - Steven D Aird
- 7744-23 Hotaka Ariake, 399-8301, Azumino-shi, Nagano-ken, Japan
| | - Burkhard Rost
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology-i12, Boltzmannstr. 3, 85748, Garching/Munich, Germany
- Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748, Garching/Munich, Germany
- TUM School of Life Sciences Weihenstephan (WZW), Alte Akademie 8, Freising, Germany
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6
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Foreman SC, Llorián-Salvador O, David DE, Rösner VKN, Rischewski JF, Feuerriegel GC, Kramp DW, Luiken I, Lohse AK, Kiefer J, Mogler C, Knebel C, Jung M, Andrade-Navarro MA, Rost B, Combs SE, Makowski MR, Woertler K, Peeken JC, Gersing AS. Development and Evaluation of MR-Based Radiogenomic Models to Differentiate Atypical Lipomatous Tumors from Lipomas. Cancers (Basel) 2023; 15:cancers15072150. [PMID: 37046811 PMCID: PMC10093205 DOI: 10.3390/cancers15072150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/10/2023] [Accepted: 03/27/2023] [Indexed: 04/08/2023] Open
Abstract
Background: The aim of this study was to develop and validate radiogenomic models to predict the MDM2 gene amplification status and differentiate between ALTs and lipomas on preoperative MR images. Methods: MR images were obtained in 257 patients diagnosed with ALTs (n = 65) or lipomas (n = 192) using histology and the MDM2 gene analysis as a reference standard. The protocols included T2-, T1-, and fat-suppressed contrast-enhanced T1-weighted sequences. Additionally, 50 patients were obtained from a different hospital for external testing. Radiomic features were selected using mRMR. Using repeated nested cross-validation, the machine-learning models were trained on radiomic features and demographic information. For comparison, the external test set was evaluated by three radiology residents and one attending radiologist. Results: A LASSO classifier trained on radiomic features from all sequences performed best, with an AUC of 0.88, 70% sensitivity, 81% specificity, and 76% accuracy. In comparison, the radiology residents achieved 60–70% accuracy, 55–80% sensitivity, and 63–77% specificity, while the attending radiologist achieved 90% accuracy, 96% sensitivity, and 87% specificity. Conclusion: A radiogenomic model combining features from multiple MR sequences showed the best performance in predicting the MDM2 gene amplification status. The model showed a higher accuracy compared to the radiology residents, though lower compared to the attending radiologist.
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Affiliation(s)
- Sarah C. Foreman
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
| | - Oscar Llorián-Salvador
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
- Department of Informatics, Bioinformatics and Computational Biology—i12, Technische Universität München, Boltzmannstr. 3, 85748 Munich, Germany
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Hanns-Dieter-Hüsch-Weg 15, 55128 Mainz, Germany
| | - Diana E. David
- Department of Informatics, Bioinformatics and Computational Biology—i12, Technische Universität München, Boltzmannstr. 3, 85748 Munich, Germany
| | - Verena K. N. Rösner
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
| | - Jon F. Rischewski
- Department of Diagnostic and Interventional Neuroradiology, University Hospital Munich (LMU), Marchioninistrasse 15, 81377 Munich, Germany
| | - Georg C. Feuerriegel
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
| | - Daniel W. Kramp
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
| | - Ina Luiken
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
| | - Ann-Kathrin Lohse
- Department of Radiology, University Hospital Munich (LMU), Marchioninistrasse 15, 81377 Munich, Germany
| | - Jurij Kiefer
- Department of Plastic Surgery, University Hospital Freiburg, University of Freiburg, Hugstetterstraße 55, 79106 Freiburg im Breisgau, Germany
| | - Carolin Mogler
- Institute of Pathology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
| | - Carolin Knebel
- Department of Orthopedics and Sport Orthopedics, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
| | - Matthias Jung
- Department of Radiology, University Hospital Freiburg, University of Freiburg, Hugstetterstraße 55, 79106 Freiburg im Breisgau, Germany
| | - Miguel A. Andrade-Navarro
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Hanns-Dieter-Hüsch-Weg 15, 55128 Mainz, Germany
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology—i12, Technische Universität München, Boltzmannstr. 3, 85748 Munich, Germany
| | - Stephanie E. Combs
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
| | - Marcus R. Makowski
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
| | - Klaus Woertler
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
| | - Jan C. Peeken
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
- Helmholtz Zentrum München, Deutsches Forschungszentrum für Umwelt und Gesundheit, Institute of Radiation Medicine Neuherberg, 85764 Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, 69120 Heidelberg, Germany
| | - Alexandra S. Gersing
- Department of Diagnostic and Interventional Neuroradiology, University Hospital Munich (LMU), Marchioninistrasse 15, 81377 Munich, Germany
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7
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Bordin N, Dallago C, Heinzinger M, Kim S, Littmann M, Rauer C, Steinegger M, Rost B, Orengo C. Novel machine learning approaches revolutionize protein knowledge. Trends Biochem Sci 2023; 48:345-359. [PMID: 36504138 PMCID: PMC10570143 DOI: 10.1016/j.tibs.2022.11.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 10/24/2022] [Accepted: 11/17/2022] [Indexed: 12/10/2022]
Abstract
Breakthrough methods in machine learning (ML), protein structure prediction, and novel ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models of proteins and annotating their functions on a large scale is no longer limited by time and resources. The most recent method to be top ranked by the Critical Assessment of Structure Prediction (CASP) assessment, AlphaFold 2 (AF2), is capable of building structural models with an accuracy comparable to that of experimental structures. Annotations of 3D models are keeping pace with the deposition of the structures due to advancements in protein language models (pLMs) and structural aligners that help validate these transferred annotations. In this review we describe how recent developments in ML for protein science are making large-scale structural bioinformatics available to the general scientific community.
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Affiliation(s)
- Nicola Bordin
- Institute of Structural and Molecular Biology, University College London, Gower St, WC1E 6BT London, UK
| | - Christian Dallago
- Technical University of Munich (TUM) Department of Informatics, Bioinformatics and Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany; VantAI, 151 W 42nd Street, New York, NY 10036, USA
| | - Michael Heinzinger
- Technical University of Munich (TUM) Department of Informatics, Bioinformatics and Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany; TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748 Garching, Germany
| | - Stephanie Kim
- School of Biological Sciences, Seoul National University, Seoul, South Korea; Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
| | - Maria Littmann
- Technical University of Munich (TUM) Department of Informatics, Bioinformatics and Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
| | - Clemens Rauer
- Institute of Structural and Molecular Biology, University College London, Gower St, WC1E 6BT London, UK
| | - Martin Steinegger
- School of Biological Sciences, Seoul National University, Seoul, South Korea; Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
| | - Burkhard Rost
- Technical University of Munich (TUM) Department of Informatics, Bioinformatics and Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany; Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748 Garching/Munich, Germany; TUM School of Life Sciences Weihenstephan (TUM-WZW), Alte Akademie 8, Freising, Germany
| | - Christine Orengo
- Institute of Structural and Molecular Biology, University College London, Gower St, WC1E 6BT London, UK.
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8
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Zatorski N, Sun Y, Elmas A, Dallago C, Karl T, Stein D, Rost B, Huang KL, Walsh M, Schlessinger A. Structural Analysis of Genomic and Proteomic Signatures Reveal Dynamic Expression of Intrinsically Disordered Regions in Breast Cancer and Tissue. bioRxiv 2023:2023.02.23.529755. [PMID: 36865220 PMCID: PMC9980136 DOI: 10.1101/2023.02.23.529755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
Abstract
Structural features of proteins capture underlying information about protein evolution and function, which enhances the analysis of proteomic and transcriptomic data. Here we develop Structural Analysis of Gene and protein Expression Signatures (SAGES), a method that describes expression data using features calculated from sequence-based prediction methods and 3D structural models. We used SAGES, along with machine learning, to characterize tissues from healthy individuals and those with breast cancer. We analyzed gene expression data from 23 breast cancer patients and genetic mutation data from the COSMIC database as well as 17 breast tumor protein expression profiles. We identified prominent expression of intrinsically disordered regions in breast cancer proteins as well as relationships between drug perturbation signatures and breast cancer disease signatures. Our results suggest that SAGES is generally applicable to describe diverse biological phenomena including disease states and drug effects.
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Affiliation(s)
- Nicole Zatorski
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave Levey Pl NY, NY 10029, USA
| | - Yifei Sun
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave Levey Pl NY, NY 10029, USA
| | - Abdulkadir Elmas
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave Levey Pl NY, NY 10029, USA
| | - Christian Dallago
- NVIDIA DE GmbH, Einsteinstraße 172, 81677 München, Germany
- Faculty of Informatics, Bioinformatics & Computational Biology, Technical University Munich (TUM), 85748 Garching, Germany
| | - Timothy Karl
- Faculty of Informatics, Bioinformatics & Computational Biology, Technical University Munich (TUM), 85748 Garching, Germany
| | - David Stein
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave Levey Pl NY, NY 10029, USA
| | - Burkhard Rost
- Faculty of Informatics, Bioinformatics & Computational Biology, Technical University Munich (TUM), 85748 Garching, Germany
| | - Kuan-Lin Huang
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave Levey Pl NY, NY 10029, USA
| | - Martin Walsh
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave Levey Pl NY, NY 10029, USA
| | - Avner Schlessinger
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave Levey Pl NY, NY 10029, USA
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9
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Olenyi T, Marquet C, Heinzinger M, Kröger B, Nikolova T, Bernhofer M, Sändig P, Schütze K, Littmann M, Mirdita M, Steinegger M, Dallago C, Rost B. LambdaPP: Fast and accessible protein-specific phenotype predictions. Protein Sci 2023; 32:e4524. [PMID: 36454227 PMCID: PMC9793974 DOI: 10.1002/pro.4524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 11/09/2022] [Accepted: 11/21/2022] [Indexed: 12/04/2022]
Abstract
The availability of accurate and fast artificial intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, the first internet server making AI protein predictions available in 1992. Given a protein sequence as input, LambdaPP provides easily accessible visualizations of protein 3D structure, along with predictions at the protein level (GeneOntology, subcellular location), and the residue level (binding to metal ions, small molecules, and nucleotides; conservation; intrinsic disorder; secondary structure; alpha-helical and beta-barrel transmembrane segments; signal-peptides; variant effect) in seconds. The structure prediction provided by LambdaPP-leveraging ColabFold and computed in minutes-is based on MMseqs2 multiple sequence alignments. All other feature prediction methods are based on the pLM ProtT5. Queried by a protein sequence, LambdaPP computes protein and residue predictions almost instantly for various phenotypes, including 3D structure and aspects of protein function. LambdaPP is freely available for everyone to use under embed.predictprotein.org, the interactive results for the case study can be found under https://embed.predictprotein.org/o/Q9NZC2. The frontend of LambdaPP can be found on GitHub (github.com/sacdallago/embed.predictprotein.org), and can be freely used and distributed under the academic free use license (AFL-2). For high-throughput applications, all methods can be executed locally via the bio-embeddings (bioembeddings.com) python package, or docker image at ghcr.io/bioembeddings/bio_embeddings, which also includes the backend of LambdaPP.
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Affiliation(s)
- Tobias Olenyi
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany,TUM Graduate SchoolCenter of Doctoral Studies in Informatics and its Applications (CeDoSIA)GarchingGermany
| | - Céline Marquet
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany,TUM Graduate SchoolCenter of Doctoral Studies in Informatics and its Applications (CeDoSIA)GarchingGermany
| | - Michael Heinzinger
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany,TUM Graduate SchoolCenter of Doctoral Studies in Informatics and its Applications (CeDoSIA)GarchingGermany
| | - Benjamin Kröger
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany
| | - Tiha Nikolova
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany
| | - Michael Bernhofer
- TUM Graduate SchoolCenter of Doctoral Studies in Informatics and its Applications (CeDoSIA)GarchingGermany
| | - Philip Sändig
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany
| | - Konstantin Schütze
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany
| | - Maria Littmann
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany
| | - Milot Mirdita
- School of Biological SciencesSeoul National UniversitySeoulSouth Korea
| | - Martin Steinegger
- School of Biological SciencesSeoul National UniversitySeoulSouth Korea,Korea Artificial Intelligence InstituteSeoul National UniversitySeoulSouth Korea,Korea Institute of Molecular Biology and GeneticsSeoul National UniversitySeoulSouth Korea
| | - Christian Dallago
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany,VantAINew YorkUSA
| | - Burkhard Rost
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany,Institute for Advanced Study (TUM‐IAS)Lichtenbergstr. 2a, 85748 Garching/Munich, Germany & TUM School of Life Sciences Weihenstephan (WZW)FreisingGermany
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10
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Nallapareddy V, Bordin N, Sillitoe I, Heinzinger M, Littmann M, Waman VP, Sen N, Rost B, Orengo C. CATHe: detection of remote homologues for CATH superfamilies using embeddings from protein language models. Bioinformatics 2023; 39:6989624. [PMID: 36648327 PMCID: PMC9887088 DOI: 10.1093/bioinformatics/btad029] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 12/07/2022] [Accepted: 01/16/2023] [Indexed: 01/18/2023] Open
Abstract
MOTIVATION CATH is a protein domain classification resource that exploits an automated workflow of structure and sequence comparison alongside expert manual curation to construct a hierarchical classification of evolutionary and structural relationships. The aim of this study was to develop algorithms for detecting remote homologues missed by state-of-the-art hidden Markov model (HMM)-based approaches. The method developed (CATHe) combines a neural network with sequence representations obtained from protein language models. It was assessed using a dataset of remote homologues having less than 20% sequence identity to any domain in the training set. RESULTS The CATHe models trained on 1773 largest and 50 largest CATH superfamilies had an accuracy of 85.6 ± 0.4% and 98.2 ± 0.3%, respectively. As a further test of the power of CATHe to detect more remote homologues missed by HMMs derived from CATH domains, we used a dataset consisting of protein domains that had annotations in Pfam, but not in CATH. By using highly reliable CATHe predictions (expected error rate <0.5%), we were able to provide CATH annotations for 4.62 million Pfam domains. For a subset of these domains from Homo sapiens, we structurally validated 90.86% of the predictions by comparing their corresponding AlphaFold2 structures with structures from the CATH superfamilies to which they were assigned. AVAILABILITY AND IMPLEMENTATION The code for the developed models is available on https://github.com/vam-sin/CATHe, and the datasets developed in this study can be accessed on https://zenodo.org/record/6327572. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Vamsi Nallapareddy
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
| | - Nicola Bordin
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
| | - Ian Sillitoe
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
| | - Michael Heinzinger
- Department of Informatics, Bioinformatics and Computational Biology—i12, Technical University of Munich (TUM), Garching/Munich 85748, Germany
| | - Maria Littmann
- Department of Informatics, Bioinformatics and Computational Biology—i12, Technical University of Munich (TUM), Garching/Munich 85748, Germany
| | - Vaishali P Waman
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
| | - Neeladri Sen
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology—i12, Technical University of Munich (TUM), Garching/Munich 85748, Germany
- Institute for Advanced Study (TUM-IAS), Garching/Munich 85748, Germany
- TUM School of Life Sciences Weihenstephan (WZW) 85354, Germany
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11
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Schütze K, Heinzinger M, Steinegger M, Rost B. Nearest neighbor search on embeddings rapidly identifies distant protein relations. Front Bioinform 2022; 2:1033775. [PMID: 36466147 PMCID: PMC9714024 DOI: 10.3389/fbinf.2022.1033775] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 10/31/2022] [Indexed: 11/29/2023] Open
Abstract
Since 1992, all state-of-the-art methods for fast and sensitive identification of evolutionary, structural, and functional relations between proteins (also referred to as "homology detection") use sequences and sequence-profiles (PSSMs). Protein Language Models (pLMs) generalize sequences, possibly capturing the same constraints as PSSMs, e.g., through embeddings. Here, we explored how to use such embeddings for nearest neighbor searches to identify relations between protein pairs with diverged sequences (remote homology detection for levels of <20% pairwise sequence identity, PIDE). While this approach excelled for proteins with single domains, we demonstrated the current challenges applying this to multi-domain proteins and presented some ideas how to overcome existing limitations, in principle. We observed that sufficiently challenging data set separations were crucial to provide deeply relevant insights into the behavior of nearest neighbor search when applied to the protein embedding space, and made all our methods readily available for others.
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Affiliation(s)
- Konstantin Schütze
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology—i12, Munich, Germany
| | - Michael Heinzinger
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology—i12, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Garching, Germany
| | - Martin Steinegger
- School of Biological Sciences, Seoul National University, Seoul, South Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
| | - Burkhard Rost
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology—i12, Munich, Germany
- Institute for Advanced Study (TUM-IAS), Germany & TUM School of Life Sciences Weihenstephan (WZW), Freising, Germany
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12
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Abstract
Predictions for millions of protein three-dimensional structures are only a few clicks away since the release of AlphaFold2 results for UniProt. However, many proteins have so-called intrinsically disordered regions (IDRs) that do not adopt unique structures in isolation. These IDRs are associated with several diseases, including Alzheimer’s Disease. We showed that three recent disorder measures of AlphaFold2 predictions (pLDDT, “experimentally resolved” prediction and “relative solvent accessibility”) correlated to some extent with IDRs. However, expert methods predict IDRs more reliably by combining complex machine learning models with expert-crafted input features and evolutionary information from multiple sequence alignments (MSAs). MSAs are not always available, especially for IDRs, and are computationally expensive to generate, limiting the scalability of the associated tools. Here, we present the novel method SETH that predicts residue disorder from embeddings generated by the protein Language Model ProtT5, which explicitly only uses single sequences as input. Thereby, our method, relying on a relatively shallow convolutional neural network, outperformed much more complex solutions while being much faster, allowing to create predictions for the human proteome in about 1 hour on a consumer-grade PC with one NVIDIA GeForce RTX 3060. Trained on a continuous disorder scale (CheZOD scores), our method captured subtle variations in disorder, thereby providing important information beyond the binary classification of most methods. High performance paired with speed revealed that SETH’s nuanced disorder predictions for entire proteomes capture aspects of the evolution of organisms. Additionally, SETH could also be used to filter out regions or proteins with probable low-quality AlphaFold2 3D structures to prioritize running the compute-intensive predictions for large data sets. SETH is freely publicly available at: https://github.com/Rostlab/SETH.
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Affiliation(s)
- Dagmar Ilzhöfer
- Faculty of Informatics, TUM (Technical University of Munich), Munich, Germany
| | - Michael Heinzinger
- Faculty of Informatics, TUM (Technical University of Munich), Munich, Germany,Center of Doctoral Studies in Informatics and Its Applications (CeDoSIA), TUM Graduate School, Garching, Germany,*Correspondence: Michael Heinzinger,
| | - Burkhard Rost
- Faculty of Informatics, TUM (Technical University of Munich), Munich, Germany,Institute for Advanced Study (TUM-IAS), TUM (Technical University of Munich), Garching, Germany,TUM School of Life Sciences Weihenstephan (WZW), TUM (Technical University of Munich), Freising, Germany
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13
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Elnaggar A, Heinzinger M, Dallago C, Rehawi G, Wang Y, Jones L, Gibbs T, Feher T, Angerer C, Steinegger M, Bhowmik D, Rost B. ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning. IEEE Trans Pattern Anal Mach Intell 2022; 44:7112-7127. [PMID: 34232869 DOI: 10.1109/tpami.2021.3095381] [Citation(s) in RCA: 270] [Impact Index Per Article: 135.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids. The protein LMs (pLMs) were trained on the Summit supercomputer using 5616 GPUs and TPU Pod up-to 1024 cores. Dimensionality reduction revealed that the raw pLM-embeddings from unlabeled data captured some biophysical features of protein sequences. We validated the advantage of using the embeddings as exclusive input for several subsequent tasks: (1) a per-residue (per-token) prediction of protein secondary structure (3-state accuracy Q3=81%-87%); (2) per-protein (pooling) predictions of protein sub-cellular location (ten-state accuracy: Q10=81%) and membrane versus water-soluble (2-state accuracy Q2=91%). For secondary structure, the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without multiple sequence alignments (MSAs) or evolutionary information thereby bypassing expensive database searches. Taken together, the results implied that pLMs learned some of the grammar of the language of life. All our models are available through https://github.com/agemagician/ProtTrans.
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14
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Elnaggar A, Heinzinger M, Dallago C, Rehawi G, Wang Y, Jones L, Gibbs T, Feher T, Angerer C, Steinegger M, Bhowmik D, Rost B. ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning. IEEE Trans Pattern Anal Mach Intell 2022. [PMID: 34232869 DOI: 10.1101/2020.07.12.199554] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids. The protein LMs (pLMs) were trained on the Summit supercomputer using 5616 GPUs and TPU Pod up-to 1024 cores. Dimensionality reduction revealed that the raw pLM-embeddings from unlabeled data captured some biophysical features of protein sequences. We validated the advantage of using the embeddings as exclusive input for several subsequent tasks: (1) a per-residue (per-token) prediction of protein secondary structure (3-state accuracy Q3=81%-87%); (2) per-protein (pooling) predictions of protein sub-cellular location (ten-state accuracy: Q10=81%) and membrane versus water-soluble (2-state accuracy Q2=91%). For secondary structure, the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without multiple sequence alignments (MSAs) or evolutionary information thereby bypassing expensive database searches. Taken together, the results implied that pLMs learned some of the grammar of the language of life. All our models are available through https://github.com/agemagician/ProtTrans.
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15
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Marquet C, Heinzinger M, Olenyi T, Dallago C, Erckert K, Bernhofer M, Nechaev D, Rost B. Embeddings from protein language models predict conservation and variant effects. Hum Genet 2022; 141:1629-1647. [PMID: 34967936 PMCID: PMC8716573 DOI: 10.1007/s00439-021-02411-y] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 12/06/2021] [Indexed: 12/13/2022]
Abstract
The emergence of SARS-CoV-2 variants stressed the demand for tools allowing to interpret the effect of single amino acid variants (SAVs) on protein function. While Deep Mutational Scanning (DMS) sets continue to expand our understanding of the mutational landscape of single proteins, the results continue to challenge analyses. Protein Language Models (pLMs) use the latest deep learning (DL) algorithms to leverage growing databases of protein sequences. These methods learn to predict missing or masked amino acids from the context of entire sequence regions. Here, we used pLM representations (embeddings) to predict sequence conservation and SAV effects without multiple sequence alignments (MSAs). Embeddings alone predicted residue conservation almost as accurately from single sequences as ConSeq using MSAs (two-state Matthews Correlation Coefficient-MCC-for ProtT5 embeddings of 0.596 ± 0.006 vs. 0.608 ± 0.006 for ConSeq). Inputting the conservation prediction along with BLOSUM62 substitution scores and pLM mask reconstruction probabilities into a simplistic logistic regression (LR) ensemble for Variant Effect Score Prediction without Alignments (VESPA) predicted SAV effect magnitude without any optimization on DMS data. Comparing predictions for a standard set of 39 DMS experiments to other methods (incl. ESM-1v, DeepSequence, and GEMME) revealed our approach as competitive with the state-of-the-art (SOTA) methods using MSA input. No method outperformed all others, neither consistently nor statistically significantly, independently of the performance measure applied (Spearman and Pearson correlation). Finally, we investigated binary effect predictions on DMS experiments for four human proteins. Overall, embedding-based methods have become competitive with methods relying on MSAs for SAV effect prediction at a fraction of the costs in computing/energy. Our method predicted SAV effects for the entire human proteome (~ 20 k proteins) within 40 min on one Nvidia Quadro RTX 8000. All methods and data sets are freely available for local and online execution through bioembeddings.com, https://github.com/Rostlab/VESPA , and PredictProtein.
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Affiliation(s)
- Céline Marquet
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany.
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany.
| | - Michael Heinzinger
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Tobias Olenyi
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Christian Dallago
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Kyra Erckert
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Michael Bernhofer
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Dmitrii Nechaev
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, Garching, 85748, Munich, Germany
- TUM School of Life Sciences Weihenstephan (TUM-WZW), Alte Akademie 8, Freising, Germany
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16
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Foley G, Mora A, Ross CM, Bottoms S, Sützl L, Lamprecht ML, Zaugg J, Essebier A, Balderson B, Newell R, Thomson RES, Kobe B, Barnard RT, Guddat L, Schenk G, Carsten J, Gumulya Y, Rost B, Haltrich D, Sieber V, Gillam EMJ, Bodén M. Engineering indel and substitution variants of diverse and ancient enzymes using Graphical Representation of Ancestral Sequence Predictions (GRASP). PLoS Comput Biol 2022; 18:e1010633. [PMID: 36279274 PMCID: PMC9632902 DOI: 10.1371/journal.pcbi.1010633] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 11/03/2022] [Accepted: 10/04/2022] [Indexed: 11/06/2022] Open
Abstract
Ancestral sequence reconstruction is a technique that is gaining widespread use in molecular evolution studies and protein engineering. Accurate reconstruction requires the ability to handle appropriately large numbers of sequences, as well as insertion and deletion (indel) events, but available approaches exhibit limitations. To address these limitations, we developed Graphical Representation of Ancestral Sequence Predictions (GRASP), which efficiently implements maximum likelihood methods to enable the inference of ancestors of families with more than 10,000 members. GRASP implements partial order graphs (POGs) to represent and infer insertion and deletion events across ancestors, enabling the identification of building blocks for protein engineering. To validate the capacity to engineer novel proteins from realistic data, we predicted ancestor sequences across three distinct enzyme families: glucose-methanol-choline (GMC) oxidoreductases, cytochromes P450, and dihydroxy/sugar acid dehydratases (DHAD). All tested ancestors demonstrated enzymatic activity. Our study demonstrates the ability of GRASP (1) to support large data sets over 10,000 sequences and (2) to employ insertions and deletions to identify building blocks for engineering biologically active ancestors, by exploring variation over evolutionary time.
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Affiliation(s)
- Gabriel Foley
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Ariane Mora
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Connie M. Ross
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Scott Bottoms
- Campus Straubing for Biotechnology and Sustainability, Technische Universität München, Straubing, Germany
| | - Leander Sützl
- Institut für Lebensmitteltechnologie, Universität für Bodenkultur Wien, Vienna, Austria
| | - Marnie L. Lamprecht
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Julian Zaugg
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Alexandra Essebier
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Brad Balderson
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Rhys Newell
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Raine E. S. Thomson
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Bostjan Kobe
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Institute for Molecular Bioscience and Australian Infectious Diseases Research Centre, The University of Queensland, Brisbane, Australia
| | - Ross T. Barnard
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Luke Guddat
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Gerhard Schenk
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Sustainable Minerals Institute, The University of Queensland, Brisbane, Australia
| | - Jörg Carsten
- Zentralinstitut für Katalyseforschung, Technische Universität München, Munich, Germany
| | - Yosephine Gumulya
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Burkhard Rost
- Fakultät für Informatik, Technische Universität München, Munich, Germany
| | - Dietmar Haltrich
- Institut für Lebensmitteltechnologie, Universität für Bodenkultur Wien, Vienna, Austria
| | - Volker Sieber
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Campus Straubing for Biotechnology and Sustainability, Technische Universität München, Straubing, Germany
- Zentralinstitut für Katalyseforschung, Technische Universität München, Munich, Germany
| | - Elizabeth M. J. Gillam
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- * E-mail: (MB); (EMJG)
| | - Mikael Bodén
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- * E-mail: (MB); (EMJG)
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17
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Abstract
BACKGROUND Despite the immense importance of transmembrane proteins (TMP) for molecular biology and medicine, experimental 3D structures for TMPs remain about 4-5 times underrepresented compared to non-TMPs. Today's top methods such as AlphaFold2 accurately predict 3D structures for many TMPs, but annotating transmembrane regions remains a limiting step for proteome-wide predictions. RESULTS Here, we present TMbed, a novel method inputting embeddings from protein Language Models (pLMs, here ProtT5), to predict for each residue one of four classes: transmembrane helix (TMH), transmembrane strand (TMB), signal peptide, or other. TMbed completes predictions for entire proteomes within hours on a single consumer-grade desktop machine at performance levels similar or better than methods, which are using evolutionary information from multiple sequence alignments (MSAs) of protein families. On the per-protein level, TMbed correctly identified 94 ± 8% of the beta barrel TMPs (53 of 57) and 98 ± 1% of the alpha helical TMPs (557 of 571) in a non-redundant data set, at false positive rates well below 1% (erred on 30 of 5654 non-membrane proteins). On the per-segment level, TMbed correctly placed, on average, 9 of 10 transmembrane segments within five residues of the experimental observation. Our method can handle sequences of up to 4200 residues on standard graphics cards used in desktop PCs (e.g., NVIDIA GeForce RTX 3060). CONCLUSIONS Based on embeddings from pLMs and two novel filters (Gaussian and Viterbi), TMbed predicts alpha helical and beta barrel TMPs at least as accurately as any other method but at lower false positive rates. Given the few false positives and its outstanding speed, TMbed might be ideal to sieve through millions of 3D structures soon to be predicted, e.g., by AlphaFold2.
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Affiliation(s)
- Michael Bernhofer
- Department of Informatics, Bioinformatics and Computational Biology ‑ i12, Technical University of Munich (TUM), Boltzmannstr. 3, 85748, Garching, Germany. .,TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany.
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology ‑ i12, Technical University of Munich (TUM), Boltzmannstr. 3, 85748, Garching, Germany.,Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748, Garching, Germany.,TUM School of Life Sciences Weihenstephan (TUM-WZW), Alte Akademie 8, Freising, Germany
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18
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Heinzinger M, Littmann M, Sillitoe I, Bordin N, Orengo C, Rost B. Contrastive learning on protein embeddings enlightens midnight zone. NAR Genom Bioinform 2022; 4:lqac043. [PMID: 35702380 PMCID: PMC9188115 DOI: 10.1093/nargab/lqac043] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 03/25/2022] [Accepted: 05/17/2022] [Indexed: 12/23/2022] Open
Abstract
Experimental structures are leveraged through multiple sequence alignments, or more generally through homology-based inference (HBI), facilitating the transfer of information from a protein with known annotation to a query without any annotation. A recent alternative expands the concept of HBI from sequence-distance lookup to embedding-based annotation transfer (EAT). These embeddings are derived from protein Language Models (pLMs). Here, we introduce using single protein representations from pLMs for contrastive learning. This learning procedure creates a new set of embeddings that optimizes constraints captured by hierarchical classifications of protein 3D structures defined by the CATH resource. The approach, dubbed ProtTucker, has an improved ability to recognize distant homologous relationships than more traditional techniques such as threading or fold recognition. Thus, these embeddings have allowed sequence comparison to step into the 'midnight zone' of protein similarity, i.e. the region in which distantly related sequences have a seemingly random pairwise sequence similarity. The novelty of this work is in the particular combination of tools and sampling techniques that ascertained good performance comparable or better to existing state-of-the-art sequence comparison methods. Additionally, since this method does not need to generate alignments it is also orders of magnitudes faster. The code is available at https://github.com/Rostlab/EAT.
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Affiliation(s)
- Michael Heinzinger
- TUM (Technical University of Munich) Dept Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
| | - Maria Littmann
- TUM (Technical University of Munich) Dept Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
| | - Ian Sillitoe
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
| | - Nicola Bordin
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
| | - Christine Orengo
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
| | - Burkhard Rost
- TUM (Technical University of Munich) Dept Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
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19
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Weissenow K, Heinzinger M, Rost B. Protein language-model embeddings for fast, accurate, and alignment-free protein structure prediction. Structure 2022; 30:1169-1177.e4. [DOI: 10.1016/j.str.2022.05.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 02/25/2022] [Accepted: 04/29/2022] [Indexed: 01/27/2023]
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20
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Lautenbacher L, Samaras P, Muller J, Grafberger A, Shraideh M, Rank J, Fuchs ST, Schmidt TK, The M, Dallago C, Wittges H, Rost B, Krcmar H, Kuster B, Wilhelm M. ProteomicsDB: toward a FAIR open-source resource for life-science research. Nucleic Acids Res 2022; 50:D1541-D1552. [PMID: 34791421 PMCID: PMC8728203 DOI: 10.1093/nar/gkab1026] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 10/12/2021] [Accepted: 10/15/2021] [Indexed: 12/28/2022] Open
Abstract
ProteomicsDB (https://www.ProteomicsDB.org) is a multi-omics and multi-organism resource for life science research. In this update, we present our efforts to continuously develop and expand ProteomicsDB. The major focus over the last two years was improving the findability, accessibility, interoperability and reusability (FAIR) of the data as well as its implementation. For this purpose, we release a new application programming interface (API) that provides systematic access to essentially all data in ProteomicsDB. Second, we release a new open-source user interface (UI) and show the advantages the scientific community gains from such software. With the new interface, two new visualizations of protein primary, secondary and tertiary structure as well an updated spectrum viewer were added. Furthermore, we integrated ProteomicsDB with our deep-neural-network Prosit that can predict the fragmentation characteristics and retention time of peptides. The result is an automatic processing pipeline that can be used to reevaluate database search engine results stored in ProteomicsDB. In addition, we extended the data content with experiments investigating different human biology as well as a newly supported organism.
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Affiliation(s)
- Ludwig Lautenbacher
- Technical University of Munich, Computational Mass Spectrometry, 85354 Freising, Bavaria, Germany
| | - Patroklos Samaras
- Technical University of Munich, Chair of Proteomics and Bioanalytics, 85354 Freising, Bavaria, Germany
| | - Julian Muller
- Technical University of Munich, Chair of Proteomics and Bioanalytics, 85354 Freising, Bavaria, Germany
| | - Andreas Grafberger
- Technical University of Munich, Chair of Proteomics and Bioanalytics, 85354 Freising, Bavaria, Germany
| | - Marwin Shraideh
- Technical University of Munich, Chair for Information Systems, 85748 Garching, Bavaria, Germany
- Technical University of Munich, SAP University Competence Center, 85748 Garching, Bavaria, Germany
| | - Johannes Rank
- Technical University of Munich, Chair for Information Systems, 85748 Garching, Bavaria, Germany
- Technical University of Munich, SAP University Competence Center, 85748 Garching, Bavaria, Germany
| | - Simon T Fuchs
- Technical University of Munich, Chair for Information Systems, 85748 Garching, Bavaria, Germany
- Technical University of Munich, SAP University Competence Center, 85748 Garching, Bavaria, Germany
| | - Tobias K Schmidt
- Technical University of Munich, Chair of Proteomics and Bioanalytics, 85354 Freising, Bavaria, Germany
| | - Matthew The
- Technical University of Munich, Chair of Proteomics and Bioanalytics, 85354 Freising, Bavaria, Germany
| | - Christian Dallago
- Technical University of Munich, Department for Bioinformatics and Computational Biology, 85748 Garching, Bavaria, Germany
- Technical University of Munich, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), 85748 Garching, Bavaria, Germany
| | - Holger Wittges
- Technical University of Munich, Chair for Information Systems, 85748 Garching, Bavaria, Germany
- Technical University of Munich, SAP University Competence Center, 85748 Garching, Bavaria, Germany
| | - Burkhard Rost
- Technical University of Munich, Department for Bioinformatics and Computational Biology, 85748 Garching, Bavaria, Germany
- Technical University of Munich, Institute for Advanced Study (TUM-IAS), 85748 Freising, Bavaria, Germany
| | - Helmut Krcmar
- Technical University of Munich, Chair for Information Systems, 85748 Garching, Bavaria, Germany
- Technical University of Munich, SAP University Competence Center, 85748 Garching, Bavaria, Germany
| | - Bernhard Kuster
- Technical University of Munich, Chair of Proteomics and Bioanalytics, 85354 Freising, Bavaria, Germany
- Technical University of Munich, Bavarian Biomolecular Mass Spectrometry Center (BayBioMS), 85354 Freising, Bavaria, Germany
| | - Mathias Wilhelm
- Technical University of Munich, Computational Mass Spectrometry, 85354 Freising, Bavaria, Germany
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21
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Littmann M, Heinzinger M, Dallago C, Weissenow K, Rost B. Protein embeddings and deep learning predict binding residues for various ligand classes. Sci Rep 2021; 11:23916. [PMID: 34903827 PMCID: PMC8668950 DOI: 10.1038/s41598-021-03431-4] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 12/02/2021] [Indexed: 01/27/2023] Open
Abstract
One important aspect of protein function is the binding of proteins to ligands, including small molecules, metal ions, and macromolecules such as DNA or RNA. Despite decades of experimental progress many binding sites remain obscure. Here, we proposed bindEmbed21, a method predicting whether a protein residue binds to metal ions, nucleic acids, or small molecules. The Artificial Intelligence (AI)-based method exclusively uses embeddings from the Transformer-based protein Language Model (pLM) ProtT5 as input. Using only single sequences without creating multiple sequence alignments (MSAs), bindEmbed21DL outperformed MSA-based predictions. Combination with homology-based inference increased performance to F1 = 48 ± 3% (95% CI) and MCC = 0.46 ± 0.04 when merging all three ligand classes into one. All results were confirmed by three independent data sets. Focusing on very reliably predicted residues could complement experimental evidence: For the 25% most strongly predicted binding residues, at least 73% were correctly predicted even when ignoring the problem of missing experimental annotations. The new method bindEmbed21 is fast, simple, and broadly applicable-neither using structure nor MSAs. Thereby, it found binding residues in over 42% of all human proteins not otherwise implied in binding and predicted about 6% of all residues as binding to metal ions, nucleic acids, or small molecules.
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Affiliation(s)
- Maria Littmann
- Department of Informatics, Bioinformatics and Computational Biology, I12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany.
| | - Michael Heinzinger
- Department of Informatics, Bioinformatics and Computational Biology, I12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and Its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Christian Dallago
- Department of Informatics, Bioinformatics and Computational Biology, I12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and Its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Konstantin Weissenow
- Department of Informatics, Bioinformatics and Computational Biology, I12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and Its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology, I12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany
- Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, Garching, 85748, Munich, Germany
- TUM School of Life Sciences Weihenstephan (TUM-WZW), Alte Akademie 8, Freising, Germany
- Department of Biochemistry and Molecular Biophysics, Columbia University, 701 West, 168th Street, New York, NY, 10032, USA
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22
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Stärk H, Dallago C, Heinzinger M, Rost B. Light attention predicts protein location from the language of life. Bioinform Adv 2021; 1:vbab035. [PMID: 36700108 PMCID: PMC9710637 DOI: 10.1093/bioadv/vbab035] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/27/2021] [Accepted: 11/15/2021] [Indexed: 01/28/2023]
Abstract
Summary Although knowing where a protein functions in a cell is important to characterize biological processes, this information remains unavailable for most known proteins. Machine learning narrows the gap through predictions from expert-designed input features leveraging information from multiple sequence alignments (MSAs) that is resource expensive to generate. Here, we showcased using embeddings from protein language models for competitive localization prediction without MSAs. Our lightweight deep neural network architecture used a softmax weighted aggregation mechanism with linear complexity in sequence length referred to as light attention. The method significantly outperformed the state-of-the-art (SOTA) for 10 localization classes by about 8 percentage points (Q10). So far, this might be the highest improvement of just embeddings over MSAs. Our new test set highlighted the limits of standard static datasets: while inviting new models, they might not suffice to claim improvements over the SOTA. Availability and implementation The novel models are available as a web-service at http://embed.protein.properties. Code needed to reproduce results is provided at https://github.com/HannesStark/protein-localization. Predictions for the human proteome are available at https://zenodo.org/record/5047020. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Hannes Stärk
- Department of Informatics, Bioinformatics & Computational Biology-i12, TUM (Technical University of Munich), 85748 Munich, Germany
| | - Christian Dallago
- Department of Informatics, Bioinformatics & Computational Biology-i12, TUM (Technical University of Munich), 85748 Munich, Germany.,TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), 85748 Munich, Germany
| | - Michael Heinzinger
- Department of Informatics, Bioinformatics & Computational Biology-i12, TUM (Technical University of Munich), 85748 Munich, Germany.,TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), 85748 Munich, Germany
| | - Burkhard Rost
- Department of Informatics, Bioinformatics & Computational Biology-i12, TUM (Technical University of Munich), 85748 Munich, Germany.,Institute for Advanced Study (TUM-IAS), 85748 Munich, Germany.,TUM School of Life Sciences Weihenstephan (WZW), Freising, Germany
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23
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Heinzinger M, Dallago C, Rost B. Protein matchmaking through representation learning. Cell Syst 2021; 12:948-950. [PMID: 34672956 DOI: 10.1016/j.cels.2021.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Sledzieski, Singh, Cowen, and Berger employ representation learning to predict protein interactions and associations, additionally identifying binding residues between protein pairs. Generalizability is showcased by training on one organism while evaluating on others. The work exemplifies how transfer of AI-learned representations can advance knowledge in molecular biology.
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Affiliation(s)
- Michael Heinzinger
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany; TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748 Garching, Germany
| | - Christian Dallago
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany; TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748 Garching, Germany.
| | - Burkhard Rost
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany; Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748 Garching/Munich, Germany; TUM School of Life Sciences Weihenstephan (TUM-WZW), Alte Akademie 8, Freising, Germany
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24
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O’Donoghue SI, Schafferhans A, Sikta N, Stolte C, Kaur S, Ho BK, Anderson S, Procter JB, Dallago C, Bordin N, Adcock M, Rost B. SARS-CoV-2 structural coverage map reveals viral protein assembly, mimicry, and hijacking mechanisms. Mol Syst Biol 2021; 17:e10079. [PMID: 34519429 PMCID: PMC8438690 DOI: 10.15252/msb.202010079] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 08/05/2021] [Accepted: 08/06/2021] [Indexed: 01/18/2023] Open
Abstract
We modeled 3D structures of all SARS-CoV-2 proteins, generating 2,060 models that span 69% of the viral proteome and provide details not available elsewhere. We found that ˜6% of the proteome mimicked human proteins, while ˜7% was implicated in hijacking mechanisms that reverse post-translational modifications, block host translation, and disable host defenses; a further ˜29% self-assembled into heteromeric states that provided insight into how the viral replication and translation complex forms. To make these 3D models more accessible, we devised a structural coverage map, a novel visualization method to show what is-and is not-known about the 3D structure of the viral proteome. We integrated the coverage map into an accompanying online resource (https://aquaria.ws/covid) that can be used to find and explore models corresponding to the 79 structural states identified in this work. The resulting Aquaria-COVID resource helps scientists use emerging structural data to understand the mechanisms underlying coronavirus infection and draws attention to the 31% of the viral proteome that remains structurally unknown or dark.
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MESH Headings
- Amino Acid Transport Systems, Neutral/chemistry
- Amino Acid Transport Systems, Neutral/genetics
- Amino Acid Transport Systems, Neutral/metabolism
- Angiotensin-Converting Enzyme 2/chemistry
- Angiotensin-Converting Enzyme 2/genetics
- Angiotensin-Converting Enzyme 2/metabolism
- Binding Sites
- COVID-19/genetics
- COVID-19/metabolism
- COVID-19/virology
- Computational Biology/methods
- Coronavirus Envelope Proteins/chemistry
- Coronavirus Envelope Proteins/genetics
- Coronavirus Envelope Proteins/metabolism
- Coronavirus Nucleocapsid Proteins/chemistry
- Coronavirus Nucleocapsid Proteins/genetics
- Coronavirus Nucleocapsid Proteins/metabolism
- Host-Pathogen Interactions/genetics
- Humans
- Mitochondrial Membrane Transport Proteins/chemistry
- Mitochondrial Membrane Transport Proteins/genetics
- Mitochondrial Membrane Transport Proteins/metabolism
- Mitochondrial Precursor Protein Import Complex Proteins
- Models, Molecular
- Molecular Mimicry
- Neuropilin-1/chemistry
- Neuropilin-1/genetics
- Neuropilin-1/metabolism
- Phosphoproteins/chemistry
- Phosphoproteins/genetics
- Phosphoproteins/metabolism
- Protein Binding
- Protein Conformation, alpha-Helical
- Protein Conformation, beta-Strand
- Protein Interaction Domains and Motifs
- Protein Interaction Mapping/methods
- Protein Multimerization
- Protein Processing, Post-Translational
- SARS-CoV-2/chemistry
- SARS-CoV-2/genetics
- SARS-CoV-2/metabolism
- Spike Glycoprotein, Coronavirus/chemistry
- Spike Glycoprotein, Coronavirus/genetics
- Spike Glycoprotein, Coronavirus/metabolism
- Viral Matrix Proteins/chemistry
- Viral Matrix Proteins/genetics
- Viral Matrix Proteins/metabolism
- Viroporin Proteins/chemistry
- Viroporin Proteins/genetics
- Viroporin Proteins/metabolism
- Virus Replication
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Affiliation(s)
- Seán I O’Donoghue
- Garvan Institute of Medical ResearchDarlinghurstNSWAustralia
- CSIRO Data61CanberraACTAustralia
- School of Biotechnology and Biomolecular Sciences (UNSW)KensingtonNSWAustralia
| | - Andrea Schafferhans
- Garvan Institute of Medical ResearchDarlinghurstNSWAustralia
- Department of Bioengineering SciencesWeihenstephan‐Tr. University of Applied SciencesFreisingGermany
- Department of InformaticsBioinformatics & Computational BiologyTechnical University of MunichMunichGermany
| | - Neblina Sikta
- Garvan Institute of Medical ResearchDarlinghurstNSWAustralia
| | | | - Sandeep Kaur
- Garvan Institute of Medical ResearchDarlinghurstNSWAustralia
- School of Biotechnology and Biomolecular Sciences (UNSW)KensingtonNSWAustralia
| | - Bosco K Ho
- Garvan Institute of Medical ResearchDarlinghurstNSWAustralia
| | | | | | - Christian Dallago
- Department of InformaticsBioinformatics & Computational BiologyTechnical University of MunichMunichGermany
| | - Nicola Bordin
- Institute of Structural and Molecular BiologyUniversity College LondonLondonUK
| | | | - Burkhard Rost
- Department of InformaticsBioinformatics & Computational BiologyTechnical University of MunichMunichGermany
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25
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Dallago C, Goldberg T, Andrade-Navarro MA, Alanis-Lobato G, Rost B. Visualizing Human Protein-Protein Interactions and Subcellular Localizations on Cell Images Through CellMap. ACTA ACUST UNITED AC 2021; 69:e97. [PMID: 32150354 DOI: 10.1002/cpbi.97] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Visualizing protein data remains a challenging and stimulating task. Useful and intuitive visualization tools may help advance biomolecular and medical research; unintuitive tools may bar important breakthroughs. This protocol describes two use cases for the CellMap (http://cellmap.protein.properties) web tool. The tool allows researchers to visualize human protein-protein interaction data constrained by protein subcellular localizations. In the simplest form, proteins are visualized on cell images that also show protein-protein interactions (PPIs) through lines (edges) connecting the proteins across the compartments. At a glance, this simultaneously highlights spatial constraints that proteins are subject to in their physical environment and visualizes PPIs against these localizations. Visualizing two realities helps in decluttering the protein interaction visualization from "hairball" phenomena that arise when single proteins or groups thereof interact with hundreds of partners. © 2019 The Authors. Basic Protocol 1: Visualizing proteins and their interactions on cell images Basic Protocol 2: Displaying all interaction partners for a protein.
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Affiliation(s)
- Christian Dallago
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Tatyana Goldberg
- Department of Informatics, Technical University of Munich, Garching, Germany
| | | | - Gregorio Alanis-Lobato
- Faculty of Biology, Johannes Gutenberg University Mainz, Mainz, Germany.,Human Embryo and Stem Cell Laboratory, The Francis Crick Institute, London, United Kingdom
| | - Burkhard Rost
- Department of Informatics, Technical University of Munich, Garching, Germany.,Institute for Advanced Study (TUM-IAS), Garching, Germany.,TUM School of Life Sciences Weihenstephan (WZW), Freising, Germany.,Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York.,New York Consortium on Membrane Protein Structure (NYCOMPS), New York, New York
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26
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Bernhofer M, Dallago C, Karl T, Satagopam V, Heinzinger M, Littmann M, Olenyi T, Qiu J, Schütze K, Yachdav G, Ashkenazy H, Ben-Tal N, Bromberg Y, Goldberg T, Kajan L, O’Donoghue S, Sander C, Schafferhans A, Schlessinger A, Vriend G, Mirdita M, Gawron P, Gu W, Jarosz Y, Trefois C, Steinegger M, Schneider R, Rost B. PredictProtein - Predicting Protein Structure and Function for 29 Years. Nucleic Acids Res 2021; 49:W535-W540. [PMID: 33999203 PMCID: PMC8265159 DOI: 10.1093/nar/gkab354] [Citation(s) in RCA: 103] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/06/2021] [Accepted: 05/10/2021] [Indexed: 12/12/2022] Open
Abstract
Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings.
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Affiliation(s)
- Michael Bernhofer
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
- TUM Graduate School CeDoSIA, Boltzmannstr 11, 85748 Garching, Germany
| | - Christian Dallago
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
- TUM Graduate School CeDoSIA, Boltzmannstr 11, 85748 Garching, Germany
| | - Tim Karl
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
| | - Venkata Satagopam
- Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
- ELIXIR Luxembourg (ELIXIR-LU) Node, University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Michael Heinzinger
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
- TUM Graduate School CeDoSIA, Boltzmannstr 11, 85748 Garching, Germany
| | - Maria Littmann
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
- TUM Graduate School CeDoSIA, Boltzmannstr 11, 85748 Garching, Germany
| | - Tobias Olenyi
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
| | - Jiajun Qiu
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
- Department of Otolaryngology Head & Neck Surgery, The Ninth People's Hospital & Ear Institute, School of Medicine & Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai Jiao Tong University, Shanghai, China
| | - Konstantin Schütze
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
| | - Guy Yachdav
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
| | - Haim Ashkenazy
- Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tübingen, Germany
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, 69978 Tel Aviv, Israel
| | - Nir Ben-Tal
- Department of Biochemistry & Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, 69978 Tel Aviv, Israel
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08901, USA
| | - Tatyana Goldberg
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
| | - Laszlo Kajan
- Roche Polska Sp. z o.o., Domaniewska 39B, 02–672 Warsaw, Poland
| | | | - Chris Sander
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA 02215, USA
- Broad Institute of MIT and Harvard, Boston, MA 02142, USA
| | - Andrea Schafferhans
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
- HSWT (Hochschule Weihenstephan Triesdorf | University of Applied Sciences), Department of Bioengineering Sciences, Am Hofgarten 10, 85354 Freising, Germany
| | - Avner Schlessinger
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Milot Mirdita
- Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Piotr Gawron
- Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Wei Gu
- Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
- ELIXIR Luxembourg (ELIXIR-LU) Node, University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Yohan Jarosz
- Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
- ELIXIR Luxembourg (ELIXIR-LU) Node, University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Christophe Trefois
- Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
- ELIXIR Luxembourg (ELIXIR-LU) Node, University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Martin Steinegger
- School of Biological Sciences, Seoul National University, Seoul, South Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
| | - Reinhard Schneider
- Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
- ELIXIR Luxembourg (ELIXIR-LU) Node, University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Burkhard Rost
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
- Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748 Garching/Munich, Germany
- TUM School of Life Sciences Weihenstephan (WZW), Alte Akademie 8, Freising, Germany
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27
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Dallago C, Schütze K, Heinzinger M, Olenyi T, Littmann M, Lu AX, Yang KK, Min S, Yoon S, Morton JT, Rost B. Learned Embeddings from Deep Learning to Visualize and Predict Protein Sets. Curr Protoc 2021; 1:e113. [PMID: 33961736 DOI: 10.1002/cpz1.113] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Models from machine learning (ML) or artificial intelligence (AI) increasingly assist in guiding experimental design and decision making in molecular biology and medicine. Recently, Language Models (LMs) have been adapted from Natural Language Processing (NLP) to encode the implicit language written in protein sequences. Protein LMs show enormous potential in generating descriptive representations (embeddings) for proteins from just their sequences, in a fraction of the time with respect to previous approaches, yet with comparable or improved predictive ability. Researchers have trained a variety of protein LMs that are likely to illuminate different angles of the protein language. By leveraging the bio_embeddings pipeline and modules, simple and reproducible workflows can be laid out to generate protein embeddings and rich visualizations. Embeddings can then be leveraged as input features through machine learning libraries to develop methods predicting particular aspects of protein function and structure. Beyond the workflows included here, embeddings have been leveraged as proxies to traditional homology-based inference and even to align similar protein sequences. A wealth of possibilities remain for researchers to harness through the tools provided in the following protocols. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. The following protocols are included in this manuscript: Basic Protocol 1: Generic use of the bio_embeddings pipeline to plot protein sequences and annotations Basic Protocol 2: Generate embeddings from protein sequences using the bio_embeddings pipeline Basic Protocol 3: Overlay sequence annotations onto a protein space visualization Basic Protocol 4: Train a machine learning classifier on protein embeddings Alternate Protocol 1: Generate 3D instead of 2D visualizations Alternate Protocol 2: Visualize protein solubility instead of protein subcellular localization Support Protocol: Join embedding generation and sequence space visualization in a pipeline.
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Affiliation(s)
- Christian Dallago
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology, Garching/Munich, Germany.,TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Garching/Munich, Germany
| | - Konstantin Schütze
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology, Garching/Munich, Germany
| | - Michael Heinzinger
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology, Garching/Munich, Germany.,TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Garching/Munich, Germany
| | - Tobias Olenyi
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology, Garching/Munich, Germany
| | - Maria Littmann
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology, Garching/Munich, Germany.,TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Garching/Munich, Germany
| | - Amy X Lu
- Department of Computer Science, University of Toronto, Toronto, Canada & Vector Institute
| | - Kevin K Yang
- Microsoft Research New England, Cambridge, Massachusetts
| | - Seonwoo Min
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
| | - Sungroh Yoon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea.,Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
| | - James T Morton
- Center for Computational Biology, Flatiron Institute, New York, New York
| | - Burkhard Rost
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology, Garching/Munich, Germany.,Institute for Advanced Study (TUM-IAS), Garching/Munich, Germany.,TUM School of Life Sciences Weihenstephan (WZW), Freising, Germany.,Columbia University, Department of Biochemistry and Molecular Biophysics, New York, New York.,New York Consortium on Membrane Protein Structure (NYCOMPS), New York, New York
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28
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Littmann M, Bordin N, Heinzinger M, Schütze K, Dallago C, Orengo C, Rost B. Clustering FunFams using sequence embeddings improves EC purity. Bioinformatics 2021; 37:3449-3455. [PMID: 33978744 PMCID: PMC8545299 DOI: 10.1093/bioinformatics/btab371] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 04/02/2021] [Accepted: 05/11/2021] [Indexed: 12/05/2022] Open
Abstract
Motivation Classifying proteins into functional families can improve our understanding of protein function and can allow transferring annotations within one family. For this, functional families need to be ‘pure’, i.e., contain only proteins with identical function. Functional Families (FunFams) cluster proteins within CATH superfamilies into such groups of proteins sharing function. 11% of all FunFams (22 830 of 203 639) contain EC annotations and of those, 7% (1526 of 22 830) have inconsistent functional annotations. Results We propose an approach to further cluster FunFams into functionally more consistent sub-families by encoding their sequences through embeddings. These embeddings originate from language models transferring knowledge gained from predicting missing amino acids in a sequence (ProtBERT) and have been further optimized to distinguish between proteins belonging to the same or a different CATH superfamily (PB-Tucker). Using distances between embeddings and DBSCAN to cluster FunFams and identify outliers, doubled the number of pure clusters per FunFam compared to random clustering. Our approach was not limited to FunFams but also succeeded on families created using sequence similarity alone. Complementing EC annotations, we observed similar results for binding annotations. Thus, we expect an increased purity also for other aspects of function. Our results can help generating FunFams; the resulting clusters with improved functional consistency allow more reliable inference of annotations. We expect this approach to succeed equally for any other grouping of proteins by their phenotypes. Availability and implementation Code and embeddings are available via GitHub: https://github.com/Rostlab/FunFamsClustering. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Maria Littmann
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany.,TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748 Garching, Germany
| | - Nicola Bordin
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
| | - Michael Heinzinger
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany.,TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748 Garching, Germany
| | - Konstantin Schütze
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
| | - Christian Dallago
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany.,TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748 Garching, Germany
| | - Christine Orengo
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
| | - Burkhard Rost
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany.,Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748 Garching/Munich, Germany & TUM School of Life Sciences Weihenstephan (WZW), Alte Akademie 8, Freising, Germany
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29
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Abstract
Knowing protein function is crucial to advance molecular and medical biology, yet experimental function annotations through the Gene Ontology (GO) exist for fewer than 0.5% of all known proteins. Computational methods bridge this sequence-annotation gap typically through homology-based annotation transfer by identifying sequence-similar proteins with known function or through prediction methods using evolutionary information. Here, we propose predicting GO terms through annotation transfer based on proximity of proteins in the SeqVec embedding rather than in sequence space. These embeddings originate from deep learned language models (LMs) for protein sequences (SeqVec) transferring the knowledge gained from predicting the next amino acid in 33 million protein sequences. Replicating the conditions of CAFA3, our method reaches an Fmax of 37 ± 2%, 50 ± 3%, and 57 ± 2% for BPO, MFO, and CCO, respectively. Numerically, this appears close to the top ten CAFA3 methods. When restricting the annotation transfer to proteins with < 20% pairwise sequence identity to the query, performance drops (Fmax BPO 33 ± 2%, MFO 43 ± 3%, CCO 53 ± 2%); this still outperforms naïve sequence-based transfer. Preliminary results from CAFA4 appear to confirm these findings. Overall, this new concept is likely to change the annotation of proteins, in particular for proteins from smaller families or proteins with intrinsically disordered regions.
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Affiliation(s)
- Maria Littmann
- Department of Informatics, Bioinformatics and Computational Biology, i12, TUM (Technical University of Munich), Boltzmannstr. 3, Garching, 85748, Munich, Germany.
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany.
| | - Michael Heinzinger
- Department of Informatics, Bioinformatics and Computational Biology, i12, TUM (Technical University of Munich), Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Christian Dallago
- Department of Informatics, Bioinformatics and Computational Biology, i12, TUM (Technical University of Munich), Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Tobias Olenyi
- Department of Informatics, Bioinformatics and Computational Biology, i12, TUM (Technical University of Munich), Boltzmannstr. 3, Garching, 85748, Munich, Germany
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology, i12, TUM (Technical University of Munich), Boltzmannstr. 3, Garching, 85748, Munich, Germany
- Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, Garching, 85748, Munich, Germany
- School of Life Sciences Weihenstephan (TUM-WZW), TUM (Technical University of Munich), Alte Akademie 8, Freising, Germany
- Department of Biochemistry and Molecular Biophysics, Columbia University, 701 West, 168th Street, New York, NY, 10032, USA
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30
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Qiu J, Nechaev D, Rost B. Protein-protein and protein-nucleic acid binding residues important for common and rare sequence variants in human. BMC Bioinformatics 2020; 21:452. [PMID: 33050876 PMCID: PMC7557062 DOI: 10.1186/s12859-020-03759-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 09/16/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Any two unrelated people differ by about 20,000 missense mutations (also referred to as SAVs: Single Amino acid Variants or missense SNV). Many SAVs have been predicted to strongly affect molecular protein function. Common SAVs (> 5% of population) were predicted to have, on average, more effect on molecular protein function than rare SAVs (< 1% of population). We hypothesized that the prevalence of effect in common over rare SAVs might partially be caused by common SAVs more often occurring at interfaces of proteins with other proteins, DNA, or RNA, thereby creating subgroup-specific phenotypes. We analyzed SAVs from 60,706 people through the lens of two prediction methods, one (SNAP2) predicting the effects of SAVs on molecular protein function, the other (ProNA2020) predicting residues in DNA-, RNA- and protein-binding interfaces. RESULTS Three results stood out. Firstly, SAVs predicted to occur at binding interfaces were predicted to more likely affect molecular function than those predicted as not binding (p value < 2.2 × 10-16). Secondly, for SAVs predicted to occur at binding interfaces, common SAVs were predicted more strongly with effect on protein function than rare SAVs (p value < 2.2 × 10-16). Restriction to SAVs with experimental annotations confirmed all results, although the resulting subsets were too small to establish statistical significance for any result. Thirdly, the fraction of SAVs predicted at binding interfaces differed significantly between tissues, e.g. urinary bladder tissue was found abundant in SAVs predicted at protein-binding interfaces, and reproductive tissues (ovary, testis, vagina, seminal vesicle and endometrium) in SAVs predicted at DNA-binding interfaces. CONCLUSIONS Overall, the results suggested that residues at protein-, DNA-, and RNA-binding interfaces contributed toward predicting that common SAVs more likely affect molecular function than rare SAVs.
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Affiliation(s)
- Jiajun Qiu
- Department of Informatics, I12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Boltzmannstrasse 3, 85748, Garching, Munich, Germany. .,TUM Graduate School, Center of Doctoral Studies in Informatics and Its Applications (CeDoSIA), 85748, Garching, Germany. .,Biobank of Ninth People's Hospital, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200125, China.
| | - Dmitrii Nechaev
- Department of Informatics, I12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Boltzmannstrasse 3, 85748, Garching, Munich, Germany.,TUM Graduate School, Center of Doctoral Studies in Informatics and Its Applications (CeDoSIA), 85748, Garching, Germany
| | - Burkhard Rost
- Department of Informatics, I12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Boltzmannstrasse 3, 85748, Garching, Munich, Germany.,Institute of Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748, Garching, Munich, Germany.,Institute for Food and Plant Sciences (WZW) Weihenstephan, Alte Akademie 8, 85354, Freising, Germany
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31
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Zaucha J, Heinzinger M, Kulandaisamy A, Kataka E, Salvádor ÓL, Popov P, Rost B, Gromiha MM, Zhorov BS, Frishman D. Mutations in transmembrane proteins: diseases, evolutionary insights, prediction and comparison with globular proteins. Brief Bioinform 2020; 22:5872174. [PMID: 32672331 DOI: 10.1093/bib/bbaa132] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 05/26/2020] [Accepted: 05/28/2020] [Indexed: 12/18/2022] Open
Abstract
Membrane proteins are unique in that they interact with lipid bilayers, making them indispensable for transporting molecules and relaying signals between and across cells. Due to the significance of the protein's functions, mutations often have profound effects on the fitness of the host. This is apparent both from experimental studies, which implicated numerous missense variants in diseases, as well as from evolutionary signals that allow elucidating the physicochemical constraints that intermembrane and aqueous environments bring. In this review, we report on the current state of knowledge acquired on missense variants (referred to as to single amino acid variants) affecting membrane proteins as well as the insights that can be extrapolated from data already available. This includes an overview of the annotations for membrane protein variants that have been collated within databases dedicated to the topic, bioinformatics approaches that leverage evolutionary information in order to shed light on previously uncharacterized membrane protein structures or interaction interfaces, tools for predicting the effects of mutations tailored specifically towards the characteristics of membrane proteins as well as two clinically relevant case studies explaining the implications of mutated membrane proteins in cancer and cardiomyopathy.
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Affiliation(s)
- Jan Zaucha
- Department of Bioinformatics of the TUM School of Life Sciences Weihenstephan in Freising, Germany
| | - Michael Heinzinger
- Department of Informatics, Bioinformatics and Computational Biology of the TUM Faculty of Informatics in Garching, Germany
| | - A Kulandaisamy
- Department of Biotechnology of the IIT Bhupat and Jyoti Mehta School of BioSciences in Madras, India
| | - Evans Kataka
- Department of Bioinformatics of the TUM School of Life Sciences Weihenstephan in Freising, Germany
| | - Óscar Llorian Salvádor
- Department of Informatics, Bioinformatics and Computational Biology of the TUM Faculty of Informatics in Garching, Germany
| | - Petr Popov
- Center for Computational and Data-Intensive Science and Engineering of the Skolkovo Institute of Science and Technology in Moscow, Russia
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology at the TUM Faculty of Informatics in Garching, Germany
| | | | - Boris S Zhorov
- Department of Biochemistry and Biomedical Sciences, McMaster University in Hamilton, Canada
| | - Dmitrij Frishman
- Department of Bioinformatics at the TUM School of Life Sciences Weihenstephan in Freising, Germany
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32
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Lai JS, Rost B, Kobe B, Bodén M. Evolutionary model of protein secondary structure capable of revealing new biological relationships. Proteins 2020; 88:1251-1259. [PMID: 32394426 DOI: 10.1002/prot.25898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Revised: 10/24/2019] [Accepted: 04/27/2020] [Indexed: 11/09/2022]
Abstract
Ancestral sequence reconstruction has had recent success in decoding the origins and the determinants of complex protein functions. However, phylogenetic analyses of remote homologues must handle extreme amino acid sequence diversity resulting from extended periods of evolutionary change. We exploited the wealth of protein structures to develop an evolutionary model based on protein secondary structure. The approach follows the differences between discrete secondary structure states observed in modern proteins and those hypothesized in their immediate ancestors. We implemented maximum likelihood-based phylogenetic inference to reconstruct ancestral secondary structure. The predictive accuracy from the use of the evolutionary model surpasses that of comparative modeling and sequence-based prediction; the reconstruction extracts information not available from modern structures or the ancestral sequences alone. Based on a phylogenetic analysis of a sequence-diverse protein family, we showed that the model can highlight relationships that are evolutionarily rooted in structure and not evident in amino acid-based analysis.
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Affiliation(s)
- Jhih-Siang Lai
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Queensland, Australia
| | - Burkhard Rost
- Department of Informatics, Institute of Advanced Studies (TUM-IAS), School of Life Sciences (WZW), Technical University of Munich (TUM), Garching, Bavaria, Germany
| | - Bostjan Kobe
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Queensland, Australia.,Australian Infectious Diseases Research Centre, The University of Queensland, Brisbane, Queensland, Australia.,Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Mikael Bodén
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Queensland, Australia.,Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
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33
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Miller M, Vitale D, Kahn PC, Rost B, Bromberg Y. funtrp: identifying protein positions for variation driven functional tuning. Nucleic Acids Res 2020; 47:e142. [PMID: 31584091 PMCID: PMC6868392 DOI: 10.1093/nar/gkz818] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 09/05/2019] [Accepted: 09/12/2019] [Indexed: 12/12/2022] Open
Abstract
Evaluating the impact of non-synonymous genetic variants is essential for uncovering disease associations and mechanisms of evolution. An in-depth understanding of sequence changes is also fundamental for synthetic protein design and stability assessments. However, the variant effect predictor performance gain observed in recent years has not kept up with the increased complexity of new methods. One likely reason for this might be that most approaches use similar sets of gene and protein features for modeling variant effects, often emphasizing sequence conservation. While high levels of conservation highlight residues essential for protein activity, much of the variation observable in vivo is arguably weaker in its impact, thus requiring evaluation at a higher level of resolution. Here, we describe functionNeutral/Toggle/Rheostatpredictor (funtrp), a novel computational method that categorizes protein positions based on the position-specific expected range of mutational impacts: Neutral (weak/no effects), Rheostat (function-tuning positions), or Toggle (on/off switches). We show that position types do not correlate strongly with familiar protein features such as conservation or protein disorder. We also find that position type distribution varies across different protein functions. Finally, we demonstrate that position types can improve performance of existing variant effect predictors and suggest a way forward for the development of new ones.
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Affiliation(s)
- Maximilian Miller
- Department of Biochemistry and Microbiology, Rutgers University, 76 Lipman Dr, New Brunswick, NJ 08901, USA
| | - Daniel Vitale
- Columbian College of Arts and Sciences Data Science Program Corcoran Hall, 725 21st Street NW, Washington, DC 20052, USA
| | - Peter C Kahn
- Department of Biochemistry and Microbiology, Rutgers University, 76 Lipman Dr, New Brunswick, NJ 08901, USA
| | - Burkhard Rost
- Department for Bioinformatics and Computational Biology, Technische Universität München, Boltzmannstr. 3, 85748 Garching/Munich, Germany.,Institute for Advanced Study at Technische Universität München (TUM-IAS), Lichtenbergstraße 2a 85748 Garching/Munich, Germany
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, 76 Lipman Dr, New Brunswick, NJ 08901, USA.,Institute for Advanced Study at Technische Universität München (TUM-IAS), Lichtenbergstraße 2a 85748 Garching/Munich, Germany.,Department of Genetics, Rutgers University, Human Genetics Institute, Life Sciences Building, 145 Bevier Road, Piscataway, NJ 08854, USA
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34
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Abstract
BACKGROUND Deep mutational scanning (DMS) studies exploit the mutational landscape of sequence variation by systematically and comprehensively assaying the effect of single amino acid variants (SAVs; also referred to as missense mutations, or non-synonymous Single Nucleotide Variants - missense SNVs or nsSNVs) for particular proteins. We assembled SAV annotations from 22 different DMS experiments and normalized the effect scores to evaluate variant effect prediction methods. Three trained on traditional variant effect data (PolyPhen-2, SIFT, SNAP2), a regression method optimized on DMS data (Envision), and a naïve prediction using conservation information from homologs. RESULTS On a set of 32,981 SAVs, all methods captured some aspects of the experimental effect scores, albeit not the same. Traditional methods such as SNAP2 correlated slightly more with measurements and better classified binary states (effect or neutral). Envision appeared to better estimate the precise degree of effect. Most surprising was that the simple naïve conservation approach using PSI-BLAST in many cases outperformed other methods. All methods captured beneficial effects (gain-of-function) significantly worse than deleterious (loss-of-function). For the few proteins with multiple independent experimental measurements, experiments differed substantially, but agreed more with each other than with predictions. CONCLUSIONS DMS provides a new powerful experimental means of understanding the dynamics of the protein sequence space. As always, promising new beginnings have to overcome challenges. While our results demonstrated that DMS will be crucial to improve variant effect prediction methods, data diversity hindered simplification and generalization.
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Affiliation(s)
- Jonas Reeb
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr 3, 85748, Garching/Munich, Germany.
| | - Theresa Wirth
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr 3, 85748, Garching/Munich, Germany
| | - Burkhard Rost
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr 3, 85748, Garching/Munich, Germany
- Institute for Advanced Study (TUM-IAS), Lichtenbergstr 2a, 85748, Garching/Munich, Germany
- TUM School of Life Sciences Weihenstephan (WZW), Alte Akademie 8, Freising, Germany
- Department of Biochemistry and Molecular Biophysics, Columbia University, 701 West, 168th Street, New York, NY, 10032, USA
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35
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Qiu J, Bernhofer M, Heinzinger M, Kemper S, Norambuena T, Melo F, Rost B. ProNA2020 predicts protein-DNA, protein-RNA, and protein-protein binding proteins and residues from sequence. J Mol Biol 2020; 432:2428-2443. [PMID: 32142788 DOI: 10.1016/j.jmb.2020.02.026] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 02/17/2020] [Accepted: 02/23/2020] [Indexed: 11/29/2022]
Abstract
The intricate details of how proteins bind to proteins, DNA, and RNA are crucial for the understanding of almost all biological processes. Disease-causing sequence variants often affect binding residues. Here, we described a new, comprehensive system of in silico methods that take only protein sequence as input to predict binding of protein to DNA, RNA, and other proteins. Firstly, we needed to develop several new methods to predict whether or not proteins bind (per-protein prediction). Secondly, we developed independent methods that predict which residues bind (per-residue). Not requiring three-dimensional information, the system can predict the actual binding residue. The system combined homology-based inference with machine learning and motif-based profile-kernel approaches with word-based (ProtVec) solutions to machine learning protein level predictions. This achieved an overall non-exclusive three-state accuracy of 77% ± 1% (±one standard error) corresponding to a 1.8 fold improvement over random (best classification for protein-protein with F1 = 91 ± 0.8%). Standard neural networks for per-residue binding residue predictions appeared best for DNA-binding (Q2 = 81 ± 0.9%) followed by RNA-binding (Q2 = 80 ± 1%) and worst for protein-protein binding (Q2 = 69 ± 0.8%). The new method, dubbed ProNA2020, is available as code through github (https://github.com/Rostlab/ProNA2020.git) and through PredictProtein (www.predictprotein.org).
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Affiliation(s)
- Jiajun Qiu
- Department of Informatics, I12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Boltzmannstrasse 3, 85748, Garching, Munich, Germany; TUM Graduate School, Center of Doctoral Studies in Informatics and Its Applications (CeDoSIA), Garching, 85748, Germany.
| | - Michael Bernhofer
- Department of Informatics, I12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Boltzmannstrasse 3, 85748, Garching, Munich, Germany; TUM Graduate School, Center of Doctoral Studies in Informatics and Its Applications (CeDoSIA), Garching, 85748, Germany
| | - Michael Heinzinger
- Department of Informatics, I12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Boltzmannstrasse 3, 85748, Garching, Munich, Germany; TUM Graduate School, Center of Doctoral Studies in Informatics and Its Applications (CeDoSIA), Garching, 85748, Germany
| | - Sofie Kemper
- Department of Informatics, I12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Boltzmannstrasse 3, 85748, Garching, Munich, Germany
| | - Tomas Norambuena
- Molecular Bioinformatics Laboratory, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Francisco Melo
- Molecular Bioinformatics Laboratory, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile; Institute of Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Burkhard Rost
- Department of Informatics, I12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Boltzmannstrasse 3, 85748, Garching, Munich, Germany; Columbia University, Department of Biochemistry and Molecular Biophysics, 701 West, 168th Street, New York, NY, 10032, USA; Institute of Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748, Garching/Munich, Germany; Germany & Institute for Food and Plant Sciences (WZW) Weihenstephan, Alte Akademie 8, 85354 Freising, Germany
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36
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Zaucha J, Heinzinger M, Tarnovskaya S, Rost B, Frishman D. Family-specific analysis of variant pathogenicity prediction tools. NAR Genom Bioinform 2020; 2:lqaa014. [PMID: 33575576 PMCID: PMC7671395 DOI: 10.1093/nargab/lqaa014] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 02/12/2020] [Accepted: 02/25/2020] [Indexed: 01/01/2023] Open
Abstract
Using the presently available datasets of annotated missense variants, we ran a protein family-specific benchmarking of tools for predicting the pathogenicity of single amino acid variants. We find that despite the high overall accuracy of all tested methods, each tool has its Achilles heel, i.e. protein families in which its predictions prove unreliable (expected accuracy does not exceed 51% in any method). As a proof of principle, we show that choosing the optimal tool and pathogenicity threshold at a protein family-individual level allows obtaining reliable predictions in all Pfam domains (accuracy no less than 68%). A functional analysis of the sets of protein domains annotated exclusively by neutral or pathogenic mutations indicates that specific protein functions can be associated with a high or low sensitivity to mutations, respectively. The highly sensitive sets of protein domains are involved in the regulation of transcription and DNA sequence-specific transcription factor binding, while the domains that do not result in disease when mutated are responsible for mediating immune and stress responses. These results suggest that future predictors of pathogenicity and especially variant prioritization tools may benefit from considering functional annotation.
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Affiliation(s)
- Jan Zaucha
- Department of Bioinformatics, Technical University of Munich, 85354 Freising, Germany
| | - Michael Heinzinger
- Department of Informatics, Bioinformatics & Computational Biology-i12, Technical University of Munich, 85748 Garching, Germany
| | | | - Burkhard Rost
- Department of Informatics, Bioinformatics & Computational Biology-i12, Technical University of Munich, 85748 Garching, Germany
| | - Dmitrij Frishman
- Department of Bioinformatics, Technical University of Munich, 85354 Freising, Germany
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37
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Littmann M, Selig K, Cohen-Lavi L, Frank Y, Hönigschmid P, Kataka E, Mösch A, Qian K, Ron A, Schmid S, Sorbie A, Szlak L, Dagan-Wiener A, Ben-Tal N, Niv MY, Razansky D, Schuller BW, Ankerst D, Hertz T, Rost B. Validity of machine learning in biology and medicine increased through collaborations across fields of expertise. NAT MACH INTELL 2020. [DOI: 10.1038/s42256-019-0139-8] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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38
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Littmann M, Goldberg T, Seitz S, Bodén M, Rost B. Correction to: Detailed prediction of protein sub-nuclear localization. BMC Bioinformatics 2019; 20:727. [PMID: 31861997 PMCID: PMC6925513 DOI: 10.1186/s12859-019-3305-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Affiliation(s)
- Maria Littmann
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany.
| | - Tatyana Goldberg
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany
| | - Sebastian Seitz
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany
| | - Mikael Bodén
- School of Chemistry and Molecular Biosciences, UQ (University of Queensland), Cooper Rd, Brisbane City, QLD 4072, Australia
| | - Burkhard Rost
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany.,Institute for Advanced Study (TUM-IAS), Lichtenbergstr 2a, 85748, Garching/Munich, Germany.,TUM School of Life Sciences Weihenstephan (WZW), Alte Akademie 8, Freising, Germany.,Department of Biochemistry and Molecular Biophysics & New York Consortium on Membrane Protein Structure (NYCOMPS), Columbia University, 701 West, 168th Street, New York, NY, 10032, USA
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39
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Heinzinger M, Elnaggar A, Wang Y, Dallago C, Nechaev D, Matthes F, Rost B. Modeling aspects of the language of life through transfer-learning protein sequences. BMC Bioinformatics 2019; 20:723. [PMID: 31847804 PMCID: PMC6918593 DOI: 10.1186/s12859-019-3220-8] [Citation(s) in RCA: 205] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 11/13/2019] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Predicting protein function and structure from sequence is one important challenge for computational biology. For 26 years, most state-of-the-art approaches combined machine learning and evolutionary information. However, for some applications retrieving related proteins is becoming too time-consuming. Additionally, evolutionary information is less powerful for small families, e.g. for proteins from the Dark Proteome. Both these problems are addressed by the new methodology introduced here. RESULTS We introduced a novel way to represent protein sequences as continuous vectors (embeddings) by using the language model ELMo taken from natural language processing. By modeling protein sequences, ELMo effectively captured the biophysical properties of the language of life from unlabeled big data (UniRef50). We refer to these new embeddings as SeqVec (Sequence-to-Vector) and demonstrate their effectiveness by training simple neural networks for two different tasks. At the per-residue level, secondary structure (Q3 = 79% ± 1, Q8 = 68% ± 1) and regions with intrinsic disorder (MCC = 0.59 ± 0.03) were predicted significantly better than through one-hot encoding or through Word2vec-like approaches. At the per-protein level, subcellular localization was predicted in ten classes (Q10 = 68% ± 1) and membrane-bound were distinguished from water-soluble proteins (Q2 = 87% ± 1). Although SeqVec embeddings generated the best predictions from single sequences, no solution improved over the best existing method using evolutionary information. Nevertheless, our approach improved over some popular methods using evolutionary information and for some proteins even did beat the best. Thus, they prove to condense the underlying principles of protein sequences. Overall, the important novelty is speed: where the lightning-fast HHblits needed on average about two minutes to generate the evolutionary information for a target protein, SeqVec created embeddings on average in 0.03 s. As this speed-up is independent of the size of growing sequence databases, SeqVec provides a highly scalable approach for the analysis of big data in proteomics, i.e. microbiome or metaproteome analysis. CONCLUSION Transfer-learning succeeded to extract information from unlabeled sequence databases relevant for various protein prediction tasks. SeqVec modeled the language of life, namely the principles underlying protein sequences better than any features suggested by textbooks and prediction methods. The exception is evolutionary information, however, that information is not available on the level of a single sequence.
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Affiliation(s)
- Michael Heinzinger
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany.
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany.
| | - Ahmed Elnaggar
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Yu Wang
- Leibniz Supercomputing Centre, Boltzmannstr. 1, 85748, Garching/Munich, Germany
| | - Christian Dallago
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Dmitrii Nechaev
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Florian Matthes
- TUM Department of Informatics, Software Engineering and Business Information Systems, Boltzmannstr. 1, 85748, Garching/Munich, Germany
| | - Burkhard Rost
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany
- Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748, Garching/Munich, Germany
- TUM School of Life Sciences Weihenstephan (WZW), Alte Akademie 8, Freising, Germany
- Department of Biochemistry and Molecular Biophysics & New York Consortium on Membrane Protein Structure (NYCOMPS), Columbia University, 701 West, 168th Street, New York, NY, 10032, USA
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40
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Zhou N, Jiang Y, Bergquist TR, Lee AJ, Kacsoh BZ, Crocker AW, Lewis KA, Georghiou G, Nguyen HN, Hamid MN, Davis L, Dogan T, Atalay V, Rifaioglu AS, Dalkıran A, Cetin Atalay R, Zhang C, Hurto RL, Freddolino PL, Zhang Y, Bhat P, Supek F, Fernández JM, Gemovic B, Perovic VR, Davidović RS, Sumonja N, Veljkovic N, Asgari E, Mofrad MRK, Profiti G, Savojardo C, Martelli PL, Casadio R, Boecker F, Schoof H, Kahanda I, Thurlby N, McHardy AC, Renaux A, Saidi R, Gough J, Freitas AA, Antczak M, Fabris F, Wass MN, Hou J, Cheng J, Wang Z, Romero AE, Paccanaro A, Yang H, Goldberg T, Zhao C, Holm L, Törönen P, Medlar AJ, Zosa E, Borukhov I, Novikov I, Wilkins A, Lichtarge O, Chi PH, Tseng WC, Linial M, Rose PW, Dessimoz C, Vidulin V, Dzeroski S, Sillitoe I, Das S, Lees JG, Jones DT, Wan C, Cozzetto D, Fa R, Torres M, Warwick Vesztrocy A, Rodriguez JM, Tress ML, Frasca M, Notaro M, Grossi G, Petrini A, Re M, Valentini G, Mesiti M, Roche DB, Reeb J, Ritchie DW, Aridhi S, Alborzi SZ, Devignes MD, Koo DCE, Bonneau R, Gligorijević V, Barot M, Fang H, Toppo S, Lavezzo E, Falda M, Berselli M, Tosatto SCE, Carraro M, Piovesan D, Ur Rehman H, Mao Q, Zhang S, Vucetic S, Black GS, Jo D, Suh E, Dayton JB, Larsen DJ, Omdahl AR, McGuffin LJ, Brackenridge DA, Babbitt PC, Yunes JM, Fontana P, Zhang F, Zhu S, You R, Zhang Z, Dai S, Yao S, Tian W, Cao R, Chandler C, Amezola M, Johnson D, Chang JM, Liao WH, Liu YW, Pascarelli S, Frank Y, Hoehndorf R, Kulmanov M, Boudellioua I, Politano G, Di Carlo S, Benso A, Hakala K, Ginter F, Mehryary F, Kaewphan S, Björne J, Moen H, Tolvanen MEE, Salakoski T, Kihara D, Jain A, Šmuc T, Altenhoff A, Ben-Hur A, Rost B, Brenner SE, Orengo CA, Jeffery CJ, Bosco G, Hogan DA, Martin MJ, O'Donovan C, Mooney SD, Greene CS, Radivojac P, Friedberg I. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens. Genome Biol 2019; 20:244. [PMID: 31744546 PMCID: PMC6864930 DOI: 10.1186/s13059-019-1835-8] [Citation(s) in RCA: 166] [Impact Index Per Article: 33.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 09/24/2019] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. RESULTS Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. CONCLUSION We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.
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Affiliation(s)
- Naihui Zhou
- Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA, USA.,Program in Bioinformatics and Computational Biology, Ames, IA, USA
| | - Yuxiang Jiang
- Indiana University Bloomington, Bloomington, Indiana, USA
| | - Timothy R Bergquist
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Alexandra J Lee
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Balint Z Kacsoh
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA.,Department of Molecular and Systems Biology, Hanover, NH, USA
| | - Alex W Crocker
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Kimberley A Lewis
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - George Georghiou
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, United Kingdom
| | - Huy N Nguyen
- Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA, USA.,Program in Computer Science, Ames, IA, USA
| | - Md Nafiz Hamid
- Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA, USA.,Program in Bioinformatics and Computational Biology, Ames, IA, USA
| | - Larry Davis
- Program in Bioinformatics and Computational Biology, Ames, IA, USA
| | - Tunca Dogan
- Department of Computer Engineering, Hacettepe University, Ankara, Turkey.,European Molecular Biolo gy Labora tory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Volkan Atalay
- Department of Computer Engineering, Middle East Technical University (METU), Ankara, Turkey
| | - Ahmet S Rifaioglu
- Department of Computer Engineering, Middle East Technical University (METU), Ankara, Turkey.,Department of Computer Engineering, Iskenderun Technical University, Hatay, Turkey
| | - Alperen Dalkıran
- Department of Computer Engineering, Middle East Technical University (METU), Ankara, Turkey
| | - Rengul Cetin Atalay
- CanSyL, Graduate School of Informatics, Middle East Technical University, Ankara, Turkey
| | - Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Rebecca L Hurto
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA
| | - Peter L Freddolino
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.,Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.,Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA
| | | | - Fran Supek
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - José M Fernández
- INB Coordination Unit, Life Sciences Department, Barcelona Supercomputing Center, Barcelona, Catalonia, Spain.,(former) INB GN2, Structural and Computational Biology Programme, Spanish National Cancer Research Centre, Barcelona, Catalonia, Spain
| | - Branislava Gemovic
- Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences VINCA, University of Belgrade, Belgrade, Serbia
| | - Vladimir R Perovic
- Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences VINCA, University of Belgrade, Belgrade, Serbia
| | - Radoslav S Davidović
- Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences VINCA, University of Belgrade, Belgrade, Serbia
| | - Neven Sumonja
- Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences VINCA, University of Belgrade, Belgrade, Serbia
| | - Nevena Veljkovic
- Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences VINCA, University of Belgrade, Belgrade, Serbia
| | - Ehsaneddin Asgari
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering, University of California Berkeley, Berkeley, CA, USA.,Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Berkeley, CA, USA
| | | | - Giuseppe Profiti
- Bologna Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.,National Research Council, IBIOM, Bologna, Italy
| | - Castrense Savojardo
- Bologna Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Pier Luigi Martelli
- Bologna Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Rita Casadio
- Bologna Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Florian Boecker
- University of Bonn: INRES Crop Bioinformatics, Bonn, North Rhine-Westphalia, Germany
| | - Heiko Schoof
- INRES Crop Bioinformatics, University of Bonn, Bonn, Germany
| | - Indika Kahanda
- Gianforte School of Computing, Montana State University, Bozeman, Montana, USA
| | - Natalie Thurlby
- University of Bristol, Computer Science, Bristol, Bristol, United Kingdom
| | - Alice C McHardy
- Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Brunswick, Germany.,RESIST, DFG Cluster of Excellence 2155, Brunswick, Germany
| | - Alexandre Renaux
- Interuniversity Institute of Bioinformatics in Brussels, Université libre de Bruxelles - Vrije Universiteit Brussel, Brussels, Belgium.,Machine Learning Group, Université libre de Bruxelles, Brussels, Belgium.,Artificial Intelligence lab, Vrije Universiteit Brussel, Brussels, Belgium
| | - Rabie Saidi
- European Molecular Biolo gy Labora tory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Julian Gough
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | - Alex A Freitas
- University of Kent, School of Computing, Canterbury, United Kingdom
| | - Magdalena Antczak
- School of Biosciences, University of Kent, Canterbury, Kent, United Kingdom
| | - Fabio Fabris
- University of Kent, School of Computing, Canterbury, United Kingdom
| | - Mark N Wass
- School of Biosciences, University of Kent, Canterbury, Kent, United Kingdom
| | - Jie Hou
- University of Missouri, Computer Science, Columbia, Missouri, USA.,Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
| | - Zheng Wang
- University of Miami, Coral Gables, Florida, USA
| | - Alfonso E Romero
- Centre for Systems and Synthetic Biology, Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, United Kingdom
| | - Alberto Paccanaro
- Centre for Systems and Synthetic Biology, Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, United Kingdom
| | - Haixuan Yang
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway, Galway, Ireland.,Technical University of Munich, Garching, Germany
| | - Tatyana Goldberg
- Department of Informatics, Bioinformatics & Computational Biology-i12, Technische Universitat Munchen, Munich, Germany
| | - Chenguang Zhao
- Faculty for Informatics, Garching, Germany.,Department for Bioinformatics and Computational Biology, Garching, Germany.,School of Computing Sciences and Computer Engineering, Hattiesburg, Mississippi, USA
| | - Liisa Holm
- Institute of Biotechnology, Helsinki Institute of Life Sciences, University of Helsinki, Finland, Helsinki, Finland
| | - Petri Törönen
- Institute of Biotechnology, Helsinki Institute of Life Sciences, University of Helsinki, Finland, Helsinki, Finland
| | - Alan J Medlar
- Institute of Biotechnology, Helsinki Institute of Life Sciences, University of Helsinki, Finland, Helsinki, Finland
| | - Elaine Zosa
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | | | - Ilya Novikov
- Baylor College of Medicine, Department of Biochemistry and Molecular Biology, Houston, TX, USA
| | - Angela Wilkins
- Baylor College of Medicine, Department of Molecular and Human Genetics, Houston, TX, USA
| | - Olivier Lichtarge
- Baylor College of Medicine, Department of Molecular and Human Genetics, Houston, TX, USA
| | - Po-Han Chi
- National TsingHua University, Hsinchu, Taiwan
| | - Wei-Cheng Tseng
- Department of Electrical Engineering in National Tsing Hua University, Hsinchu City, Taiwan
| | - Michal Linial
- The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Peter W Rose
- University of California San Diego, San Diego Supercomputer Center, La Jolla, California, USA
| | - Christophe Dessimoz
- Department of Computational Biology and Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.,Department of Genetics, Evolution & Environment, and Department of Computer Science, University College London, London, UK.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Vedrana Vidulin
- Department of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Saso Dzeroski
- Jozef Stefan Institute, Ljubljana, Slovenia.,Jozef Stefan International Postgraduate School, Ljubljana, Slovenia
| | - Ian Sillitoe
- Research Department of Structural and Molecular Biology, University College London, London, England
| | - Sayoni Das
- Research Department of Structural and Molecular Biology, University College London, London, United Kingdom
| | - Jonathan Gill Lees
- Research Department of Structural and Molecular Biology, University College London, London, United Kingdom.,Department of Health and Life Sciences, Oxford Brookes University, London, UK
| | - David T Jones
- The Francis Crick Institute, Biomedical Data Science Laboratory, London, United Kingdom.,Department of Genetics, Evolution and Environment, University College London, Gower Street, London, WC1E 6BT, United Kingdom
| | - Cen Wan
- Department of Computer Science, University College London, London, United Kingdom.,The Francis Crick Institute, Biomedical Data Science Laboratory, London, United Kingdom
| | - Domenico Cozzetto
- Department of Computer Science, University College London, London, United Kingdom.,The Francis Crick Institute, Biomedical Data Science Laboratory, London, United Kingdom
| | - Rui Fa
- Department of Computer Science, University College London, London, United Kingdom.,The Francis Crick Institute, Biomedical Data Science Laboratory, London, United Kingdom
| | - Mateo Torres
- Centre for Systems and Synthetic Biology, Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, United Kingdom
| | - Alex Warwick Vesztrocy
- Department of Genetics, Evolution and Environment, University College London, Gower Street, London, WC1E 6BT, United Kingdom.,SIB Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
| | - Jose Manuel Rodriguez
- Cardiovascular Proteomics Laboratory, Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain
| | - Michael L Tress
- Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Marco Frasca
- Università degli Studi di Milano - Computer Science Department - AnacletoLab, Milan, Milan, Italy
| | - Marco Notaro
- Università degli Studi di Milano - Computer Science Department - AnacletoLab, Milan, Milan, Italy
| | - Giuliano Grossi
- Università degli Studi di Milano - Computer Science Department - AnacletoLab, Milan, Milan, Italy
| | - Alessandro Petrini
- Università degli Studi di Milano - Computer Science Department - AnacletoLab, Milan, Milan, Italy
| | - Matteo Re
- Università degli Studi di Milano - Computer Science Department - AnacletoLab, Milan, Milan, Italy
| | - Giorgio Valentini
- Università degli Studi di Milano - Computer Science Department - AnacletoLab, Milan, Milan, Italy
| | - Marco Mesiti
- Università degli Studi di Milano - Computer Science Department - AnacletoLab, Milan, Milan, Italy.,Institut de Biologie Computationnelle, LIRMM, CNRS-UMR 5506, Universite de Montpellier, Montpellier, France
| | - Daniel B Roche
- Department of Informatics, Bioinformatics and Computational Biology-i12, Technische Universitat Munchen, Munich, Germany
| | - Jonas Reeb
- Department of Informatics, Bioinformatics and Computational Biology-i12, Technische Universitat Munchen, Munich, Germany
| | - David W Ritchie
- University of Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France
| | - Sabeur Aridhi
- University of Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France
| | | | - Marie-Dominique Devignes
- University of Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France.,University of Lorraine, Nancy, Lorraine, France.,Inria, Nancy, France
| | | | - Richard Bonneau
- NYU Center for Data Science, New York, 10010, NY, USA.,Flatiron Institute, CCB, New York, 10010, NY, USA
| | - Vladimir Gligorijević
- Center for Computational Biology (CCB), Flatiron Institute, Simons Foundation, New York, New York, USA
| | - Meet Barot
- Center for Data Science, New York University, New York, 10011, NY, USA
| | - Hai Fang
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Stefano Toppo
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | - Enrico Lavezzo
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | - Marco Falda
- Department of Biology, University of Padova, Padova, Italy
| | - Michele Berselli
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | - Silvio C E Tosatto
- CNR Institute of Neuroscience, Padova, Italy.,Department of Biomedical Sciences, University of Padua, Padova, Italy
| | - Marco Carraro
- Department of Biomedical Sciences, University of Padua, Padova, Italy
| | - Damiano Piovesan
- Department of Biomedical Sciences, University of Padua, Padova, Italy
| | - Hafeez Ur Rehman
- Department of Computer Science, National University of Computer and Emerging Sciences, Peshawar, Khyber Pakhtoonkhwa, Pakistan
| | - Qizhong Mao
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA.,University of California, Riverside, Philadelphia, PA, USA
| | - Shanshan Zhang
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
| | - Slobodan Vucetic
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
| | - Gage S Black
- Department of Biology, Brigham Young University, Provo, UT, USA.,Bioinformatics Research Group, Provo, UT, USA
| | - Dane Jo
- Department of Biology, Brigham Young University, Provo, UT, USA.,Bioinformatics Research Group, Provo, UT, USA
| | - Erica Suh
- Department of Biology, Brigham Young University, Provo, UT, USA
| | - Jonathan B Dayton
- Department of Biology, Brigham Young University, Provo, UT, USA.,Bioinformatics Research Group, Provo, UT, USA
| | - Dallas J Larsen
- Department of Biology, Brigham Young University, Provo, UT, USA.,Bioinformatics Research Group, Provo, UT, USA
| | - Ashton R Omdahl
- Department of Biology, Brigham Young University, Provo, UT, USA.,Bioinformatics Research Group, Provo, UT, USA
| | - Liam J McGuffin
- School of Biological Sciences, University of Reading, Reading, England, United Kingdom
| | | | - Patricia C Babbitt
- Department of Pharmaceutical Chemistry, San Francisco, CA, USA.,Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, 94158, CA, USA
| | - Jeffrey M Yunes
- UC Berkeley - UCSF Graduate Program in Bioengineering, University of California, San Francisco, 94158, CA, USA.,Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, 94158, CA, USA
| | - Paolo Fontana
- Research and Innovation Center, Edmund Mach Foundation, San Michele all'Adige, Italy
| | - Feng Zhang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, Shanghai, China.,Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai, Shanghai, China
| | - Shanfeng Zhu
- School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.,Institute of Science and Technology for Brain-Inspired Intelligence and Shanghai Institute of Artificial Intelligence Algorithms, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Ronghui You
- School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.,Institute of Science and Technology for Brain-Inspired Intelligence and Shanghai Institute of Artificial Intelligence Algorithms, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Zihan Zhang
- School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Suyang Dai
- School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Shuwei Yao
- School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.,Institute of Science and Technology for Brain-Inspired Intelligence and Shanghai Institute of Artificial Intelligence Algorithms, Fudan University, Shanghai, China
| | - Weidong Tian
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai, Shanghai, China.,Department of Pediatrics, Brain Tumor Center, Division of Experimental Hematology and Cancer Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA
| | - Caleb Chandler
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA
| | - Miguel Amezola
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA
| | - Devon Johnson
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA
| | - Jia-Ming Chang
- Department of Computer Science, National Chengchi University, Taipei, Taiwan
| | - Wen-Hung Liao
- Department of Computer Science, National Chengchi University, Taipei, Taiwan
| | - Yi-Wei Liu
- Department of Computer Science, National Chengchi University, Taipei, Taiwan
| | | | | | - Robert Hoehndorf
- Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Jeddah, Saudi Arabia
| | - Maxat Kulmanov
- Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Jeddah, Saudi Arabia
| | - Imane Boudellioua
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.,Computer, Electrical and Mathematical Sciences Engineering Division (CEMSE), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Gianfranco Politano
- Control and Computer Engineering Department, Politecnico di Torino, Torino, TO, Italy
| | - Stefano Di Carlo
- Control and Computer Engineering Department, Politecnico di Torino, Torino, TO, Italy
| | - Alfredo Benso
- Control and Computer Engineering Department, Politecnico di Torino, Torino, TO, Italy
| | - Kai Hakala
- Department of Future Technologies, Turku NLP Group, University of Turku, Turku, Finland.,University of Turku Graduate School (UTUGS), Turku, Finland
| | - Filip Ginter
- Department of Future Technologies, Turku NLP Group, University of Turku, Turku, Finland.,University of Turku, Turku, Finland
| | - Farrokh Mehryary
- Department of Future Technologies, Turku NLP Group, University of Turku, Turku, Finland.,University of Turku Graduate School (UTUGS), Turku, Finland
| | - Suwisa Kaewphan
- Department of Future Technologies, Turku NLP Group, University of Turku, Turku, Finland.,University of Turku Graduate School (UTUGS), Turku, Finland.,Turku Centre for Computer Science (TUCS), Turku, Finland
| | - Jari Björne
- Department of Future Technologies, Faculty of Science and Engineering, University of Turku, Turku, FI-20014, Finland.,Turku Centre for Computer Science (TUCS), Agora, Vesilinnantie 3, Turku, FI-20500, Finland
| | | | | | - Tapio Salakoski
- Department of Future Technologies, Faculty of Science and Engineering, University of Turku, Turku, FI-20014, Finland.,Turku Centre for Computer Science (TUCS), Agora, Vesilinnantie 3, Turku, FI-20500, Finland
| | - Daisuke Kihara
- Department of Biological Sciences, Department of Computer Science, Purdue University, 47907, IN, USA.,Department of Pediatrics, University of Cincinnati, Cincinnati, 45229, OH, USA
| | - Aashish Jain
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Tomislav Šmuc
- Division of Electronics, Rudjer Boskovic Institute, Zagreb, Croatia
| | - Adrian Altenhoff
- Department of Computer Science, ETH Zurich, Zurich, Switzerland.,SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Asa Ben-Hur
- Department of Computer Science, Colorado State University, Fort Collins, CO, USA
| | - Burkhard Rost
- Department of Informatics, Bioinformatics & Computational Biology-i12, Technische Universitat Munchen, Munich, Germany.,Institute for Food and Plant Sciences WZW, Technische Universität München, Freising, Germany
| | | | - Christine A Orengo
- Research Department of Structural and Molecular Biology, University College London, London, United Kingdom
| | - Constance J Jeffery
- Biological Sciences, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Giovanni Bosco
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Deborah A Hogan
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA.,Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Maria J Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, United Kingdom
| | - Claire O'Donovan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, United Kingdom
| | - Sean D Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Casey S Greene
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Childhood Cancer Data Lab, Alex's Lemonade Stand Foundation, Philadelphia, Pennsylvania, USA
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.
| | - Iddo Friedberg
- Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA, USA.
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Abstract
Motivation The rapid drop in sequencing costs has produced many more (predicted) protein sequences than can feasibly be functionally annotated with wet-lab experiments. Thus, many computational methods have been developed for this purpose. Most of these methods employ homology-based inference, approximated via sequence alignments, to transfer functional annotations between proteins. The increase in the number of available sequences, however, has drastically increased the search space, thus significantly slowing down alignment methods. Results Here we describe homology-derived functional similarity of proteins (HFSP), a novel computational method that uses results of a high-speed alignment algorithm, MMseqs2, to infer functional similarity of proteins on the basis of their alignment length and sequence identity. We show that our method is accurate (85% precision) and fast (more than 40-fold speed increase over state-of-the-art). HFSP can help correct at least a 16% error in legacy curations, even for a resource of as high quality as Swiss-Prot. These findings suggest HFSP as an ideal resource for large-scale functional annotation efforts. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yannick Mahlich
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA.,Computational Biology & Bioinformatics - i12 Informatics, Technical University of Munich (TUM), Munich, Germany.,Institute for Advanced Study, Technical University of Munich (TUM), Munich, Germany
| | - Martin Steinegger
- Computational Biology & Bioinformatics - i12 Informatics, Technical University of Munich (TUM), Munich, Germany.,Quantitative and Computational Biology Group, Max-Planck Institute for Biophysical Chemistry, Göttingen, Germany.,Department of Chemistry, Seoul National University, Seoul, Korea
| | - Burkhard Rost
- Computational Biology & Bioinformatics - i12 Informatics, Technical University of Munich (TUM), Munich, Germany.,Institute for Advanced Study, Technical University of Munich (TUM), Munich, Germany.,TUM School of Life Sciences Weihenstephan (WZW), Technical University Munich (TUM), Freising, Germany.,Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA.,New York Consortium on Membrane Protein Structure (NYCOMPS), New York, NY, USA
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA.,Institute for Advanced Study, Technical University of Munich (TUM), Munich, Germany.,Department of Genetics, Human Genetics Institute, Rutgers University, Piscataway, NJ, USA
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42
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Abstract
Motivation Many applications monitor predictions of a whole range of features for biological datasets, e.g. the fraction of secreted human proteins in the human proteome. Results and error estimates are typically derived from publications. Results Here, we present a simple, alternative approximation that uses performance estimates of methods to error-correct the predicted distributions. This approximation uses the confusion matrix (TP true positives, TN true negatives, FP false positives and FN false negatives) describing the performance of the prediction tool for correction. As proof-of-principle, the correction was applied to a two-class (membrane/not) and to a seven-class (localization) prediction. Availability and implementation Datasets and a simple JavaScript tool available freely for all users at http://www.rostlab.org/services/distributions. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Valérie Marot-Lassauzaie
- Department of Informatics, l12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Garching/Munich, Germany
| | - Michael Bernhofer
- Department of Informatics, l12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Garching/Munich, Germany
| | - Burkhard Rost
- Department of Informatics, l12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Garching/Munich, Germany
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43
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Schafferhans A, O'Donoghue SI, Heinzinger M, Rost B. Dark Proteins Important for Cellular Function. Proteomics 2019; 18:e1800227. [PMID: 30318701 DOI: 10.1002/pmic.201800227] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Revised: 09/14/2018] [Indexed: 01/08/2023]
Abstract
Despite substantial and successful projects for structural genomics, many proteins remain for which neither experimental structures nor homology-based models are known for any part of the amino acid sequence. These have been called "dark proteins," in contrast to non-dark proteins, in which at least part of the sequence has a known or inferred structure. It has been hypothesized that non-dark proteins may be more abundantly expressed than dark proteins, which are known to have much fewer sequence relatives. Surprisingly, the opposite has been observed: human dark and non-dark proteins had quite similar levels of expression, in terms of both mRNA and protein abundance. Such high levels of expression strongly indicate that dark proteins-as a group-are important for cellular function. This is remarkable, given how carefully structural biologists have focused on proteins crucial for function, and highlights the important challenge posed by dark proteins in future research.
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Affiliation(s)
- Andrea Schafferhans
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748 Garching, Germany.,Department of Bioengineering Sciences, University of Applied Sciences, Freising, Germany
| | - Seán I O'Donoghue
- CSIRO Data61, Sydney, Australia.,Division of Genomics & Epigenetics, Garvan Institute of Medical Research, Sydney, Australia.,School of Biotechnology & Biomolecular Sciences, University of New South Wales (UNSW), Sydney, NSW, Australia
| | - Michael Heinzinger
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748 Garching, Germany
| | - Burkhard Rost
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748 Garching, Germany.,Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748 Garching, Germany.,TUM School of Life Sciences Weihenstephan (WZW), Alte Akademie 8, Freising, Germany
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44
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Bernhofer M, Goldberg T, Wolf S, Ahmed M, Zaugg J, Boden M, Rost B. NLSdb-major update for database of nuclear localization signals and nuclear export signals. Nucleic Acids Res 2019; 46:D503-D508. [PMID: 29106588 PMCID: PMC5753228 DOI: 10.1093/nar/gkx1021] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 10/18/2017] [Indexed: 11/13/2022] Open
Abstract
NLSdb is a database collecting nuclear export signals (NES) and nuclear localization signals (NLS) along with experimentally annotated nuclear and non-nuclear proteins. NES and NLS are short sequence motifs related to protein transport out of and into the nucleus. The updated NLSdb now contains 2253 NLS and introduces 398 NES. The potential sets of novel NES and NLS have been generated by a simple 'in silico mutagenesis' protocol. We started with motifs annotated by experiments. In step 1, we increased specificity such that no known non-nuclear protein matched the refined motif. In step 2, we increased the sensitivity trying to match several different families with a motif. We then iterated over steps 1 and 2. The final set of 2253 NLS motifs matched 35% of 8421 experimentally verified nuclear proteins (up from 21% for the previous version) and none of 18 278 non-nuclear proteins. We updated the web interface providing multiple options to search protein sequences for NES and NLS motifs, and to evaluate your own signal sequences. NLSdb can be accessed via Rostlab services at: https://rostlab.org/services/nlsdb/.
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Affiliation(s)
- Michael Bernhofer
- Department of Informatics, I12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Boltzmannstrasse 3, 85748 Garching/Munich, Germany
| | - Tatyana Goldberg
- Department of Informatics, I12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Boltzmannstrasse 3, 85748 Garching/Munich, Germany
| | - Silvana Wolf
- Department of Informatics, I12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Boltzmannstrasse 3, 85748 Garching/Munich, Germany
| | - Mohamed Ahmed
- Department of Informatics, I12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Boltzmannstrasse 3, 85748 Garching/Munich, Germany
| | - Julian Zaugg
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane 4072, Australia
| | - Mikael Boden
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane 4072, Australia
| | - Burkhard Rost
- Department of Informatics, I12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Boltzmannstrasse 3, 85748 Garching/Munich, Germany.,Institute of Advanced Study (TUM-IAS), Lichtenbergstrasse 2a, 85748 Garching/Munich, Germany.,Institute for Food and Plant Sciences WZW-Weihenstephan, Alte Akademie 8, 85354 Freising, Germany.,Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA
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45
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Scheibenreif L, Littmann M, Orengo C, Rost B. FunFam protein families improve residue level molecular function prediction. BMC Bioinformatics 2019; 20:400. [PMID: 31319797 PMCID: PMC6639920 DOI: 10.1186/s12859-019-2988-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 07/09/2019] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND The CATH database provides a hierarchical classification of protein domain structures including a sub-classification of superfamilies into functional families (FunFams). We analyzed the similarity of binding site annotations in these FunFams and incorporated FunFams into the prediction of protein binding residues. RESULTS FunFam members agreed, on average, in 36.9 ± 0.6% of their binding residue annotations. This constituted a 6.7-fold increase over randomly grouped proteins and a 1.2-fold increase (1.1-fold on the same dataset) over proteins with the same enzymatic function (identical Enzyme Commission, EC, number). Mapping de novo binding residue prediction methods (BindPredict-CCS, BindPredict-CC) onto FunFam resulted in consensus predictions for those residues that were aligned and predicted alike (binding/non-binding) within a FunFam. This simple consensus increased the F1-score (for binding) 1.5-fold over the original prediction method. Variation of the threshold for how many proteins in the consensus prediction had to agree provided a convenient control of accuracy/precision and coverage/recall, e.g. reaching a precision as high as 60.8 ± 0.4% for a stringent threshold. CONCLUSIONS The FunFams outperformed even the carefully curated EC numbers in terms of agreement of binding site residues. Additionally, we assume that our proof-of-principle through the prediction of protein binding residues will be relevant for many other solutions profiting from FunFams to infer functional information at the residue level.
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Affiliation(s)
- Linus Scheibenreif
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany.
| | - Maria Littmann
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany.
| | - Christine Orengo
- Department of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Burkhard Rost
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany
- Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748, Garching/Munich, Germany
- TUM School of Life Sciences Weihenstephan (WZW), Alte Akademie 8, Freising, Germany
- Department of Biochemistry and Molecular Biophysics & New York Consortium on Membrane Protein Structure (NYCOMPS), Columbia University, 701 West, 168th Street, New York, NY 10032, USA
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46
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Peeken JC, Bernhofer M, Spraker MB, Pfeiffer D, Devecka M, Thamer A, Shouman MA, Ott A, Nüsslin F, Mayr NA, Rost B, Nyflot MJ, Combs SE. CT-based radiomic features predict tumor grading and have prognostic value in patients with soft tissue sarcomas treated with neoadjuvant radiation therapy. Radiother Oncol 2019; 135:187-196. [DOI: 10.1016/j.radonc.2019.01.004] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 12/19/2018] [Accepted: 01/05/2019] [Indexed: 01/01/2023]
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47
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Littmann M, Goldberg T, Seitz S, Bodén M, Rost B. Detailed prediction of protein sub-nuclear localization. BMC Bioinformatics 2019; 20:205. [PMID: 31014229 PMCID: PMC6480651 DOI: 10.1186/s12859-019-2790-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 04/02/2019] [Indexed: 12/21/2022] Open
Abstract
Background Sub-nuclear structures or locations are associated with various nuclear processes. Proteins localized in these substructures are important to understand the interior nuclear mechanisms. Despite advances in high-throughput methods, experimental protein annotations remain limited. Predictions of cellular compartments have become very accurate, largely at the expense of leaving out substructures inside the nucleus making a fine-grained analysis impossible. Results Here, we present a new method (LocNuclei) that predicts nuclear substructures from sequence alone. LocNuclei used a string-based Profile Kernel with Support Vector Machines (SVMs). It distinguishes sub-nuclear localization in 13 distinct substructures and distinguishes between nuclear proteins confined to the nucleus and those that are also native to other compartments (traveler proteins). High performance was achieved by implicitly leveraging a large biological knowledge-base in creating predictions by homology-based inference through BLAST. Using this approach, the performance reached AUC = 0.70–0.74 and Q13 = 59–65%. Travelling proteins (nucleus and other) were identified at Q2 = 70–74%. A Gene Ontology (GO) analysis of the enrichment of biological processes revealed that the predicted sub-nuclear compartments matched the expected functionality. Analysis of protein-protein interactions (PPI) show that formation of compartments and functionality of proteins in these compartments highly rely on interactions between proteins. This suggested that the LocNuclei predictions carry important information about function. The source code and data sets are available through GitHub: https://github.com/Rostlab/LocNuclei. Conclusions LocNuclei predicts subnuclear compartments and traveler proteins accurately. These predictions carry important information about functionality and PPIs. Electronic supplementary material The online version of this article (10.1186/s12859-019-2790-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Maria Littmann
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany.
| | - Tatyana Goldberg
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany
| | - Sebastian Seitz
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany
| | - Mikael Bodén
- School of Chemistry and Molecular Biosciences, UQ (University of Queensland), Cooper Rd, Brisbane City, QLD, 4072, Australia
| | - Burkhard Rost
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany.,Institute for Advanced Study (TUM-IAS), Lichtenbergstr 2a, 85748, Garching/Munich, Germany.,TUM School of Life Sciences Weihenstephan (WZW), Alte Akademie 8, Freising, Germany.,Department of Biochemistry and Molecular Biophysics & New York Consortium on Membrane Protein Structure (NYCOMPS), Columbia University, 701 West, 168th Street, New York, NY, 10032, USA
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48
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Peeken JC, Goldberg T, Pyka T, Bernhofer M, Wiestler B, Kessel KA, Tafti PD, Nüsslin F, Braun AE, Zimmer C, Rost B, Combs SE. Combining multimodal imaging and treatment features improves machine learning-based prognostic assessment in patients with glioblastoma multiforme. Cancer Med 2018; 8:128-136. [PMID: 30561851 PMCID: PMC6346243 DOI: 10.1002/cam4.1908] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 11/14/2018] [Accepted: 11/14/2018] [Indexed: 12/22/2022] Open
Abstract
Background For Glioblastoma (GBM), various prognostic nomograms have been proposed. This study aims to evaluate machine learning models to predict patients' overall survival (OS) and progression‐free survival (PFS) on the basis of clinical, pathological, semantic MRI‐based, and FET‐PET/CT‐derived information. Finally, the value of adding treatment features was evaluated. Methods One hundred and eighty‐nine patients were retrospectively analyzed. We assessed clinical, pathological, and treatment information. The VASARI set of semantic imaging features was determined on MRIs. Metabolic information was retained from preoperative FET‐PET/CT images. We generated multiple random survival forest prediction models on a patient training set and performed internal validation. Single feature class models were created including "clinical," "pathological," "MRI‐based," and "FET‐PET/CT‐based" models, as well as combinations. Treatment features were combined with all other features. Results Of all single feature class models, the MRI‐based model had the highest prediction performance on the validation set for OS (C‐index: 0.61 [95% confidence interval: 0.51‐0.72]) and PFS (C‐index: 0.61 [0.50‐0.72]). The combination of all features did increase performance above all single feature class models up to C‐indices of 0.70 (0.59‐0.84) and 0.68 (0.57‐0.78) for OS and PFS, respectively. Adding treatment information further increased prognostic performance up to C‐indices of 0.73 (0.62‐0.84) and 0.71 (0.60‐0.81) on the validation set for OS and PFS, respectively, allowing significant stratification of patient groups for OS. Conclusions MRI‐based features were the most relevant feature class for prognostic assessment. Combining clinical, pathological, and imaging information increased predictive power for OS and PFS. A further increase was achieved by adding treatment features.
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Affiliation(s)
- Jan C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar der Technischem Universität München (TUM), München, Germany.,Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany.,Department of Radiation Sciences (DRS), Institute of Innovative Radiotherapy (iRT), Helmholtz Zentrum München, Neuherberg, Germany
| | | | - Thomas Pyka
- Department of Nuclear Medicine, Klinikum rechts der Isar der Technischen Universität München (TUM), Munich, Germany
| | - Michael Bernhofer
- Department for Bioinformatics and Computational Biology, Technical University of Munich (TUM), Garching, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, Klinikum rechts der Isar der Technischen Universität, Munich (TUM), München, Germany
| | - Kerstin A Kessel
- Department of Radiation Oncology, Klinikum rechts der Isar der Technischem Universität München (TUM), München, Germany.,Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany.,Department of Radiation Sciences (DRS), Institute of Innovative Radiotherapy (iRT), Helmholtz Zentrum München, Neuherberg, Germany
| | | | - Fridtjof Nüsslin
- Department of Radiation Oncology, Klinikum rechts der Isar der Technischem Universität München (TUM), München, Germany
| | | | - Claus Zimmer
- Department of Neuroradiology, Klinikum rechts der Isar der Technischen Universität, Munich (TUM), München, Germany
| | - Burkhard Rost
- Department of Nuclear Medicine, Klinikum rechts der Isar der Technischen Universität München (TUM), Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar der Technischem Universität München (TUM), München, Germany.,Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany.,Department of Radiation Sciences (DRS), Institute of Innovative Radiotherapy (iRT), Helmholtz Zentrum München, Neuherberg, Germany
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Schelling M, Hopf TA, Rost B. Evolutionary couplings and sequence variation effect predict protein binding sites. Proteins 2018; 86:1064-1074. [DOI: 10.1002/prot.25585] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2017] [Revised: 06/14/2018] [Accepted: 07/04/2018] [Indexed: 01/16/2023]
Affiliation(s)
- Maria Schelling
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics, & Computational Biology - i12; Garching/Munich Germany
| | - Thomas A. Hopf
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics, & Computational Biology - i12; Garching/Munich Germany
- Department of Systems Biology & Department of Cell Biology; Harvard Medical School; Boston Massachusetts
| | - Burkhard Rost
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics, & Computational Biology - i12; Garching/Munich Germany
- Institute for Advanced Study (TUM-IAS); Garching/Munich Germany
- TUM School of Life Sciences Weihenstephan (WZW); Freising Germany
- Department of Biochemistry and Molecular Biophysics & New York Consortium on Membrane Protein Structure (NYCOMPS); Columbia University; New York New York
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Abstract
MOTIVATION Protein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods. RESULTS We extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, recent methods explicitly ignored such a network-structure and rely either on domain knowledge or sequence-only methods. Our approach is independent of domain-knowledge and leverages evolutionary information. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/. The data can also be downloaded from https://figshare.com/collections/ProfPPI-DB/4141784.
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Affiliation(s)
- Linh Tran
- Imperial College London (ICL), Department of Computing, United Kingdom
- Technical University of Munich (TUM), Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr, Germany
- * E-mail:
| | - Tobias Hamp
- Technical University of Munich (TUM), Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr, Germany
| | - Burkhard Rost
- Technical University of Munich (TUM), Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr, Germany
- Technical University of Munich (TUM), Institute for Advanced Study (TUM-IAS), Lichtenbergstr, Germany
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