1
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Rodrigues CHM, Portelli S, Ascher DB. Exploring the effects of missense mutations on protein thermodynamics through structure-based approaches: findings from the CAGI6 challenges. Hum Genet 2024:10.1007/s00439-023-02623-4. [PMID: 38227011 DOI: 10.1007/s00439-023-02623-4] [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: 06/15/2023] [Accepted: 11/18/2023] [Indexed: 01/17/2024]
Abstract
Missense mutations are known contributors to diverse genetic disorders, due to their subtle, single amino acid changes imparted on the resultant protein. Because of this, understanding the impact of these mutations on protein stability and function is crucial for unravelling disease mechanisms and developing targeted therapies. The Critical Assessment of Genome Interpretation (CAGI) provides a valuable platform for benchmarking state-of-the-art computational methods in predicting the impact of disease-related mutations on protein thermodynamics. Here we report the performance of our comprehensive platform of structure-based computational approaches to evaluate mutations impacting protein structure and function on 3 challenges from CAGI6: Calmodulin, MAPK1 and MAPK3. Our stability predictors have achieved correlations of up to 0.74 and AUCs of 1 when predicting changes in ΔΔG for MAPK1 and MAPK3, respectively, and AUC of up to 0.75 in the Calmodulin challenge. Overall, our study highlights the importance of structure-based approaches in understanding the effects of missense mutations on protein thermodynamics. The results obtained from the CAGI6 challenges contribute to the ongoing efforts to enhance our understanding of disease mechanisms and facilitate the development of personalised medicine approaches.
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Affiliation(s)
- Carlos H M Rodrigues
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, 4072, Australia
| | - Stephanie Portelli
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, 4072, Australia
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia.
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, 4072, Australia.
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2
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Rodrigues CHM, Ascher DB. CSM-Potential2: A comprehensive deep learning platform for the analysis of protein interacting interfaces. Proteins 2023. [PMID: 37870486 DOI: 10.1002/prot.26615] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 10/24/2023]
Abstract
Proteins are molecular machinery that participate in virtually all essential biological functions within the cell, which are tightly related to their 3D structure. The importance of understanding protein structure-function relationship is highlighted by the exponential growth of experimental structures, which has been greatly expanded by recent breakthroughs in protein structure prediction, most notably RosettaFold, and AlphaFold2. These advances have prompted the development of several computational approaches that leverage these data sources to explore potential biological interactions. However, most methods are generally limited to analysis of single types of interactions, such as protein-protein or protein-ligand interactions, and their complexity limits the usability to expert users. Here we report CSM-Potential2, a deep learning platform for the analysis of binding interfaces on protein structures. In addition to prediction of protein-protein interactions binding sites and classification of biological ligands, our new platform incorporates prediction of interactions with nucleic acids at the residue level and allows for ligand transplantation based on sequence and structure similarity to experimentally determined structures. We anticipate our platform to be a valuable resource that provides easy access to a range of state-of-the-art methods to expert and non-expert users for the study of biological interactions. Our tool is freely available as an easy-to-use web server and API available at https://biosig.lab.uq.edu.au/csm_potential.
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Affiliation(s)
- Carlos H M Rodrigues
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia
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3
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Nguyen TB, de Sá AGC, Rodrigues CHM, Pires DEV, Ascher DB. LEGO-CSM: a tool for functional characterisation of proteins. Bioinformatics 2023:btad402. [PMID: 37382560 DOI: 10.1093/bioinformatics/btad402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 02/22/2023] [Accepted: 06/27/2023] [Indexed: 06/30/2023]
Abstract
MOTIVATION With the development of sequencing techniques, the discovery of new proteins significantly exceeds the human capacity and resources for experimentally characterising protein functions. LEGO-CSM is a comprehensive web-based resource that fills this gap by leveraging the well-established and robust graph-based signatures to supervised learning models using both protein sequence and structure information to accurately model protein function in terms of Subcellular Localisation, Enzyme Commission (EC) numbers and Gene Ontology (GO) terms. RESULTS We show our models perform as well as or better than alternative approaches, achieving Area Under the Receiver Operating Characteristic Curve (ROC AUC) of up to 0.93 for subcellular localisation, up to 0.93 for EC and up to 0.81 for GO terms on independent blind tests. AVAILABILITY LEGO-CSM's web server is freely available at https://biosig.lab.uq.edu.au/lego_csm. In addition, all datasets used to train and test LEGO-CSM's models can be downloaded at https://biosig.lab.uq.edu.au/lego_csm/data. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Thanh Binh Nguyen
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane City, Queensland 4072, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria 3052, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia
| | - Alex G C de Sá
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane City, Queensland 4072, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria 3052, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Carlos H M Rodrigues
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane City, Queensland 4072, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria 3052, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia
| | - Douglas E V Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria 3052, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia
- School of Computing and Information Systems, University of Melbourne, Parkville, Victoria 3052, Australia
| | - David B Ascher
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane City, Queensland 4072, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria 3052, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria 3010, Australia
- School of Computing and Information Systems, University of Melbourne, Parkville, Victoria 3052, Australia
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4
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Zhou Y, Pan Q, Pires DEV, Rodrigues CHM, Ascher DB. DDMut: predicting effects of mutations on protein stability using deep learning. Nucleic Acids Res 2023:7191416. [PMID: 37283042 PMCID: PMC10320186 DOI: 10.1093/nar/gkad472] [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] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 05/11/2023] [Accepted: 05/18/2023] [Indexed: 06/08/2023] Open
Abstract
Understanding the effects of mutations on protein stability is crucial for variant interpretation and prioritisation, protein engineering, and biotechnology. Despite significant efforts, community assessments of predictive tools have highlighted ongoing limitations, including computational time, low predictive power, and biased predictions towards destabilising mutations. To fill this gap, we developed DDMut, a fast and accurate siamese network to predict changes in Gibbs Free Energy upon single and multiple point mutations, leveraging both forward and hypothetical reverse mutations to account for model anti-symmetry. Deep learning models were built by integrating graph-based representations of the localised 3D environment, with convolutional layers and transformer encoders. This combination better captured the distance patterns between atoms by extracting both short-range and long-range interactions. DDMut achieved Pearson's correlations of up to 0.70 (RMSE: 1.37 kcal/mol) on single point mutations, and 0.70 (RMSE: 1.84 kcal/mol) on double/triple mutants, outperforming most available methods across non-redundant blind test sets. Importantly, DDMut was highly scalable and demonstrated anti-symmetric performance on both destabilising and stabilising mutations. We believe DDMut will be a useful platform to better understand the functional consequences of mutations, and guide rational protein engineering. DDMut is freely available as a web server and API at https://biosig.lab.uq.edu.au/ddmut.
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Affiliation(s)
- Yunzhuo Zhou
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Qisheng Pan
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Douglas E V Pires
- School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - Carlos H M Rodrigues
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - David B Ascher
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
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5
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Williams NP, Rodrigues CHM, Truong J, Ascher DB, Holien JK. DockNet: high-throughput protein-protein interface contact prediction. Bioinformatics 2022; 39:6885444. [PMID: 36484688 PMCID: PMC9825772 DOI: 10.1093/bioinformatics/btac797] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 10/27/2022] [Accepted: 12/08/2022] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION Over 300 000 protein-protein interaction (PPI) pairs have been identified in the human proteome and targeting these is fast becoming the next frontier in drug design. Predicting PPI sites, however, is a challenging task that traditionally requires computationally expensive and time-consuming docking simulations. A major weakness of modern protein docking algorithms is the inability to account for protein flexibility, which ultimately leads to relatively poor results. RESULTS Here, we propose DockNet, an efficient Siamese graph-based neural network method which predicts contact residues between two interacting proteins. Unlike other methods that only utilize a protein's surface or treat the protein structure as a rigid body, DockNet incorporates the entire protein structure and places no limits on protein flexibility during an interaction. Predictions are modeled at the residue level, based on a diverse set of input node features including residue type, surface accessibility, residue depth, secondary structure, pharmacophore and torsional angles. DockNet is comparable to current state-of-the-art methods, achieving an area under the curve (AUC) value of up to 0.84 on an independent test set (DB5), can be applied to a variety of different protein structures and can be utilized in situations where accurate unbound protein structures cannot be obtained. AVAILABILITY AND IMPLEMENTATION DockNet is available at https://github.com/npwilliams09/docknet and an easy-to-use webserver at https://biosig.lab.uq.edu.au/docknet. All other data underlying this article are available in the article and in its online supplementary material. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Jia Truong
- STEM College, RMIT University, Melbourne, VIC, Australia
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia,School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia
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6
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Zhou Y, Al‐Jarf R, Alavi A, Nguyen TB, Rodrigues CHM, Pires DEV, Ascher DB. kinCSM: Using graph-based signatures to predict small molecule CDK2 inhibitors. Protein Sci 2022; 31:e4453. [PMID: 36305769 PMCID: PMC9597374 DOI: 10.1002/pro.4453] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [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: 07/31/2022] [Revised: 09/14/2022] [Accepted: 09/15/2022] [Indexed: 11/20/2022]
Abstract
Protein phosphorylation acts as an essential on/off switch in many cellular signaling pathways. This has led to ongoing interest in targeting kinases for therapeutic intervention. Computer‐aided drug discovery has been proven a useful and cost‐effective approach for facilitating prioritization and enrichment of screening libraries, but limited effort has been devoted providing insights on what makes a potent kinase inhibitor. To fill this gap, here we developed kinCSM, an integrative computational tool capable of accurately identifying potent cyclin‐dependent kinase 2 (CDK2) inhibitors, quantitatively predicting CDK2 ligand–kinase inhibition constants (pKi) and classifying different types of inhibitors based on their favorable binding modes. kinCSM predictive models were built using supervised learning and leveraged the concept of graph‐based signatures to capture both physicochemical properties and geometry properties of small molecules. CDK2 inhibitors were accurately identified with Matthew's Correlation Coefficients (MCC) of up to 0.74, and inhibition constants predicted with Pearson's correlation of up to 0.76, both with consistent performances of 0.66 and 0.68 on a nonredundant blind test, respectively. kinCSM was also able to identify the potential type of inhibition for a given molecule, achieving MCC of up to 0.80 on cross‐validation and 0.73 on the blind test. Analyzing the molecular composition of revealed enriched chemical fragments in CDK2 inhibitors and different types of inhibitors, which provides insights into the molecular mechanisms behind ligand–kinase interactions. kinCSM will be an invaluable tool to guide future kinase drug discovery. To aid the fast and accurate screening of CDK2 inhibitors, kinCSM is freely available at https://biosig.lab.uq.edu.au/kin_csm/.
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Affiliation(s)
- Yunzhuo Zhou
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia,Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Raghad Al‐Jarf
- Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Azadeh Alavi
- Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Thanh Binh Nguyen
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia,Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Carlos H. M. Rodrigues
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia,Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Douglas E. V. Pires
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia,Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia,School of Computing and Information SystemsUniversity of MelbourneMelbourneVictoriaAustralia
| | - David B. Ascher
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia,Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
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7
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Akdel M, Pires DEV, Pardo EP, Jänes J, Zalevsky AO, Mészáros B, Bryant P, Good LL, Laskowski RA, Pozzati G, Shenoy A, Zhu W, Kundrotas P, Serra VR, Rodrigues CHM, Dunham AS, Burke D, Borkakoti N, Velankar S, Frost A, Basquin J, Lindorff-Larsen K, Bateman A, Kajava AV, Valencia A, Ovchinnikov S, Durairaj J, Ascher DB, Thornton JM, Davey NE, Stein A, Elofsson A, Croll TI, Beltrao P. A structural biology community assessment of AlphaFold2 applications. Nat Struct Mol Biol 2022; 29:1056-1067. [PMID: 36344848 PMCID: PMC9663297 DOI: 10.1038/s41594-022-00849-w] [Citation(s) in RCA: 179] [Impact Index Per Article: 89.5] [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: 10/25/2021] [Accepted: 09/20/2022] [Indexed: 11/09/2022]
Abstract
Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods for protein structure predictions have reached the accuracy of experimentally determined models. Although this has been independently verified, the implementation of these methods across structural-biology applications remains to be tested. Here, we evaluate the use of AlphaFold2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modeling of interactions; and modeling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modeled when compared with homology modeling, identifying structural features rarely seen in the Protein Data Bank. AF2-based predictions of protein disorder and complexes surpass dedicated tools, and AF2 models can be used across diverse applications equally well compared with experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life-science research.
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Affiliation(s)
- Mehmet Akdel
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, the Netherlands
| | - Douglas E V Pires
- School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - Eduard Porta Pardo
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Jürgen Jänes
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Arthur O Zalevsky
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russian Federation
| | | | - Patrick Bryant
- Dep of Biochemistry and Biophysics and Science for Life Laboratory, Solna, Sweden
| | - Lydia L Good
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Roman A Laskowski
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Gabriele Pozzati
- Dep of Biochemistry and Biophysics and Science for Life Laboratory, Solna, Sweden
| | - Aditi Shenoy
- Dep of Biochemistry and Biophysics and Science for Life Laboratory, Solna, Sweden
| | - Wensi Zhu
- Dep of Biochemistry and Biophysics and Science for Life Laboratory, Solna, Sweden
| | - Petras Kundrotas
- Dep of Biochemistry and Biophysics and Science for Life Laboratory, Solna, Sweden
| | | | - Carlos H M Rodrigues
- School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - Alistair S Dunham
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - David Burke
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Neera Borkakoti
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Sameer Velankar
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Adam Frost
- Department of Biochemistry and Biophysics University of California, San Francisco, CA, USA
| | - Jérôme Basquin
- Department of Structural Cell Biology, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Alex Bateman
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Andrey V Kajava
- Université de Montpellier, Centre de Recherche en Biologie Cellulaire de Montpellier (CRBM) CNRS, Montpellier, France
| | | | - Sergey Ovchinnikov
- Faculty of Arts and Sciences, Division of Science, Harvard University, Cambridge, MA, USA.
| | | | - David B Ascher
- School of Chemistry and Molecular Biology, University of Queensland, Brisbane, Queensland, Australia.
| | - Janet M Thornton
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.
| | | | - Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| | - Arne Elofsson
- Dep of Biochemistry and Biophysics and Science for Life Laboratory, Solna, Sweden.
| | - Tristan I Croll
- Cambridge Institute for Medical Research, Department of Haematology, The University of Cambridge, Cambridge, UK.
| | - Pedro Beltrao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.
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8
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Rodrigues CHM, Garg A, Keizer D, Pires DEV, Ascher DB. CSM-peptides: A computational approach to rapid identification of therapeutic peptides. Protein Sci 2022; 31:e4442. [PMID: 36173168 PMCID: PMC9518225 DOI: 10.1002/pro.4442] [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: 07/17/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 11/25/2022]
Abstract
Peptides are attractive alternatives for the development of new therapeutic strategies due to their versatility and low complexity of synthesis. Increasing interest in these molecules has led to the creation of large collections of experimentally characterized therapeutic peptides, which greatly contributes to development of data‐driven computational approaches. Here we propose CSM‐peptides, a novel machine learning method for rapid identification of eight different types of therapeutic peptides: anti‐angiogenic, anti‐bacterial, anti‐cancer, anti‐inflammatory, anti‐viral, cell‐penetrating, quorum sensing, and surface binding. Our method has shown to outperform existing approaches, achieving an AUC of up to 0.92 on independent blind tests, and consistent performance on cross‐validation. We anticipate CSM‐peptides to be of great value in helping screening large libraries to identify novel peptides with therapeutic potential and have made it freely available as a user‐friendly web server and Application Programming Interface at https://biosig.lab.uq.edu.au/csm_peptides.
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Affiliation(s)
- Carlos H M Rodrigues
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Melbourne, Victoria, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, Queensland, Australia
| | - Anjali Garg
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Melbourne, Victoria, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - David Keizer
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Melbourne, Victoria, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Douglas E V Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - David B Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Melbourne, Victoria, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, Queensland, Australia
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9
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Rodrigues CHM, Pires DEV, Blundell TL, Ascher DB. Structural landscapes of PPI interfaces. Brief Bioinform 2022; 23:bbac165. [PMID: 35656714 PMCID: PMC9294409 DOI: 10.1093/bib/bbac165] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [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: 12/22/2021] [Revised: 03/10/2022] [Accepted: 04/13/2022] [Indexed: 02/07/2023] Open
Abstract
Proteins are capable of highly specific interactions and are responsible for a wide range of functions, making them attractive in the pursuit of new therapeutic options. Previous studies focusing on overall geometry of protein-protein interfaces, however, concluded that PPI interfaces were generally flat. More recently, this idea has been challenged by their structural and thermodynamic characterisation, suggesting the existence of concave binding sites that are closer in character to traditional small-molecule binding sites, rather than exhibiting complete flatness. Here, we present a large-scale analysis of binding geometry and physicochemical properties of all protein-protein interfaces available in the Protein Data Bank. In this review, we provide a comprehensive overview of the protein-protein interface landscape, including evidence that even for overall larger, more flat interfaces that utilize discontinuous interacting regions, small and potentially druggable pockets are utilized at binding sites.
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Affiliation(s)
- Carlos H M Rodrigues
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria
- School of Chemistry and Molecular Biosciences, Bio21 Institute, University of Queensland, Brisbane, Victoria
| | - Douglas E V Pires
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria
- School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria
| | - Tom L Blundell
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria
- School of Chemistry and Molecular Biosciences, Bio21 Institute, University of Queensland, Brisbane, Victoria
- Department of Biochemistry, University of Cambridge, Cambridge, UK
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10
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Rodrigues CHM, Ascher DB. CSM-Potential: mapping protein interactions and biological ligands in 3D space using geometric deep learning. Nucleic Acids Res 2022; 50:W204-W209. [PMID: 35609999 PMCID: PMC9252741 DOI: 10.1093/nar/gkac381] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [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: 03/24/2022] [Revised: 04/19/2022] [Accepted: 05/05/2022] [Indexed: 11/13/2022] Open
Abstract
Recent advances in protein structural modelling have enabled the accurate prediction of the holo 3D structures of almost any protein, however protein function is intrinsically linked to the interactions it makes. While a number of computational approaches have been proposed to explore potential biological interactions, they have been limited to specific interactions, and have not been readily accessible for non-experts or use in bioinformatics pipelines. Here we present CSM-Potential, a geometric deep learning approach to identify regions of a protein surface that are likely to mediate protein-protein and protein-ligand interactions in order to provide a link between 3D structure and biological function. Our method has shown robust performance, outperforming existing methods for both predictive tasks. By assessing the performance of CSM-Potential on independent blind tests, we show that our method was able to achieve ROC AUC values of up to 0.81 for the identification of potential protein-protein binding sites, and up to 0.96 accuracy on biological ligand classification. Our method is freely available as a user-friendly and easy-to-use web server and API at http://biosig.unimelb.edu.au/csm_potential.
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Affiliation(s)
- Carlos H M Rodrigues
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia
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11
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Robinson DRL, Hock DH, Muellner-Wong L, Kugapreethan R, Reljic B, Surgenor EE, Rodrigues CHM, Caruana NJ, Stroud DA. Applying Sodium Carbonate Extraction Mass Spectrometry to Investigate Defects in the Mitochondrial Respiratory Chain. Front Cell Dev Biol 2022; 10:786268. [PMID: 35300415 PMCID: PMC8921082 DOI: 10.3389/fcell.2022.786268] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [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: 09/30/2021] [Accepted: 02/03/2022] [Indexed: 12/03/2022] Open
Abstract
Mitochondria are complex organelles containing 13 proteins encoded by mitochondrial DNA and over 1,000 proteins encoded on nuclear DNA. Many mitochondrial proteins are associated with the inner or outer mitochondrial membranes, either peripherally or as integral membrane proteins, while others reside in either of the two soluble mitochondrial compartments, the mitochondrial matrix and the intermembrane space. The biogenesis of the five complexes of the oxidative phosphorylation system are exemplars of this complexity. These large multi-subunit complexes are comprised of more than 80 proteins with both membrane integral and peripheral associations and require soluble, membrane integral and peripherally associated assembly factor proteins for their biogenesis. Mutations causing human mitochondrial disease can lead to defective complex assembly due to the loss or altered function of the affected protein and subsequent destabilization of its interactors. Here we couple sodium carbonate extraction with quantitative mass spectrometry (SCE-MS) to track changes in the membrane association of the mitochondrial proteome across multiple human knockout cell lines. In addition to identifying the membrane association status of over 840 human mitochondrial proteins, we show how SCE-MS can be used to understand the impacts of defective complex assembly on protein solubility, giving insights into how specific subunits and sub-complexes become destabilized.
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Affiliation(s)
- David R L Robinson
- Department of Biochemistry and Pharmacology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, VIC, Australia
| | - Daniella H Hock
- Department of Biochemistry and Pharmacology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, VIC, Australia
| | - Linden Muellner-Wong
- Department of Biochemistry and Pharmacology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, VIC, Australia.,The Royal Children's Hospital, Murdoch Children's Research Institute, Parkville, VIC, Australia
| | - Roopasingam Kugapreethan
- Department of Biochemistry and Pharmacology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, VIC, Australia
| | - Boris Reljic
- Department of Biochemistry and Pharmacology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, VIC, Australia.,Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Elliot E Surgenor
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia.,The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
| | - Carlos H M Rodrigues
- Department of Biochemistry and Pharmacology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, VIC, Australia.,Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Nikeisha J Caruana
- Department of Biochemistry and Pharmacology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, VIC, Australia.,Institute for Health and Sport (IHES), Victoria University, Melbourne, VIC, Australia
| | - David A Stroud
- Department of Biochemistry and Pharmacology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, VIC, Australia.,The Royal Children's Hospital, Murdoch Children's Research Institute, Parkville, VIC, Australia
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12
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Abstract
Protein-protein interactions are promising sites for development of selective drugs; however, they have generally been viewed as challenging targets. Molecules targeting protein-protein interactions tend to be larger and more lipophilic than other drug-like molecules, mimicking the properties of interacting interfaces. Here, we propose a machine learning approach that uses a graph-based representation of small molecules to guide identification of inhibitors modulating protein-protein interactions, pdCSM-PPI. This approach was applied to 21 different PPI targets. We developed interaction-specific models that were able to accurately identify active compounds achieving MCC and F1 scores up to 1, and Pearson's correlations up to 0.87, outperforming previous approaches. Using insights from these individual models, we developed a generic protein-protein interaction modulator predictive model, which accurately predicted IC50 with a Pearson's correlation of 0.64 on a low redundancy blind test. Importantly, we were able to accurately identify active from inactive compounds, achieving an AUC of 0.77 and sensitivity and specificity of 76% and 78%, respectively. We believe pdCSM-PPI will be an important tool to help guide more efficient screening of new PPI inhibitors; it is freely available as an easy-to-use web server and API at http://biosig.unimelb.edu.au/pdcsm_ppi.
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Affiliation(s)
- Carlos H M Rodrigues
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane 4072, Australia
| | - Douglas E V Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane 4072, Australia.,School of Computing and Information Systems, University of Melbourne, Parkville 3052, Victoria, Australia
| | - David B Ascher
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane 4072, Australia
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13
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Silk M, Pires DEV, Rodrigues CHM, D'Souza EN, Olshansky M, Thorne N, Ascher DB. MTR3D: identifying regions within protein tertiary structures under purifying selection. Nucleic Acids Res 2021; 49:W438-W445. [PMID: 34050760 PMCID: PMC8265191 DOI: 10.1093/nar/gkab428] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 04/23/2021] [Accepted: 05/19/2021] [Indexed: 01/08/2023] Open
Abstract
The identification of disease-causal variants is non-trivial. By mapping population variation from over 448,000 exome and genome sequences to over 81,000 experimental structures and homology models of the human proteome, we have calculated both regional intolerance to missense variation (Missense Tolerance Ratio, MTR), using a sliding window of 21–41 codons, and introduce a new 3D spatial intolerance to missense variation score (3D Missense Tolerance Ratio, MTR3D), using spheres of 5–8 Å. We show that the MTR3D is less biased by regions with limited data and more accurately identifies regions under purifying selection than estimates relying on the sequence alone. Intolerant regions were highly enriched for both ClinVar pathogenic and COSMIC somatic missense variants (Mann–Whitney U test P < 2.2 × 10−16). Further, we combine sequence- and spatial-based scores to generate a consensus score, MTRX, which distinguishes pathogenic from benign variants more accurately than either score separately (AUC = 0.85). The MTR3D server enables easy visualisation of population variation, MTR, MTR3D and MTRX scores across the entire gene and protein structure for >17,000 human genes and >42,000 alternative alternate transcripts, including both Ensembl and RefSeq transcripts. MTR3D is freely available by user-friendly web-interface and API at http://biosig.unimelb.edu.au/mtr3d/.
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Affiliation(s)
- Michael Silk
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Australia.,Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Melbourne, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Australia
| | - Douglas E V Pires
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Australia.,Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Melbourne, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Australia.,School of Computing and Information Systems, University of Melbourne, Melbourne, Australia
| | - Carlos H M Rodrigues
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Australia.,Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Melbourne, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Australia
| | - Elston N D'Souza
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Australia.,Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Melbourne, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Australia
| | - Moshe Olshansky
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Australia
| | - Natalie Thorne
- Melbourne Genomics Health Alliance, Melbourne, Australia
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Australia.,Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Melbourne, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Australia.,Department of Biochemistry, University of Cambridge, Cambridge, UK
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14
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Rodrigues CHM, Pires DEV, Ascher DB. mmCSM-PPI: predicting the effects of multiple point mutations on protein-protein interactions. Nucleic Acids Res 2021; 49:W417-W424. [PMID: 33893812 PMCID: PMC8262703 DOI: 10.1093/nar/gkab273] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [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: 01/27/2021] [Revised: 03/18/2021] [Accepted: 04/15/2021] [Indexed: 11/16/2022] Open
Abstract
Protein-protein interactions play a crucial role in all cellular functions and biological processes and mutations leading to their disruption are enriched in many diseases. While a number of computational methods to assess the effects of variants on protein-protein binding affinity have been proposed, they are in general limited to the analysis of single point mutations and have been shown to perform poorly on independent test sets. Here, we present mmCSM-PPI, a scalable and effective machine learning model for accurately assessing changes in protein-protein binding affinity caused by single and multiple missense mutations. We expanded our well-established graph-based signatures in order to capture physicochemical and geometrical properties of multiple wild-type residue environments and integrated them with substitution scores and dynamics terms from normal mode analysis. mmCSM-PPI was able to achieve a Pearson's correlation of up to 0.75 (RMSE = 1.64 kcal/mol) under 10-fold cross-validation and 0.70 (RMSE = 2.06 kcal/mol) on a non-redundant blind test, outperforming existing methods. Our method is freely available as a user-friendly and easy-to-use web server and API at http://biosig.unimelb.edu.au/mmcsm_ppi.
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Affiliation(s)
- Carlos H M Rodrigues
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, Victoria, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Douglas E V Pires
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, Victoria, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, Victoria, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Department of Biochemistry, University of Cambridge, Cambridge, UK
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15
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Potter EL, Rodrigues CHM, Ascher DB, Abhayaratna WP, Sengupta PP, Marwick TH. Machine Learning of ECG Waveforms to Improve Selection for Testing for Asymptomatic Left Ventricular Dysfunction Prompt. JACC Cardiovasc Imaging 2021; 14:1904-1915. [PMID: 34147443 DOI: 10.1016/j.jcmg.2021.04.020] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 03/24/2021] [Accepted: 04/08/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVES To identify whether machine learning from processing of continuous wave transforms (CWTs) to provide an "energy waveform" electrocardiogram (ewECG) could be integrated with echocardiographic assessment of subclinical systolic and diastolic left ventricular dysfunction (LVD). BACKGROUND Asymptomatic LVD has management implications, but routine echocardiography is not undertaken in subjects at risk of heart failure. Signal processing of the surface ECG with the use of CWT can identify abnormal myocardial relaxation. METHODS EwECG and echocardiography were undertaken in 398 participants at risk of heart failure (HF). Reduced global longitudinal strain (GLS ≤16%)), diastolic abnormalities (E/e' >15, left atrial enlargement with E/e' >10 or impaired relaxation) or LV hypertrophy defined LVD. EwECG feature selection and supervised machine-learning by random forest (RF) classifier was undertaken with 643 CWT-derived features and the Atherosclerosis Risk in Communities (ARIC) heart failure risk score. RESULTS The ARIC score and 18 CWT features were selected to build a RF predictive model for LVD in a training dataset (n = 287; 60% female, median age 71 [interquartile range: 68 to 74] years). Model performance was tested in an independent group (n = 111; 49% female, median age 61 years [59 to 66 years]), demonstrating 85% sensitivity and 72% specificity (area under the receiver-operating characteristic curve [AUC]: 0.83; 95% confidence interval [CI]: 0.74 to 0.92). With ARIC score removed, sensitivity was 88% and specificity, 70% (AUC: 0.78; 95% CI: 0.70 to 0.86). RF models for reduced GLS and diastolic abnormalities including similar features had sensitivities that were unsuitable for screening. Conventional candidates for LVD screening (ARIC score, N-terminal pro-B-type natriuretic peptide, and standard automated ECG analysis) had inferior discriminative ability. Integration of ewECG in screening of people at risk of HF would reduce need for echocardiography by 45% while missing 12% of LVD cases. CONCLUSIONS Machine learning applied to ewECG is a sensitive screening test for LVD, and its integration into screening of patients at risk for HF would reduce the number of echocardiograms by almost one-half.
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Affiliation(s)
- Elizabeth L Potter
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Carlos H M Rodrigues
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Biomedical Sciences, Melbourne University, Melbourne, Victoria, Australia
| | - David B Ascher
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Biomedical Sciences, Melbourne University, Melbourne, Victoria, Australia
| | - Walter P Abhayaratna
- Australian National University Medical School, Australian National University, Canberra, Australian Capital Territory, Australia; Division of Medicine, Canberra Hospital, Canberra, Australian Capital Territory, Australia
| | - Partho P Sengupta
- West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA
| | - Thomas H Marwick
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
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16
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Pires DEV, Veloso WNP, Myung Y, Rodrigues CHM, Silk M, Rezende PM, Silva F, Xavier JS, Velloso JPL, da Silveira CH, Ascher DB. EasyVS: a user-friendly web-based tool for molecule library selection and structure-based virtual screening. Bioinformatics 2021; 36:4200-4202. [PMID: 32399551 DOI: 10.1093/bioinformatics/btaa480] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Revised: 04/01/2020] [Accepted: 05/05/2020] [Indexed: 11/14/2022] Open
Abstract
SUMMARY EasyVS is a web-based platform built to simplify molecule library selection and virtual screening. With an intuitive interface, the tool allows users to go from selecting a protein target with a known structure and tailoring a purchasable molecule library to performing and visualizing docking in a few clicks. Our system also allows users to filter screening libraries based on molecule properties, cluster molecules by similarity and personalize docking parameters. AVAILABILITY AND IMPLEMENTATION EasyVS is freely available as an easy-to-use web interface at http://biosig.unimelb.edu.au/easyvs. CONTACT douglas.pires@unimelb.edu.au or david.ascher@unimelb.edu.au. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Douglas E V Pires
- School of Computing and Information Systems, University of Melbourne, Melbourne 3010, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Australia.,Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne 3010, Australia
| | - Wandré N P Veloso
- Institute of Technological Sciences, Universidade Federal de Itajubá, Itabira 35903-087, Brazil
| | - YooChan Myung
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Australia.,Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne 3010, Australia
| | - Carlos H M Rodrigues
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Australia.,Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne 3010, Australia
| | - Michael Silk
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Australia.,Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne 3010, Australia
| | - Pâmela M Rezende
- Instituto René Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-002, Brazil
| | - Francislon Silva
- Instituto René Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-002, Brazil
| | - Joicymara S Xavier
- Instituto René Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-002, Brazil.,Instituto de Ciências Agrárias, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Unaí 38610-000, Brazil
| | - João P L Velloso
- Instituto René Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-002, Brazil
| | - Carlos H da Silveira
- Institute of Technological Sciences, Universidade Federal de Itajubá, Itabira 35903-087, Brazil
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Australia.,Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne 3010, Australia.,Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK
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17
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Portelli S, Olshansky M, Rodrigues CHM, D'Souza EN, Myung Y, Silk M, Alavi A, Pires DEV, Ascher DB. Author Correction: Exploring the structural distribution of genetic variation in SARS-CoV-2 with the COVID-3D online resource. Nat Genet 2021; 53:254. [PMID: 33398199 PMCID: PMC7781176 DOI: 10.1038/s41588-020-00775-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Stephanie Portelli
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Moshe Olshansky
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Carlos H M Rodrigues
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Elston N D'Souza
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Yoochan Myung
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Michael Silk
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Azadeh Alavi
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Douglas E V Pires
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - David B Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia. .,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia. .,Department of Biochemistry, University of Cambridge, Cambridge, UK.
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18
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Portelli S, Olshansky M, Rodrigues CHM, D'Souza EN, Myung Y, Silk M, Alavi A, Pires DEV, Ascher DB. Exploring the structural distribution of genetic variation in SARS-CoV-2 with the COVID-3D online resource. Nat Genet 2020; 52:999-1001. [PMID: 32908256 DOI: 10.1038/s41588-020-0693-3] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Stephanie Portelli
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Moshe Olshansky
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Carlos H M Rodrigues
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Elston N D'Souza
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Yoochan Myung
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Michael Silk
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Azadeh Alavi
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Douglas E V Pires
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - David B Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia. .,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia. .,Department of Biochemistry, University of Cambridge, Cambridge, UK.
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19
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Pires DEV, Rodrigues CHM, Ascher DB. mCSM-membrane: predicting the effects of mutations on transmembrane proteins. Nucleic Acids Res 2020; 48:W147-W153. [PMID: 32469063 PMCID: PMC7319563 DOI: 10.1093/nar/gkaa416] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [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: 02/21/2020] [Revised: 05/04/2020] [Accepted: 05/28/2020] [Indexed: 12/17/2022] Open
Abstract
Significant efforts have been invested into understanding and predicting the molecular consequences of mutations in protein coding regions, however nearly all approaches have been developed using globular, soluble proteins. These methods have been shown to poorly translate to studying the effects of mutations in membrane proteins. To fill this gap, here we report, mCSM-membrane, a user-friendly web server that can be used to analyse the impacts of mutations on membrane protein stability and the likelihood of them being disease associated. mCSM-membrane derives from our well-established mutation modelling approach that uses graph-based signatures to model protein geometry and physicochemical properties for supervised learning. Our stability predictor achieved correlations of up to 0.72 and 0.67 (on cross validation and blind tests, respectively), while our pathogenicity predictor achieved a Matthew's Correlation Coefficient (MCC) of up to 0.77 and 0.73, outperforming previously described methods in both predicting changes in stability and in identifying pathogenic variants. mCSM-membrane will be an invaluable and dedicated resource for investigating the effects of single-point mutations on membrane proteins through a freely available, user friendly web server at http://biosig.unimelb.edu.au/mcsm_membrane.
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Affiliation(s)
- Douglas E V Pires
- Computational Biology and Clinical Informatics, Baker Institute, Melbourne, Victoria 3004, Australia.,Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC, 3052, Australia.,School of Computing and Information Systems, University of Melbourne, Parkville, VIC, 3052, Australia
| | - Carlos H M Rodrigues
- Computational Biology and Clinical Informatics, Baker Institute, Melbourne, Victoria 3004, Australia.,Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC, 3052, Australia
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Institute, Melbourne, Victoria 3004, Australia.,Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC, 3052, Australia.,Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK
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Myung Y, Rodrigues CHM, Ascher DB, Pires DEV. mCSM-AB2: guiding rational antibody design using graph-based signatures. Bioinformatics 2020; 36:1453-1459. [PMID: 31665262 DOI: 10.1093/bioinformatics/btz779] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 10/07/2019] [Accepted: 10/23/2019] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION A lack of accurate computational tools to guide rational mutagenesis has made affinity maturation a recurrent challenge in antibody (Ab) development. We previously showed that graph-based signatures can be used to predict the effects of mutations on Ab binding affinity. RESULTS Here we present an updated and refined version of this approach, mCSM-AB2, capable of accurately modelling the effects of mutations on Ab-antigen binding affinity, through the inclusion of evolutionary and energetic terms. Using a new and expanded database of over 1800 mutations with experimental binding measurements and structural information, mCSM-AB2 achieved a Pearson's correlation of 0.73 and 0.77 across training and blind tests, respectively, outperforming available methods currently used for rational Ab engineering. AVAILABILITY AND IMPLEMENTATION mCSM-AB2 is available as a user-friendly and freely accessible web server providing rapid analysis of both individual mutations or the entire binding interface to guide rational antibody affinity maturation at http://biosig.unimelb.edu.au/mcsm_ab2. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yoochan Myung
- Department of Biochemistry and Molecular Biology.,ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, VIC 3010, Australia.,Structural Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
| | - Carlos H M Rodrigues
- Department of Biochemistry and Molecular Biology.,ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, VIC 3010, Australia.,Structural Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
| | - David B Ascher
- Department of Biochemistry and Molecular Biology.,ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, VIC 3010, Australia.,Structural Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia.,Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK
| | - Douglas E V Pires
- Department of Biochemistry and Molecular Biology.,ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, VIC 3010, Australia.,Structural Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia.,School of Computing and Information Systems, University of Melbourne, Melbourne, VIC 3010, Australia
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Rodrigues CHM, Pires DEV, Ascher DB. DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations. Protein Sci 2020; 30:60-69. [PMID: 32881105 PMCID: PMC7737773 DOI: 10.1002/pro.3942] [Citation(s) in RCA: 190] [Impact Index Per Article: 47.5] [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: 06/30/2020] [Revised: 08/27/2020] [Accepted: 08/28/2020] [Indexed: 12/11/2022]
Abstract
Predicting the effect of missense variations on protein stability and dynamics is important for understanding their role in diseases, and the link between protein structure and function. Approaches to estimate these changes have been proposed, but most only consider single‐point missense variants and a static state of the protein, with those that incorporate dynamics are computationally expensive. Here we present DynaMut2, a web server that combines Normal Mode Analysis (NMA) methods to capture protein motion and our graph‐based signatures to represent the wildtype environment to investigate the effects of single and multiple point mutations on protein stability and dynamics. DynaMut2 was able to accurately predict the effects of missense mutations on protein stability, achieving Pearson's correlation of up to 0.72 (RMSE: 1.02 kcal/mol) on a single point and 0.64 (RMSE: 1.80 kcal/mol) on multiple‐point missense mutations across 10‐fold cross‐validation and independent blind tests. For single‐point mutations, DynaMut2 achieved comparable performance with other methods when predicting variations in Gibbs Free Energy (ΔΔG) and in melting temperature (ΔTm). We anticipate our tool to be a valuable suite for the study of protein flexibility analysis and the study of the role of variants in disease. DynaMut2 is freely available as a web server and API at http://biosig.unimelb.edu.au/dynamut2.
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Affiliation(s)
- Carlos H M Rodrigues
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Douglas E V Pires
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - David B Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,Department of Biochemistry, University of Cambridge, Cambridge, UK
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22
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Karmakar M, Rodrigues CHM, Horan K, Denholm JT, Ascher DB. Structure guided prediction of Pyrazinamide resistance mutations in pncA. Sci Rep 2020; 10:1875. [PMID: 32024884 PMCID: PMC7002382 DOI: 10.1038/s41598-020-58635-x] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [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: 07/11/2019] [Accepted: 11/28/2019] [Indexed: 11/29/2022] Open
Abstract
Pyrazinamide plays an important role in tuberculosis treatment; however, its use is complicated by side-effects and challenges with reliable drug susceptibility testing. Resistance to pyrazinamide is largely driven by mutations in pyrazinamidase (pncA), responsible for drug activation, but genetic heterogeneity has hindered development of a molecular diagnostic test. We proposed to use information on how variants were likely to affect the 3D structure of pncA to identify variants likely to lead to pyrazinamide resistance. We curated 610 pncA mutations with high confidence experimental and clinical information on pyrazinamide susceptibility. The molecular consequences of each mutation on protein stability, conformation, and interactions were computationally assessed using our comprehensive suite of graph-based signature methods, mCSM. The molecular consequences of the variants were used to train a classifier with an accuracy of 80%. Our model was tested against internationally curated clinical datasets, achieving up to 85% accuracy. Screening of 600 Victorian clinical isolates identified a set of previously unreported variants, which our model had a 71% agreement with drug susceptibility testing. Here, we have shown the 3D structure of pncA can be used to accurately identify pyrazinamide resistance mutations. SUSPECT-PZA is freely available at: http://biosig.unimelb.edu.au/suspect_pza/.
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Affiliation(s)
- Malancha Karmakar
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Victorian Tuberculosis Program, Melbourne Health and Department of Microbiology and Immunology, University of Melbourne, Melbourne, Victoria, Australia
| | - Carlos H M Rodrigues
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Kristy Horan
- Microbiological Diagnostic Unit Public Health Laboratory, University of Melbourne at The Peter Doherty Institute for Infection &Immunity, Melbourne, Victoria, Australia
| | - Justin T Denholm
- Victorian Tuberculosis Program, Melbourne Health and Department of Microbiology and Immunology, University of Melbourne, Melbourne, Victoria, Australia
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK.
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Vedithi SC, Rodrigues CHM, Portelli S, Skwark MJ, Das M, Ascher DB, Blundell TL, Malhotra S. Computational saturation mutagenesis to predict structural consequences of systematic mutations in the beta subunit of RNA polymerase in Mycobacterium leprae. Comput Struct Biotechnol J 2020; 18:271-286. [PMID: 32042379 PMCID: PMC7000446 DOI: 10.1016/j.csbj.2020.01.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [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: 07/18/2019] [Revised: 01/03/2020] [Accepted: 01/07/2020] [Indexed: 11/26/2022] Open
Abstract
Rifampin resistance in leprosy may remain undetected due to the lack of rapid and effective diagnostic tools. A quick and reliable method is essential to determine the impacts of emerging detrimental mutations in the drug targets. The functional consequences of missense mutations in the β-subunit of RNA polymerase (RNAP) in Mycobacterium leprae (M. leprae) contribute to phenotypic resistance to rifampin in leprosy. Here, we report in-silico saturation mutagenesis of all residues in the β-subunit of RNAP to all other 19 amino acid types (generating 21,394 mutations for 1126 residues) and predict their impacts on overall thermodynamic stability, on interactions at subunit interfaces, and on β-subunit-RNA and rifampin affinities (only for the rifampin binding site) using state-of-the-art structure, sequence and normal mode analysis-based methods. Mutations in the conserved residues that line the active-site cleft show largely destabilizing effects, resulting in increased relative solvent accessibility and a concomitant decrease in residue-depth (the extent to which a residue is buried in the protein structure space) of the mutant residues. The mutations at residue positions S437, G459, H451, P489, K884 and H1035 are identified as extremely detrimental as they induce highly destabilizing effects on the overall protein stability, and nucleic acid and rifampin affinities. Destabilizing effects were predicted for all the clinically/experimentally identified rifampin-resistant mutations in M. leprae indicating that this model can be used as a surveillance tool to monitor emerging detrimental mutations that destabilise RNAP-rifampin interactions and confer rifampin resistance in leprosy. Author summary The emergence of primary and secondary drug resistance to rifampin in leprosy is a growing concern and poses a threat to the leprosy control and elimination measures globally. In the absence of an effective in-vitro system to detect and monitor phenotypic resistance to rifampin in leprosy, diagnosis mainly relies on the presence of mutations in drug resistance determining regions of the rpoB gene that encodes the β-subunit of RNAP in M. leprae. Few labs in the world perform mouse food pad propagation of M. leprae in the presence of drugs (rifampin) to determine growth patterns and confirm resistance, however the duration of these methods lasts from 8 to 12 months making them impractical for diagnosis. Understanding molecular mechanisms of drug resistance is vital to associating mutations to clinically detected drug resistance in leprosy. Here we propose an in-silico saturation mutagenesis approach to comprehensively elucidate the structural implications of any mutations that exist or that can arise in the β-subunit of RNAP in M. leprae. Most of the predicted mutations may not occur in M. leprae due to fitness costs but the information thus generated by this approach help decipher the impacts of mutations across the structure and conversely enable identification of stable regions in the protein that are least impacted by mutations (mutation coolspots) which can be a potential choice for small molecule binding and structure guided drug discovery.
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Affiliation(s)
| | - Carlos H M Rodrigues
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC 3052, Australia.,Structural Biology and Bioinformatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
| | - Stephanie Portelli
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC 3052, Australia.,Structural Biology and Bioinformatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
| | - Marcin J Skwark
- Department of Biochemistry, University of Cambridge, Tennis Court Rd., CB2 1GA, UK
| | - Madhusmita Das
- Molecular Biology Laboratory, Schieffelin Institute of Heath-Research and Leprosy Center, Karigiri, Vellore, Tamil Nadu 632106, India
| | - David B Ascher
- Department of Biochemistry, University of Cambridge, Tennis Court Rd., CB2 1GA, UK.,Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC 3052, Australia.,Structural Biology and Bioinformatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
| | - Tom L Blundell
- Department of Biochemistry, University of Cambridge, Tennis Court Rd., CB2 1GA, UK
| | - Sony Malhotra
- Department of Biochemistry, University of Cambridge, Tennis Court Rd., CB2 1GA, UK
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Rodrigues CHM, Myung Y, Pires DEV, Ascher DB. mCSM-PPI2: predicting the effects of mutations on protein-protein interactions. Nucleic Acids Res 2019; 47:W338-W344. [PMID: 31114883 PMCID: PMC6602427 DOI: 10.1093/nar/gkz383] [Citation(s) in RCA: 192] [Impact Index Per Article: 38.4] [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: 02/11/2019] [Revised: 04/30/2019] [Accepted: 05/20/2019] [Indexed: 12/13/2022] Open
Abstract
Protein-protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predict the effects of missense mutations on protein-protein interaction binding affinity. mCSM-PPI2 uses graph-based structural signatures to model effects of variations on the inter-residue interaction network, evolutionary information, complex network metrics and energetic terms to generate an optimised predictor. We demonstrate that our method outperforms previous methods, ranking first among 26 others on CAPRI blind tests. mCSM-PPI2 is freely available as a user friendly webserver at http://biosig.unimelb.edu.au/mcsm_ppi2/.
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Affiliation(s)
- Carlos H M Rodrigues
- Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Australia
- ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, Australia
- Structural Biology and Bioinformatics, Baker Heart and Diabetes Institute, Melbourne, Australia
| | - Yoochan Myung
- Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Australia
- ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, Australia
- Structural Biology and Bioinformatics, Baker Heart and Diabetes Institute, Melbourne, Australia
| | - Douglas E V Pires
- Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Australia
- ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, Australia
- Structural Biology and Bioinformatics, Baker Heart and Diabetes Institute, Melbourne, Australia
| | - David B Ascher
- Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Australia
- ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, Australia
- Structural Biology and Bioinformatics, Baker Heart and Diabetes Institute, Melbourne, Australia
- Department of Biochemistry, University of Cambridge, Cambridge, UK
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25
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Pires DEV, Rodrigues CHM, Albanaz ATS, Karmakar M, Myung Y, Xavier J, Michanetzi EM, Portelli S, Ascher DB. Exploring Protein Supersecondary Structure Through Changes in Protein Folding, Stability, and Flexibility. Methods Mol Biol 2019; 1958:173-185. [PMID: 30945219 DOI: 10.1007/978-1-4939-9161-7_9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [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: 12/29/2022]
Abstract
The ability to predict how mutations affect protein structure, folding, and flexibility can elucidate the molecular mechanisms leading to disruption of supersecondary structures, the emergence of phenotypes, as well guiding rational protein engineering. The advent of fast and accurate computational tools has enabled us to comprehensively explore the landscape of mutation effects on protein structures, prioritizing mutations for rational experimental validation.Here we describe the use of two complementary web-based in silico methods, DUET and DynaMut, developed to infer the effects of mutations on folding, stability, and flexibility and how they can be used to explore and interpret these effects on protein supersecondary structures.
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Affiliation(s)
- Douglas E V Pires
- Instituto René Rachou, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil. .,Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia.
| | - Carlos H M Rodrigues
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
| | | | - Malancha Karmakar
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
| | - Yoochan Myung
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
| | - Joicymara Xavier
- Instituto René Rachou, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Eleni-Maria Michanetzi
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
| | - Stephanie Portelli
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
| | - David B Ascher
- Instituto René Rachou, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.,Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia.,Department of Biochemistry, University of Cambridge, Cambridge, UK
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Albanaz ATS, Rodrigues CHM, Pires DEV, Ascher DB. Combating mutations in genetic disease and drug resistance: understanding molecular mechanisms to guide drug design. Expert Opin Drug Discov 2017; 12:553-563. [PMID: 28490289 DOI: 10.1080/17460441.2017.1322579] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [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: 12/22/2022]
Abstract
INTRODUCTION Mutations introduce diversity into genomes, leading to selective changes and driving evolution. These changes have contributed to the emergence of many of the current major health concerns of the 21st century, from the development of genetic diseases and cancers to the rise and spread of drug resistance. The experimental systematic testing of all mutations in a system of interest is impractical and not cost-effective, which has created interest in the development of computational tools to understand the molecular consequences of mutations to aid and guide rational experimentation. Areas covered: Here, the authors discuss the recent development of computational methods to understand the effects of coding mutations to protein function and interactions, particularly in the context of the 3D structure of the protein. Expert opinion: While significant progress has been made in terms of innovative tools to understand and quantify the different range of effects in which a mutation or a set of mutations can give rise to a phenotype, a great gap still exists when integrating these predictions and drawing causality conclusions linking variants. This often requires a detailed understanding of the system being perturbed. However, as part of the drug development process it can be used preemptively in a similar fashion to pharmacokinetics predictions, to guide development of therapeutics to help guide the design and analysis of clinical trials, patient treatment and public health policy strategies.
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Affiliation(s)
- Amanda T S Albanaz
- a Centro de Pesquisas René Rachou, FIOCRUZ , Belo Horizonte , MG , Brazil.,b Department of Biochemistry and Immunology , Universidade Federal de Minas Gerais , Belo Horizonte , Minas Gerais , Brazil
| | - Carlos H M Rodrigues
- a Centro de Pesquisas René Rachou, FIOCRUZ , Belo Horizonte , MG , Brazil.,b Department of Biochemistry and Immunology , Universidade Federal de Minas Gerais , Belo Horizonte , Minas Gerais , Brazil
| | - Douglas E V Pires
- a Centro de Pesquisas René Rachou, FIOCRUZ , Belo Horizonte , MG , Brazil
| | - David B Ascher
- a Centro de Pesquisas René Rachou, FIOCRUZ , Belo Horizonte , MG , Brazil.,c Department of Biochemistry , University of Cambridge , Cambridge , Cambridgeshire , UK.,d Department of Biochemistry and Molecular Biology , University of Melbourne , Melbourne , Victoria , Australia
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