1
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Hu X, Xu Y, Yi J, Wang C, Zhu Z, Yue T, Zhang H, Wang X, Wu F, Xue L, Bai L, Liu H, Chen Q. Using Protein Design and Directed Evolution to Monomerize a Bright Near-Infrared Fluorescent Protein. ACS Synth Biol 2024; 13:1177-1190. [PMID: 38552148 DOI: 10.1021/acssynbio.3c00643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
The small ultrared fluorescent protein (smURFP) is a bright near-infrared (NIR) fluorescent protein (FP) that forms a dimer and binds its fluorescence chromophore, biliverdin, at its dimer interface. To engineer a monomeric NIR FP based on smURFP potentially more suitable for bioimaging, we employed protein design to extend the protein backbone with a new segment of two helices that shield the original dimer interface while covering the biliverdin binding pocket in place of the second chain in the original dimer. We experimentally characterized 13 designs and obtained a monomeric protein with a weak fluorescence. We enhanced the fluorescence of this designed protein through two rounds of directed evolution and obtained designed monomeric smURFP (DMsmURFP), a bright, stable, and monomeric NIR FP with a molecular weight of 19.6 kDa. We determined the crystal structures of DMsmURFP both in the apo state and in complex with biliverdin, which confirmed the designed structure. The use of DMsmURFP in in vivo imaging of mammalian systems was demonstrated. The backbone design-based strategy used here can also be applied to monomerize other naturally multimeric proteins with intersubunit functional sites.
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Affiliation(s)
- Xiuhong Hu
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Hefei National Center for Interdisciplinary Sciences at the Microscale, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Yang Xu
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Hefei National Center for Interdisciplinary Sciences at the Microscale, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Junxi Yi
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Hefei National Center for Interdisciplinary Sciences at the Microscale, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
- School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Chenchen Wang
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Zhongliang Zhu
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Ting Yue
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Haiyan Zhang
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Xinyu Wang
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Hefei National Center for Interdisciplinary Sciences at the Microscale, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Fan Wu
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Hefei National Center for Interdisciplinary Sciences at the Microscale, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Lin Xue
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
- Biomedical Sciences and Health Laboratory of Anhui Province, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Li Bai
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
- Biomedical Sciences and Health Laboratory of Anhui Province, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Haiyan Liu
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
- Biomedical Sciences and Health Laboratory of Anhui Province, University of Science and Technology of China, Hefei, Anhui 230027, China
- School of Data Science, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Quan Chen
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Hefei National Center for Interdisciplinary Sciences at the Microscale, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
- Biomedical Sciences and Health Laboratory of Anhui Province, University of Science and Technology of China, Hefei, Anhui 230027, China
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2
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Chu AE, Lu T, Huang PS. Sparks of function by de novo protein design. Nat Biotechnol 2024; 42:203-215. [PMID: 38361073 DOI: 10.1038/s41587-024-02133-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 01/09/2024] [Indexed: 02/17/2024]
Abstract
Information in proteins flows from sequence to structure to function, with each step causally driven by the preceding one. Protein design is founded on inverting this process: specify a desired function, design a structure executing this function, and find a sequence that folds into this structure. This 'central dogma' underlies nearly all de novo protein-design efforts. Our ability to accomplish these tasks depends on our understanding of protein folding and function and our ability to capture this understanding in computational methods. In recent years, deep learning-derived approaches for efficient and accurate structure modeling and enrichment of successful designs have enabled progression beyond the design of protein structures and towards the design of functional proteins. We examine these advances in the broader context of classical de novo protein design and consider implications for future challenges to come, including fundamental capabilities such as sequence and structure co-design and conformational control considering flexibility, and functional objectives such as antibody and enzyme design.
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Affiliation(s)
- Alexander E Chu
- Biophysics Program, Stanford University, Palo Alto, CA, USA
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA
- Google DeepMind, London, UK
| | - Tianyu Lu
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA
| | - Po-Ssu Huang
- Biophysics Program, Stanford University, Palo Alto, CA, USA.
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA.
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3
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Wang T, Wang L, Zhang X, Shen C, Zhang O, Wang J, Wu J, Jin R, Zhou D, Chen S, Liu L, Wang X, Hsieh CY, Chen G, Pan P, Kang Y, Hou T. Comprehensive assessment of protein loop modeling programs on large-scale datasets: prediction accuracy and efficiency. Brief Bioinform 2023; 25:bbad486. [PMID: 38171930 PMCID: PMC10764206 DOI: 10.1093/bib/bbad486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 12/04/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024] Open
Abstract
Protein loops play a critical role in the dynamics of proteins and are essential for numerous biological functions, and various computational approaches to loop modeling have been proposed over the past decades. However, a comprehensive understanding of the strengths and weaknesses of each method is lacking. In this work, we constructed two high-quality datasets (i.e. the General dataset and the CASP dataset) and systematically evaluated the accuracy and efficiency of 13 commonly used loop modeling approaches from the perspective of loop lengths, protein classes and residue types. The results indicate that the knowledge-based method FREAD generally outperforms the other tested programs in most cases, but encountered challenges when predicting loops longer than 15 and 30 residues on the CASP and General datasets, respectively. The ab initio method Rosetta NGK demonstrated exceptional modeling accuracy for short loops with four to eight residues and achieved the highest success rate on the CASP dataset. The well-known AlphaFold2 and RoseTTAFold require more resources for better performance, but they exhibit promise for predicting loops longer than 16 and 30 residues in the CASP and General datasets. These observations can provide valuable insights for selecting suitable methods for specific loop modeling tasks and contribute to future advancements in the field.
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Affiliation(s)
- Tianyue Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Langcheng Wang
- Department of Pathology, New York University Medical Center, 550 First Avenue, New York, NY 10016, USA
| | - Xujun Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Chao Shen
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Odin Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jike Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jialu Wu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Ruofan Jin
- College of Life Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Donghao Zhou
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, China
| | - Shicheng Chen
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Liwei Liu
- Advanced Computing and Storage Laboratory, Central Research Institute, 2012 Laboratories, Huawei Technologies Co., Ltd., Shenzhen 518129, Guangdong, China
| | - Xiaorui Wang
- State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macao, China
| | - Chang-Yu Hsieh
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Guangyong Chen
- Zhejiang Lab, Zhejiang University, Hangzhou 311121, Zhejiang, China
| | - Peichen Pan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
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4
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Kryś JD, Gront D. Coarse-grained potential for hydrogen bond interactions. J Mol Graph Model 2023; 124:108507. [PMID: 37295157 DOI: 10.1016/j.jmgm.2023.108507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/12/2023] [Accepted: 04/18/2023] [Indexed: 06/12/2023]
Abstract
Understanding protein structure and dynamics is crucial for investigating numerous biological processes. This however requires proper description of molecular interactions, most notably hydrogen bonds, which are the driving force behind the folding of protein sequences into working molecules. Due to the multi-body character of this interaction, proper mathematical formulation has been a matter of long debate in the literature. This description becomes even more complex in reduced protein models. In this contribution, we propose a novel hydrogen bond energy function definition that is based only on Cα positions and used for coarse-grained simulations. We show that this new method has the capability to recognize hydrogen bonds with over 80% accuracy and can successfully identify β-sheet in β-amyloid peptide simulations.
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Affiliation(s)
- Justyna D Kryś
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland.
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
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5
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Zsidó BZ, Bayarsaikhan B, Börzsei R, Hetényi C. Construction of Histone-Protein Complex Structures by Peptide Growing. Int J Mol Sci 2023; 24:13831. [PMID: 37762134 PMCID: PMC10530865 DOI: 10.3390/ijms241813831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/04/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
The structures of histone complexes are master keys to epigenetics. Linear histone peptide tails often bind to shallow pockets of reader proteins via weak interactions, rendering their structure determination challenging. In the present study, a new protocol, PepGrow, is introduced. PepGrow uses docked histone fragments as seeds and grows the full peptide tails in the reader-binding pocket, producing atomic-resolution structures of histone-reader complexes. PepGrow is able to handle the flexibility of histone peptides, and it is demonstrated to be more efficient than linking pre-docked peptide fragments. The new protocol combines the advantages of popular program packages and allows fast generation of solution structures. AutoDock, a force-field-based program, is used to supply the docked peptide fragments used as structural seeds, and the building algorithm of Modeller is adopted and tested as a peptide growing engine. The performance of PepGrow is compared to ten other docking methods, and it is concluded that in situ growing of a ligand from a seed is a viable strategy for the production of complex structures of histone peptides at atomic resolution.
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Affiliation(s)
| | | | | | - Csaba Hetényi
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti Út 12, 7624 Pécs, Hungary; (B.Z.Z.); (B.B.); (R.B.)
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6
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Mi Y, Marcu SB, Tabirca S, Yallapragada VVB. PROFASA-a web-based protein fragment and structure analysis workstation. Front Bioeng Biotechnol 2023; 11:1192094. [PMID: 37545885 PMCID: PMC10401835 DOI: 10.3389/fbioe.2023.1192094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 07/10/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction: In the field of bioinformatics and computational biology, protein structure modelling and analysis is a crucial aspect. However, most existing tools require a high degree of technical expertise and lack a user-friendly interface. To address this problem, we developed a protein workstation called PROFASA. Methods: PROFASA is an innovative protein workstation that combines state-of-the-art protein structure visualisation techniques with cutting-edge tools and algorithms for protein analysis. Our goal is to provide users with a comprehensive platform for all protein sequence and structure analyses. PROFASA is designed with the idea of simplifying complex protein analysis workflows into one-click operations, while providing powerful customisation options to meet the needs of professional users. Results: PROFASA provides a one-stop solution that enables users to perform protein structure evaluation, parametric analysis and protein visualisation. Users can use I-TASSER or AlphaFold2 to construct protein models with one click, generate new protein sequences, models, and calculate protein parameters. In addition, PROFASA offers features such as real-time collaboration, note sharing, and shared projects, making it an ideal tool for researchers and teaching professionals. Discussion: PROFASA's innovation lies in its user-friendly interface and one-stop solution. It not only lowers the barrier to entry for protein computation, analysis and visualisation tools, but also opens up new possibilities for protein research and education. We expect PROFASA to advance the study of protein design and engineering and open up new research areas.
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Affiliation(s)
- Yanlin Mi
- School of Computer Science and Information Technology, University College Cork, Cork, Ireland
- SFI Centre for Research Training in Artificial Intelligence, University College Cork, Cork, Ireland
| | - Stefan-Bogdan Marcu
- School of Computer Science and Information Technology, University College Cork, Cork, Ireland
| | - Sabin Tabirca
- School of Computer Science and Information Technology, University College Cork, Cork, Ireland
- Faculty of Mathematics and Informatics, Transylvania University of Brasov, Brasov, Romania
| | - Venkata V. B. Yallapragada
- Centre for Advanced Photonics and Process Analytics, Munster Technological University, Cork, Ireland
- Tyndall National Institute, Cork, Ireland
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7
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Ledwitch KV, Künze G, McKinney JR, Okwei E, Larochelle K, Pankewitz L, Ganguly S, Darling HL, Coin I, Meiler J. Sparse pseudocontact shift NMR data obtained from a non-canonical amino acid-linked lanthanide tag improves integral membrane protein structure prediction. JOURNAL OF BIOMOLECULAR NMR 2023; 77:69-82. [PMID: 37016190 PMCID: PMC10443207 DOI: 10.1007/s10858-023-00412-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 03/20/2023] [Indexed: 06/19/2023]
Abstract
A single experimental method alone often fails to provide the resolution, accuracy, and coverage needed to model integral membrane proteins (IMPs). Integrating computation with experimental data is a powerful approach to supplement missing structural information with atomic detail. We combine RosettaNMR with experimentally-derived paramagnetic NMR restraints to guide membrane protein structure prediction. We demonstrate this approach using the disulfide bond formation protein B (DsbB), an α-helical IMP. Here, we attached a cyclen-based paramagnetic lanthanide tag to an engineered non-canonical amino acid (ncAA) using a copper-catalyzed azide-alkyne cycloaddition (CuAAC) click chemistry reaction. Using this tagging strategy, we collected 203 backbone HN pseudocontact shifts (PCSs) for three different labeling sites and used these as input to guide de novo membrane protein structure prediction protocols in Rosetta. We find that this sparse PCS dataset combined with 44 long-range NOEs as restraints in our calculations improves structure prediction of DsbB by enhancements in model accuracy, sampling, and scoring. The inclusion of this PCS dataset improved the Cα-RMSD transmembrane segment values of the best-scoring and best-RMSD models from 9.57 Å and 3.06 Å (no NMR data) to 5.73 Å and 2.18 Å, respectively.
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Affiliation(s)
- Kaitlyn V Ledwitch
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37240, USA.
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA.
- Department of Chemistry, Center for Structural Biology, MRBIII 5154E, Vanderbilt University, Nashville, TN, 37212, USA.
| | - Georg Künze
- Institute of Drug Discovery, Faculty of Medicine, University of Leipzig, 04103, Leipzig, Germany
| | - Jacob R McKinney
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37240, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
| | - Elleansar Okwei
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37240, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
| | - Katherine Larochelle
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37240, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
| | - Lisa Pankewitz
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37240, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
| | - Soumya Ganguly
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37240, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
| | - Heather L Darling
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37240, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
| | - Irene Coin
- Institute of Biochemistry, Faculty of Life Science, University of Leipzig, 04103, Leipzig, Germany
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37240, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Institute of Drug Discovery, Faculty of Medicine, University of Leipzig, 04103, Leipzig, Germany
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8
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Ali M, Khramushin A, Yadav VK, Schueler-Furman O, Ivarsson Y. Elucidation of Short Linear Motif-Based Interactions of the FERM Domains of Ezrin, Radixin, Moesin, and Merlin. Biochemistry 2023. [PMID: 37224425 DOI: 10.1021/acs.biochem.3c00096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
The ERM (ezrin, radixin, and moesin) family of proteins and the related protein merlin participate in scaffolding and signaling events at the cell cortex. The proteins share an N-terminal FERM [band four-point-one (4.1) ERM] domain composed of three subdomains (F1, F2, and F3) with binding sites for short linear peptide motifs. By screening the FERM domains of the ERMs and merlin against a phage library that displays peptides representing the intrinsically disordered regions of the human proteome, we identified a large number of novel ligands. We determined the affinities for the ERM and merlin FERM domains interacting with 18 peptides and validated interactions with full-length proteins through pull-down experiments. The majority of the peptides contained an apparent Yx[FILV] motif; others show alternative motifs. We defined distinct binding sites for two types of similar but distinct binding motifs (YxV and FYDF) using a combination of Rosetta FlexPepDock computational peptide docking protocols and mutational analysis. We provide a detailed molecular understanding of how the two types of peptides with distinct motifs bind to different sites on the moesin FERM phosphotyrosine binding-like subdomain and uncover interdependencies between the different types of ligands. The study expands the motif-based interactomes of the ERMs and merlin and suggests that the FERM domain acts as a switchable interaction hub.
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Affiliation(s)
- Muhammad Ali
- Department of Chemistry - BMC, Uppsala University, Husargatan 3, 751 23 Uppsala, Sweden
| | - Alisa Khramushin
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Vikash K Yadav
- Department of Chemistry - BMC, Uppsala University, Husargatan 3, 751 23 Uppsala, Sweden
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Ylva Ivarsson
- Department of Chemistry - BMC, Uppsala University, Husargatan 3, 751 23 Uppsala, Sweden
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9
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Koehler Leman J, Szczerbiak P, Renfrew PD, Gligorijevic V, Berenberg D, Vatanen T, Taylor BC, Chandler C, Janssen S, Pataki A, Carriero N, Fisk I, Xavier RJ, Knight R, Bonneau R, Kosciolek T. Sequence-structure-function relationships in the microbial protein universe. Nat Commun 2023; 14:2351. [PMID: 37100781 PMCID: PMC10133388 DOI: 10.1038/s41467-023-37896-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 04/05/2023] [Indexed: 04/28/2023] Open
Abstract
For the past half-century, structural biologists relied on the notion that similar protein sequences give rise to similar structures and functions. While this assumption has driven research to explore certain parts of the protein universe, it disregards spaces that don't rely on this assumption. Here we explore areas of the protein universe where similar protein functions can be achieved by different sequences and different structures. We predict ~200,000 structures for diverse protein sequences from 1,003 representative genomes across the microbial tree of life and annotate them functionally on a per-residue basis. Structure prediction is accomplished using the World Community Grid, a large-scale citizen science initiative. The resulting database of structural models is complementary to the AlphaFold database, with regards to domains of life as well as sequence diversity and sequence length. We identify 148 novel folds and describe examples where we map specific functions to structural motifs. We also show that the structural space is continuous and largely saturated, highlighting the need for a shift in focus across all branches of biology, from obtaining structures to putting them into context and from sequence-based to sequence-structure-function based meta-omics analyses.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, NY, USA.
| | - Pawel Szczerbiak
- Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
| | - P Douglas Renfrew
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Department of Biology, New York University, New York, NY, USA
| | - Vladimir Gligorijevic
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Prescient Design, a Genentech accelerator, New York, NY, 10010, USA
| | - Daniel Berenberg
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Prescient Design, a Genentech accelerator, New York, NY, 10010, USA
- Center for Data Science, New York University, New York, NY, 10011, USA
- Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, NY, USA
| | - Tommi Vatanen
- Broad Institute, Cambridge, MA, USA
- Liggins Institute, University of Auckland, Auckland, New Zealand
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, 00014 University of Helsinki, Helsinki, Finland
| | - Bryn C Taylor
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- In Silico Discovery and External Innovation, Janssen Research and Development, San Diego, CA, 92122, USA
| | - Chris Chandler
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Stefan Janssen
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, 92093, USA
- Algorithmic Bioinformatics, Justus Liebig University Giessen, Giessen, Germany
| | - Andras Pataki
- Scientific Computing Core, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Nick Carriero
- Scientific Computing Core, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Ian Fisk
- Scientific Computing Core, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Ramnik J Xavier
- Broad Institute, Cambridge, MA, USA
- Center for Microbiome Informatics and Therapeutics, MIT, Cambridge, MA, 02139, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California, San Diego, USA
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Department of Biology, New York University, New York, NY, USA
- Center for Data Science, New York University, New York, NY, 10011, USA
- Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, NY, USA
- Prescient Design, a Genentech accelerator, New York, NY, 10010, USA
| | - Tomasz Kosciolek
- Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland.
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10
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Lutz ID, Wang S, Norn C, Courbet A, Borst AJ, Zhao YT, Dosey A, Cao L, Xu J, Leaf EM, Treichel C, Litvicov P, Li Z, Goodson AD, Rivera-Sánchez P, Bratovianu AM, Baek M, King NP, Ruohola-Baker H, Baker D. Top-down design of protein architectures with reinforcement learning. Science 2023; 380:266-273. [PMID: 37079676 DOI: 10.1126/science.adf6591] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 03/21/2023] [Indexed: 04/22/2023]
Abstract
As a result of evolutionary selection, the subunits of naturally occurring protein assemblies often fit together with substantial shape complementarity to generate architectures optimal for function in a manner not achievable by current design approaches. We describe a "top-down" reinforcement learning-based design approach that solves this problem using Monte Carlo tree search to sample protein conformers in the context of an overall architecture and specified functional constraints. Cryo-electron microscopy structures of the designed disk-shaped nanopores and ultracompact icosahedra are very close to the computational models. The icosohedra enable very-high-density display of immunogens and signaling molecules, which potentiates vaccine response and angiogenesis induction. Our approach enables the top-down design of complex protein nanomaterials with desired system properties and demonstrates the power of reinforcement learning in protein design.
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Affiliation(s)
- Isaac D Lutz
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Shunzhi Wang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Christoffer Norn
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- BioInnovation Institute, DK2200 Copenhagen N, Denmark
| | - Alexis Courbet
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Andrew J Borst
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Yan Ting Zhao
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
- Oral Health Sciences, University of Washington, Seattle, WA, USA
| | - Annie Dosey
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Longxing Cao
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
| | - Jinwei Xu
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Elizabeth M Leaf
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Catherine Treichel
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Patrisia Litvicov
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Zhe Li
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Alexander D Goodson
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | | | - Minkyung Baek
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Neil P King
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Hannele Ruohola-Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
- Oral Health Sciences, University of Washington, Seattle, WA, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
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11
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Mufassirin MMM, Newton MAH, Sattar A. Artificial intelligence for template-free protein structure prediction: a comprehensive review. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10350-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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12
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Förster D, Idier J, Liberti L, Mucherino A, Lin JH, Malliavin TE. Low-resolution description of the conformational space for intrinsically disordered proteins. Sci Rep 2022; 12:19057. [PMID: 36352011 PMCID: PMC9646904 DOI: 10.1038/s41598-022-21648-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 09/29/2022] [Indexed: 11/11/2022] Open
Abstract
Intrinsically disordered proteins (IDP) are at the center of numerous biological processes, and attract consequently extreme interest in structural biology. Numerous approaches have been developed for generating sets of IDP conformations verifying a given set of experimental measurements. We propose here to perform a systematic enumeration of protein conformations, carried out using the TAiBP approach based on distance geometry. This enumeration was performed on two proteins, Sic1 and pSic1, corresponding to unphosphorylated and phosphorylated states of an IDP. The relative populations of the obtained conformations were then obtained by fitting SAXS curves as well as Ramachandran probability maps, the original finite mixture approach RamaMix being developed for this second task. The similarity between profiles of local gyration radii provides to a certain extent a converged view of the Sic1 and pSic1 conformational space. Profiles and populations are thus proposed for describing IDP conformations. Different variations of the resulting gyration radius between phosphorylated and unphosphorylated states are observed, depending on the set of enumerated conformations as well as on the methods used for obtaining the populations.
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Affiliation(s)
- Daniel Förster
- grid.112485.b0000 0001 0217 6921UMR7374 Interfaces, Confinement, Matériaux et Nanostructures, Université d’Orléans, Orléans, France
| | - Jérôme Idier
- grid.503212.70000 0000 9563 6044UMR6004 Laboratoire des Sciences du Numérique de Nantes, Nantes, France
| | - Leo Liberti
- grid.508893.fLIX UMR 7161 CNRS École Polytechnique, Institut Polytechnique de Paris, 91128 Palaiseau, France
| | - Antonio Mucherino
- grid.420225.30000 0001 2298 7270IRISA, University of Rennes 1, Rennes, France
| | - Jung-Hsin Lin
- grid.509455.8Biomedical Translation Research Center, Academia Sinica, Taipei, Taiwan
| | - Thérèse E. Malliavin
- grid.428999.70000 0001 2353 6535Institut Pasteur, Université Paris Cité, CNRS UMR3528, Unité de Bioinformatique Structurale, F-75015 Paris, France ,grid.29172.3f0000 0001 2194 6418Université de Lorraine, CNRS UMR7019, LPCT, F-54000 Nancy, France
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13
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Qing R, Hao S, Smorodina E, Jin D, Zalevsky A, Zhang S. Protein Design: From the Aspect of Water Solubility and Stability. Chem Rev 2022; 122:14085-14179. [PMID: 35921495 PMCID: PMC9523718 DOI: 10.1021/acs.chemrev.1c00757] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Indexed: 12/13/2022]
Abstract
Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.
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Affiliation(s)
- Rui Qing
- State
Key Laboratory of Microbial Metabolism, School of Life Sciences and
Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- The
David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Shilei Hao
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- Key
Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, China
| | - Eva Smorodina
- Department
of Immunology, University of Oslo and Oslo
University Hospital, Oslo 0424, Norway
| | - David Jin
- Avalon GloboCare
Corp., Freehold, New Jersey 07728, United States
| | - Arthur Zalevsky
- Laboratory
of Bioinformatics Approaches in Combinatorial Chemistry and Biology, Shemyakin−Ovchinnikov Institute of Bioorganic
Chemistry RAS, Moscow 117997, Russia
| | - Shuguang Zhang
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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14
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Targeted Mutagenesis of the Multicopy Myrosinase Gene Family in Allotetraploid Brassica juncea Reduces Pungency in Fresh Leaves across Environments. PLANTS 2022; 11:plants11192494. [PMID: 36235360 PMCID: PMC9572489 DOI: 10.3390/plants11192494] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/08/2022] [Accepted: 09/19/2022] [Indexed: 11/17/2022]
Abstract
Recent breeding efforts in Brassica have focused on the development of new oilseed feedstock crop for biofuels (e.g., ethanol, biodiesel, bio-jet fuel), bio-industrial uses (e.g., bio-plastics, lubricants), specialty fatty acids (e.g., erucic acid), and producing low glucosinolates levels for oilseed and feed meal production for animal consumption. We identified a novel opportunity to enhance the availability of nutritious, fresh leafy greens for human consumption. Here, we demonstrated the efficacy of disarming the ‘mustard bomb’ reaction in reducing pungency upon the mastication of fresh tissue—a major source of unpleasant flavor and/or odor in leafy Brassica. Using gene-specific mutagenesis via CRISPR-Cas12a, we created knockouts of all functional copies of the type-I myrosinase multigene family in tetraploid Brassica juncea. Our greenhouse and field trials demonstrate, via sensory and biochemical analyses, a stable reduction in pungency in edited plants across multiple environments. Collectively, these efforts provide a compelling path toward boosting the human consumption of nutrient-dense, fresh, leafy green vegetables.
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15
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Verburgt J, Zhang Z, Kihara D. Multi-level analysis of intrinsically disordered protein docking methods. Methods 2022; 204:55-63. [PMID: 35609776 PMCID: PMC9701586 DOI: 10.1016/j.ymeth.2022.05.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/17/2022] [Accepted: 05/19/2022] [Indexed: 12/29/2022] Open
Abstract
Intrinsically Disordered Proteins (IDPs) are a class of proteins in which at least some region of the protein does not possess any stable structure in solution in the physiological condition but may adopt an ordered structure upon binding to a globular receptor. These IDP-receptor complexes are thus subject to protein complex modeling in which computational techniques are applied to accurately reproduce the IDP ligand-receptor interactions. This often exists in the form of protein docking, in which the 3D structures of both the subunits are known, but the position of the ligand relative to the receptor is not. Here, we evaluate the performance of three IDP-receptor modeling tools with metrics that characterize the IDP-receptor interface at various resolutions. We show that all three methods are able to properly identify the general binding site, as identified by lower resolution metrics, but begin to struggle with higher resolution metrics that capture biophysical interactions.
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Affiliation(s)
- Jacob Verburgt
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Zicong Zhang
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA,Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA,Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN, 47907, USA,Corresponding Author
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16
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Turzo SMBA, Seffernick JT, Rolland AD, Donor MT, Heinze S, Prell JS, Wysocki VH, Lindert S. Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction. Nat Commun 2022; 13:4377. [PMID: 35902583 PMCID: PMC9334640 DOI: 10.1038/s41467-022-32075-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 07/14/2022] [Indexed: 11/09/2022] Open
Abstract
Ion mobility (IM) mass spectrometry provides structural information about protein shape and size in the form of an orientationally-averaged collision cross-section (CCSIM). While IM data have been used with various computational methods, they have not yet been utilized to predict monomeric protein structure from sequence. Here, we show that IM data can significantly improve protein structure determination using the modelling suite Rosetta. We develop the Rosetta Projection Approximation using Rough Circular Shapes (PARCS) algorithm that allows for fast and accurate prediction of CCSIM from structure. Following successful testing of the PARCS algorithm, we use an integrative modelling approach to utilize IM data for protein structure prediction. Additionally, we propose a confidence metric that identifies near native models in the absence of a known structure. The results of this study demonstrate the ability of IM data to consistently improve protein structure prediction. Collision cross sections (CCS) from ion mobility mass spectrometry provide information about protein shape and size. Here, the authors develop an algorithm to predict CCS and integrate experimental ion mobility data into Rosetta-based molecular modelling to predict protein structures from sequence.
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Affiliation(s)
- S M Bargeen Alam Turzo
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH, 43210, USA
| | - Justin T Seffernick
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH, 43210, USA
| | - Amber D Rolland
- Department of Chemistry and Biochemistry and Materials Science Institute, University of Oregon, Eugene, OR, 97403, USA
| | - Micah T Donor
- Department of Chemistry and Biochemistry and Materials Science Institute, University of Oregon, Eugene, OR, 97403, USA
| | - Sten Heinze
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH, 43210, USA
| | - James S Prell
- Department of Chemistry and Biochemistry and Materials Science Institute, University of Oregon, Eugene, OR, 97403, USA
| | - Vicki H Wysocki
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH, 43210, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH, 43210, USA.
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17
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Magi Meconi G, Sasselli IR, Bianco V, Onuchic JN, Coluzza I. Key aspects of the past 30 years of protein design. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2022; 85:086601. [PMID: 35704983 DOI: 10.1088/1361-6633/ac78ef] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 06/15/2022] [Indexed: 06/15/2023]
Abstract
Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins' most remarkable feature is their modularity. The large amount of information required to specify each protein's function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.
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Affiliation(s)
- Giulia Magi Meconi
- Computational Biophysics Lab, Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Paseo de Miramon 182, 20014, Donostia-San Sebastián, Spain
| | - Ivan R Sasselli
- Computational Biophysics Lab, Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Paseo de Miramon 182, 20014, Donostia-San Sebastián, Spain
| | | | - Jose N Onuchic
- Center for Theoretical Biological Physics, Department of Physics & Astronomy, Department of Chemistry, Department of Biosciences, Rice University, Houston, TX 77251, United States of America
| | - Ivan Coluzza
- BCMaterials, Basque Center for Materials, Applications and Nanostructures, Bld. Martina Casiano, UPV/EHU Science Park, Barrio Sarriena s/n, 48940 Leioa, Spain
- Basque Foundation for Science, Ikerbasque, 48009, Bilbao, Spain
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18
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Elazar A, Chandler NJ, Davey AS, Weinstein JY, Nguyen JV, Trenker R, Cross RS, Jenkins MR, Call MJ, Call ME, Fleishman SJ. De novo-designed transmembrane domains tune engineered receptor functions. eLife 2022; 11:75660. [PMID: 35506657 PMCID: PMC9068223 DOI: 10.7554/elife.75660] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 04/14/2022] [Indexed: 12/20/2022] Open
Abstract
De novo-designed receptor transmembrane domains (TMDs) present opportunities for precise control of cellular receptor functions. We developed a de novo design strategy for generating programmed membrane proteins (proMPs): single-pass α-helical TMDs that self-assemble through computationally defined and crystallographically validated interfaces. We used these proMPs to program specific oligomeric interactions into a chimeric antigen receptor (CAR) that we expressed in mouse primary T cells and found that both in vitro CAR T cell cytokine release and in vivo antitumor activity scaled linearly with the oligomeric state encoded by the receptor TMD, from monomers up to tetramers. All programmed CARs stimulated substantially lower T cell cytokine release relative to the commonly used CD28 TMD, which we show elevated cytokine release through lateral recruitment of the endogenous T cell costimulatory receptor CD28. Precise design using orthogonal and modular TMDs thus provides a new way to program receptor structure and predictably tune activity for basic or applied synthetic biology.
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Affiliation(s)
- Assaf Elazar
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Nicholas J Chandler
- Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Ashleigh S Davey
- Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Jonathan Y Weinstein
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Julie V Nguyen
- Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Raphael Trenker
- Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Ryan S Cross
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia.,Immunology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Misty R Jenkins
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia.,Immunology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,La Trobe Institute of Molecular Science, La Trobe University, Bundoora, Victoria, Australia
| | - Melissa J Call
- Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Matthew E Call
- Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
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19
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Matching protein surface structural patches for high-resolution blind peptide docking. Proc Natl Acad Sci U S A 2022; 119:e2121153119. [PMID: 35482919 PMCID: PMC9170164 DOI: 10.1073/pnas.2121153119] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Modeling interactions between short peptides and their receptors is a challenging docking problem due to the peptide flexibility, resulting in a formidable sampling problem of peptide conformation in addition to its orientation. Alternatively, the peptide can be viewed as a piece that complements the receptor monomer structure. Here, we show that the peptide conformation can be determined based on the receptor backbone only and sampled using local structural motifs found in solved protein monomers and interfaces, independent of sequence similarity. This approach outperforms current peptide docking protocols and promotes new directions for peptide interface design. Peptide docking can be perceived as a subproblem of protein–protein docking. However, due to the short length and flexible nature of peptides, many do not adopt one defined conformation prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation, but also the bound conformation of the peptide needs to be modeled. Traditional peptide-centered approaches use information about peptide sequences to generate representative conformer ensembles, which can then be rigid-body docked to the receptor. Alternatively, one may look at this problem from the viewpoint of the receptor, namely, that the protein surface defines the peptide-bound conformation. Here, we present PatchMAN (Patch-Motif AligNments), a global peptide-docking approach that uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. On a nonredundant set of protein–peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide–protein complexes in 58%/84% within 2.5 Å/5 Å interface backbone RMSD, with corresponding sampling in 81%/100% of the cases, outperforming other approaches. PatchMAN leverages the observation that structural units of peptides with their binding pocket can be found not only within interfaces, but also within monomers. We show that the bound peptide conformation is sampled based on the structural context of the receptor only, without taking into account any sequence information. Beyond peptide docking, this approach opens exciting new avenues to study principles of peptide–protein association, and to the design of new peptide binders. PatchMAN is available as a server at https://furmanlab.cs.huji.ac.il/patchman/.
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20
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Krivacic C, Kundert K, Pan X, Pache RA, Liu L, Conchúir SO, Jeliazkov JR, Gray JJ, Thompson MC, Fraser JS, Kortemme T. Accurate positioning of functional residues with robotics-inspired computational protein design. Proc Natl Acad Sci U S A 2022; 119:e2115480119. [PMID: 35254891 PMCID: PMC8931229 DOI: 10.1073/pnas.2115480119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 01/27/2022] [Indexed: 11/18/2022] Open
Abstract
SignificanceComputational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geometries, which remains a major challenge. Here, we develop computational methods that design and predict local protein geometries with greater accuracy than existing methods. Then, as a proof of concept, we leverage these methods to design new protein conformations in the enzyme ketosteroid isomerase that change the protein's preference for a key functional residue. Our computational methods are openly accessible and can be applied to the design of other intricate geometries customized for new user-defined protein functions.
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Affiliation(s)
- Cody Krivacic
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - Kale Kundert
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Biophysics Graduate Program, University of California, San Francisco, CA 94158
| | - Xingjie Pan
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - Roland A. Pache
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - Lin Liu
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - Shane O Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | | | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Michael C. Thompson
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - James S. Fraser
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Biophysics Graduate Program, University of California, San Francisco, CA 94158
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
| | - Tanja Kortemme
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Biophysics Graduate Program, University of California, San Francisco, CA 94158
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
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21
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Feng Q, Hou M, Liu J, Zhao K, Zhang G. Construct a variable-length fragment library for de novo protein structure prediction. Brief Bioinform 2022; 23:6547572. [PMID: 35284936 DOI: 10.1093/bib/bbac086] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 02/10/2022] [Accepted: 02/20/2022] [Indexed: 11/12/2022] Open
Abstract
Although remarkable achievements, such as AlphaFold2, have been made in end-to-end structure prediction, fragment libraries remain essential for de novo protein structure prediction, which can help explore and understand the protein-folding mechanism. In this work, we developed a variable-length fragment library (VFlib). In VFlib, a master structure database was first constructed from the Protein Data Bank through sequence clustering. The hidden Markov model (HMM) profile of each protein in the master structure database was generated by HHsuite, and the secondary structure of each protein was calculated by DSSP. For the query sequence, the HMM-profile was first constructed. Then, variable-length fragments were retrieved from the master structure database through dynamically variable-length profile-profile comparison. A complete method for chopping the query HMM-profile during this process was proposed to obtain fragments with increased diversity. Finally, secondary structure information was used to further screen the retrieved fragments to generate the final fragment library of specific query sequence. The experimental results obtained with a set of 120 nonredundant proteins show that the global precision and coverage of the fragment library generated by VFlib were 55.04% and 94.95% at the RMSD cutoff of 1.5 Å, respectively. Compared with the benchmark method of NNMake, the global precision of our fragment library had increased by 62.89% with equivalent coverage. Furthermore, the fragments generated by VFlib and NNMake were used to predict structure models through fragment assembly. Controlled experimental results demonstrate that the average TM-score of VFlib was 16.00% higher than that of NNMake.
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Affiliation(s)
- Qiongqiong Feng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Minghua Hou
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Jun Liu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Kailong Zhao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Guijun Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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22
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Abstract
The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design. Rational protein design to achieve a given protein backbone conformation is needed to engineer specific functions. Here Anand et al. describe a machine learning method using a learned neural network potential for fixed-backbone protein design.
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23
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Tsaban T, Varga JK, Avraham O, Ben-Aharon Z, Khramushin A, Schueler-Furman O. Harnessing protein folding neural networks for peptide-protein docking. Nat Commun 2022; 13:176. [PMID: 35013344 PMCID: PMC8748686 DOI: 10.1038/s41467-021-27838-9] [Citation(s) in RCA: 203] [Impact Index Per Article: 101.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 12/10/2021] [Indexed: 12/31/2022] Open
Abstract
Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been developed for the in silico folding of protein monomers, AlphaFold2 also enables quick and accurate modeling of peptide-protein interactions. Our simple implementation of AlphaFold2 generates peptide-protein complex models without requiring multiple sequence alignment information for the peptide partner, and can handle binding-induced conformational changes of the receptor. We explore what AlphaFold2 has memorized and learned, and describe specific examples that highlight differences compared to state-of-the-art peptide docking protocol PIPER-FlexPepDock. These results show that AlphaFold2 holds great promise for providing structural insight into a wide range of peptide-protein complexes, serving as a starting point for the detailed characterization and manipulation of these interactions.
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Affiliation(s)
- Tomer Tsaban
- Department of Microbiology and Molecular Genetics, Institute for Biomedical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Julia K Varga
- Department of Microbiology and Molecular Genetics, Institute for Biomedical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Orly Avraham
- Department of Microbiology and Molecular Genetics, Institute for Biomedical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ziv Ben-Aharon
- Department of Microbiology and Molecular Genetics, Institute for Biomedical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Alisa Khramushin
- Department of Microbiology and Molecular Genetics, Institute for Biomedical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Institute for Biomedical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel.
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24
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Koehler Leman J, Lyskov S, Lewis SM, Adolf-Bryfogle J, Alford RF, Barlow K, Ben-Aharon Z, Farrell D, Fell J, Hansen WA, Harmalkar A, Jeliazkov J, Kuenze G, Krys JD, Ljubetič A, Loshbaugh AL, Maguire J, Moretti R, Mulligan VK, Nance ML, Nguyen PT, Ó Conchúir S, Roy Burman SS, Samanta R, Smith ST, Teets F, Tiemann JKS, Watkins A, Woods H, Yachnin BJ, Bahl CD, Bailey-Kellogg C, Baker D, Das R, DiMaio F, Khare SD, Kortemme T, Labonte JW, Lindorff-Larsen K, Meiler J, Schief W, Schueler-Furman O, Siegel JB, Stein A, Yarov-Yarovoy V, Kuhlman B, Leaver-Fay A, Gront D, Gray JJ, Bonneau R. Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks. Nat Commun 2021; 12:6947. [PMID: 34845212 PMCID: PMC8630030 DOI: 10.1038/s41467-021-27222-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 11/02/2021] [Indexed: 01/14/2023] Open
Abstract
Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA.
- Department of Biology, New York University, New York, NY, 10003, USA.
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Steven M Lewis
- Cyrus Biotechnology, 1201 Second Ave, Suite 900, Seattle, WA, 98101, USA
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research, La Jolla, CA, 92037, USA
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kyle Barlow
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Ziv Ben-Aharon
- Department of Microbiology and Molecular Genetics, Hebrew University, Hadassah Medical School, POB 12272, Jerusalem, 91120, Israel
| | - Daniel Farrell
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Jason Fell
- Genome Center, University of California, Davis, CA, 95616, USA
- Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, 95616, USA
- Department of Chemistry, University of California, Davis, CA, 95616, USA
| | - William A Hansen
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Jeliazko Jeliazkov
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Institute for Drug Discovery, Medical School, Leipzig University, 04103, Leipzig, Germany
| | - Justyna D Krys
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Ajasja Ljubetič
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Amanda L Loshbaugh
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
- Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA
| | - Morgan L Nance
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Phuong T Nguyen
- Department of Physiology and Membrane Biology, School of Medicine, University of California, Davis, CA, 95616, USA
| | - Shane Ó Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Rituparna Samanta
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Shannon T Smith
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, 37235, USA
| | - Frank Teets
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Johanna K S Tiemann
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Andrew Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Hope Woods
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, 37235, USA
| | - Brahm J Yachnin
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Christopher D Bahl
- Institute for Protein Innovation, Boston, MA, 02115, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA
| | | | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Sagar D Khare
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
- Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Institute for Drug Discovery, Medical School, Leipzig University, 04103, Leipzig, Germany
| | - William Schief
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research, La Jolla, CA, 92037, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Hebrew University, Hadassah Medical School, POB 12272, Jerusalem, 91120, Israel
| | - Justin B Siegel
- Genome Center, University of California, Davis, CA, 95616, USA
- Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, 95616, USA
- Department of Chemistry, University of California, Davis, CA, 95616, USA
| | - Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Vladimir Yarov-Yarovoy
- Department of Physiology and Membrane Biology, School of Medicine, University of California, Davis, CA, 95616, USA
| | - Brian Kuhlman
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Andrew Leaver-Fay
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA.
- Department of Biology, New York University, New York, NY, 10003, USA.
- Department of Computer Science, New York University, New York, NY, 10003, USA.
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25
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Johansson-Åkhe I, Mirabello C, Wallner B. InterPepRank: Assessment of Docked Peptide Conformations by a Deep Graph Network. FRONTIERS IN BIOINFORMATICS 2021; 1:763102. [PMID: 36303778 PMCID: PMC9581042 DOI: 10.3389/fbinf.2021.763102] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 10/05/2021] [Indexed: 11/13/2022] Open
Abstract
Peptide-protein interactions between a smaller or disordered peptide stretch and a folded receptor make up a large part of all protein-protein interactions. A common approach for modeling such interactions is to exhaustively sample the conformational space by fast-Fourier-transform docking, and then refine a top percentage of decoys. Commonly, methods capable of ranking the decoys for selection fast enough for larger scale studies rely on first-principle energy terms such as electrostatics, Van der Waals forces, or on pre-calculated statistical potentials. We present InterPepRank for peptide-protein complex scoring and ranking. InterPepRank is a machine learning-based method which encodes the structure of the complex as a graph; with physical pairwise interactions as edges and evolutionary and sequence features as nodes. The graph network is trained to predict the LRMSD of decoys by using edge-conditioned graph convolutions on a large set of peptide-protein complex decoys. InterPepRank is tested on a massive independent test set with no targets sharing CATH annotation nor 30% sequence identity with any target in training or validation data. On this set, InterPepRank has a median AUC of 0.86 for finding coarse peptide-protein complexes with LRMSD < 4Å. This is an improvement compared to other state-of-the-art ranking methods that have a median AUC between 0.65 and 0.79. When included as a selection-method for selecting decoys for refinement in a previously established peptide docking pipeline, InterPepRank improves the number of medium and high quality models produced by 80% and 40%, respectively. The InterPepRank program as well as all scripts for reproducing and retraining it are available from: http://wallnerlab.org/InterPepRank.
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26
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Gumbart JC, Ferreira JL, Hwang H, Hazel AJ, Cooper CJ, Parks JM, Smith JC, Zgurskaya HI, Beeby M. Lpp positions peptidoglycan at the AcrA-TolC interface in the AcrAB-TolC multidrug efflux pump. Biophys J 2021; 120:3973-3982. [PMID: 34411576 DOI: 10.1016/j.bpj.2021.08.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 07/02/2021] [Accepted: 08/11/2021] [Indexed: 01/07/2023] Open
Abstract
The multidrug efflux pumps of Gram-negative bacteria are a class of complexes that span the periplasm, coupling both the inner and outer membranes to expel toxic molecules. The best-characterized example of these tripartite pumps is the AcrAB-TolC complex of Escherichia coli. However, how the complex interacts with the peptidoglycan (PG) cell wall, which is anchored to the outer membrane (OM) by Braun's lipoprotein (Lpp), is still largely unknown. In this work, we present molecular dynamics simulations of a complete, atomistic model of the AcrAB-TolC complex with the inner membrane, OM, and PG layers all present. We find that the PG localizes to the junction of AcrA and TolC, in agreement with recent cryo-tomography data. Free-energy calculations reveal that the positioning of PG is determined by the length and conformation of multiple Lpp copies anchoring it to the OM. The distance between the PG and OM measured in cryo-electron microscopy images of wild-type E. coli also agrees with the simulation-derived spacing. Sequence analysis of AcrA suggests a conserved role for interactions with PG in the assembly and stabilization of efflux pumps, one that may extend to other trans-envelope complexes as well.
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Affiliation(s)
- James C Gumbart
- School of Physics, Georgia Institute of Technology, Atlanta, Georgia.
| | - Josie L Ferreira
- Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Hyea Hwang
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - Anthony J Hazel
- School of Physics, Georgia Institute of Technology, Atlanta, Georgia
| | - Connor J Cooper
- UT/ORNL Center for Molecular Biophysics, Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee
| | - Jerry M Parks
- UT/ORNL Center for Molecular Biophysics, Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee
| | - Jeremy C Smith
- UT/ORNL Center for Molecular Biophysics, Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee; Department of Biochemistry & Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee
| | - Helen I Zgurskaya
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma
| | - Morgan Beeby
- Department of Life Sciences, Imperial College London, London, United Kingdom
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27
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Wieser F, Stryeck S, Lang K, Hahn C, Thallinger G, Feichtinger J, Hack P, Stepponat M, Merchant N, Lindstaedt S, Oberdorfer G. A local platform for user-friendly FAIR data management and reproducible analytics. J Biotechnol 2021; 341:43-50. [PMID: 34400238 DOI: 10.1016/j.jbiotec.2021.08.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 06/24/2021] [Accepted: 08/04/2021] [Indexed: 10/20/2022]
Abstract
Collaborative research is common practice in modern life sciences. For most projects several researchers from multiple universities collaborate on a specific topic. Frequently, these research projects produce a wealth of data that requires central and secure storage, which should also allow for easy sharing among project participants. Only under best circumstances, this comes with minimal technical overhead for the researchers. Moreover, the need for data to be analyzed in a reproducible way often poses a challenge for researchers without a data science background and thus represents an overly time-consuming process. Here, we report on the integration of CyVerse Austria (CAT), a new cyberinfrastructure for a local community of life science researchers and provide two examples how it can be used to facilitate FAIR data management and reproducible analytics for teaching and research. In particular, we describe in detail how CAT can be used (i) as a teaching platform with a defined software environment and data management/sharing possibilities, and (ii) to build a data analysis pipeline using the Docker technology tailored to the needs and interests of the researcher.
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Affiliation(s)
- Florian Wieser
- Institute of Biochemistry, Graz University of Technology, 8010, Graz, Austria
| | - Sarah Stryeck
- Institute for Interactive Systems and Data Science, Graz University of Technology, 8010, Graz, Austria; Know-Center GmbH, 8010, Graz, Austria
| | - Konrad Lang
- Institute for Interactive Systems and Data Science, Graz University of Technology, 8010, Graz, Austria; Know-Center GmbH, 8010, Graz, Austria
| | - Christoph Hahn
- Institute of Biology, University of Graz, 8010, Graz, Austria
| | - Gerhard Thallinger
- Institute of Biomedical Informatics, Graz University of Technology, 8010, Graz, Austria; BioTechMed-Graz, Mozartgasse 12/II, 8010, Graz, Styria, Austria
| | - Julia Feichtinger
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center, Medical University of Graz, 8010, Graz, Austria; BioTechMed-Graz, Mozartgasse 12/II, 8010, Graz, Styria, Austria
| | - Philipp Hack
- Central Information Technology, Graz University of Technology, 8010, Graz, Austria
| | - Manfred Stepponat
- Central Information Technology, Graz University of Technology, 8010, Graz, Austria
| | - Nirav Merchant
- Data Science Institute, University of Arizona, BSRL 200 A, Tucson, AZ, 85721, United States
| | - Stefanie Lindstaedt
- Institute for Interactive Systems and Data Science, Graz University of Technology, 8010, Graz, Austria; Know-Center GmbH, 8010, Graz, Austria.
| | - Gustav Oberdorfer
- Institute of Biochemistry, Graz University of Technology, 8010, Graz, Austria; BioTechMed-Graz, Mozartgasse 12/II, 8010, Graz, Styria, Austria.
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28
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DFT calculations of electronic structure evaluation and intermolecular interactions of p53-derived peptides with cytotoxic effect on breast cancer. Theor Chem Acc 2021. [DOI: 10.1007/s00214-021-02822-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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29
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Liu S, Wang T, Xu Q, Shao B, Yin J, Liu TY. Complementing sequence-derived features with structural information extracted from fragment libraries for protein structure prediction. BMC Bioinformatics 2021; 22:351. [PMID: 34182922 PMCID: PMC8240311 DOI: 10.1186/s12859-021-04258-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 06/10/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Fragment libraries play a key role in fragment-assembly based protein structure prediction, where protein fragments are assembled to form a complete three-dimensional structure. Rich and accurate structural information embedded in fragment libraries has not been systematically extracted and used beyond fragment assembly. METHODS To better leverage the valuable structural information for protein structure prediction, we extracted seven types of structural information from fragment libraries. We broadened the usage of such structural information by transforming fragment libraries into protein-specific potentials for gradient-descent based protein folding and encoding fragment libraries as structural features for protein property prediction. RESULTS Fragment libraires improved the accuracy of protein folding and outperformed state-of-the-art algorithms with respect to predicted properties, such as torsion angles and inter-residue distances. CONCLUSION Our work implies that the rich structural information extracted from fragment libraries can complement sequence-derived features to help protein structure prediction.
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Affiliation(s)
- Siyuan Liu
- School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China
- Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China
- Microsoft Research Asia, Beijing, China
| | - Tong Wang
- Microsoft Research Asia, Beijing, China.
| | | | - Bin Shao
- Microsoft Research Asia, Beijing, China
| | - Jian Yin
- School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China
- Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China
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30
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Robustification of RosettaAntibody and Rosetta SnugDock. PLoS One 2021; 16:e0234282. [PMID: 33764990 PMCID: PMC7993800 DOI: 10.1371/journal.pone.0234282] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 01/11/2021] [Indexed: 11/19/2022] Open
Abstract
In recent years, the observed antibody sequence space has grown exponentially due to advances in high-throughput sequencing of immune receptors. The rise in sequences has not been mirrored by a rise in structures, as experimental structure determination techniques have remained low-throughput. Computational modeling, however, has the potential to close the sequence–structure gap. To achieve this goal, computational methods must be robust, fast, easy to use, and accurate. Here we report on the latest advances made in RosettaAntibody and Rosetta SnugDock—methods for antibody structure prediction and antibody–antigen docking. We simplified the user interface, expanded and automated the template database, generalized the kinematics of antibody–antigen docking (which enabled modeling of single-domain antibodies) and incorporated new loop modeling techniques. To evaluate the effects of our updates on modeling accuracy, we developed rigorous tests under a new scientific benchmarking framework within Rosetta. Benchmarking revealed that more structurally similar templates could be identified in the updated database and that SnugDock broadened its applicability without losing accuracy. However, there are further advances to be made, including increasing the accuracy and speed of CDR-H3 loop modeling, before computational approaches can accurately model any antibody.
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31
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Zhang GJ, Xie TY, Zhou XG, Wang LJ, Hu J. Protein Structure Prediction Using Population-Based Algorithm Guided by Information Entropy. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:697-707. [PMID: 31180869 DOI: 10.1109/tcbb.2019.2921958] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Ab initio protein structure prediction is one of the most challenging problems in computational biology. Multistage algorithms are widely used in ab initio protein structure prediction. The different computational costs of a multistage algorithm for different proteins are important to be considered. In this study, a population-based algorithm guided by information entropy (PAIE), which includes exploration and exploitation stages, is proposed for protein structure prediction. In PAIE, an entropy-based stage switch strategy is designed to switch from the exploration stage to the exploitation stage. Torsion angle statistical information is also deduced from the first stage and employed to enhance the exploitation in the second stage. Results indicate that an improvement in the performance of protein structure prediction in a benchmark of 30 proteins and 17 other free modeling targets in CASP.
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32
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Vorobieva AA, White P, Liang B, Horne JE, Bera AK, Chow CM, Gerben S, Marx S, Kang A, Stiving AQ, Harvey SR, Marx DC, Khan GN, Fleming KG, Wysocki VH, Brockwell DJ, Tamm LK, Radford SE, Baker D. De novo design of transmembrane β barrels. Science 2021; 371:eabc8182. [PMID: 33602829 PMCID: PMC8064278 DOI: 10.1126/science.abc8182] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 12/07/2020] [Indexed: 12/12/2022]
Abstract
Transmembrane β-barrel proteins (TMBs) are of great interest for single-molecule analytical technologies because they can spontaneously fold and insert into membranes and form stable pores, but the range of pore properties that can be achieved by repurposing natural TMBs is limited. We leverage the power of de novo computational design coupled with a "hypothesis, design, and test" approach to determine TMB design principles, notably, the importance of negative design to slow β-sheet assembly. We design new eight-stranded TMBs, with no homology to known TMBs, that insert and fold reversibly into synthetic lipid membranes and have nuclear magnetic resonance and x-ray crystal structures very similar to the computational models. These advances should enable the custom design of pores for a wide range of applications.
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Affiliation(s)
- Anastassia A Vorobieva
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
| | - Paul White
- Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, USA
| | - Binyong Liang
- Department of Molecular Physiology and Biological Physics and Center for Membrane and Cell Physiology, University of Virginia, Charlottesville, VA 22903, USA
| | - Jim E Horne
- Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, USA
| | - Asim K Bera
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Cameron M Chow
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Stacey Gerben
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Sinduja Marx
- Department of Molecular Engineering and Sciences, University of Washington, Seattle, WA 98195, USA
| | - Alex Kang
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Alyssa Q Stiving
- Department of Chemistry and Biochemistry, Resource for Native Mass Spectrometry Guided Structural Biology, The Ohio State University, Columbus, OH 43210, USA
| | - Sophie R Harvey
- Department of Chemistry and Biochemistry, Resource for Native Mass Spectrometry Guided Structural Biology, The Ohio State University, Columbus, OH 43210, USA
| | - Dagan C Marx
- TC Jenkins Department of Biophysics Johns Hopkins University, Baltimore, MD 21218, USA
| | - G Nasir Khan
- Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, USA
| | - Karen G Fleming
- TC Jenkins Department of Biophysics Johns Hopkins University, Baltimore, MD 21218, USA
| | - Vicki H Wysocki
- Department of Chemistry and Biochemistry, Resource for Native Mass Spectrometry Guided Structural Biology, The Ohio State University, Columbus, OH 43210, USA
| | - David J Brockwell
- Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, USA
| | - Lukas K Tamm
- Department of Molecular Physiology and Biological Physics and Center for Membrane and Cell Physiology, University of Virginia, Charlottesville, VA 22903, USA
| | - Sheena E Radford
- Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
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33
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Studer G, Tauriello G, Bienert S, Biasini M, Johner N, Schwede T. ProMod3-A versatile homology modelling toolbox. PLoS Comput Biol 2021; 17:e1008667. [PMID: 33507980 PMCID: PMC7872268 DOI: 10.1371/journal.pcbi.1008667] [Citation(s) in RCA: 130] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 02/09/2021] [Accepted: 01/03/2021] [Indexed: 11/18/2022] Open
Abstract
Computational methods for protein structure modelling are routinely used to complement experimental structure determination, thus they help to address a broad spectrum of scientific questions in biomedical research. The most accurate methods today are based on homology modelling, i.e. detecting a homologue to the desired target sequence that can be used as a template for modelling. Here we present a versatile open source homology modelling toolbox as foundation for flexible and computationally efficient modelling workflows. ProMod3 is a fully scriptable software platform that can perform all steps required to generate a protein model by homology. Its modular design aims at fast prototyping of novel algorithms and implementing flexible modelling pipelines. Common modelling tasks, such as loop modelling, sidechain modelling or generating a full protein model by homology, are provided as production ready pipelines, forming the starting point for own developments and enhancements. ProMod3 is the central software component of the widely used SWISS-MODEL web-server.
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Affiliation(s)
- Gabriel Studer
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Gerardo Tauriello
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Stefan Bienert
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Marco Biasini
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Niklaus Johner
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Torsten Schwede
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
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34
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Abstract
For two decades, Rosetta has consistently been at the forefront of protein structure
prediction. While it has become a very large package comprising programs, scripts, and tools, for
different types of macromolecular modelling such as ligand docking, protein-protein docking,
protein design, and loop modelling, it started as the implementation of an algorithm for ab initio
protein structure prediction. The term ’Rosetta’ appeared for the first time twenty years ago in the
literature to describe that algorithm and its contribution to the third edition of the community wide
Critical Assessment of techniques for protein Structure Prediction (CASP3). Similar to the Rosetta
stone that allowed deciphering the ancient Egyptian civilisation, David Baker and his co-workers
have been contributing to deciphering ’the second half of the genetic code’. Although the focus of
Baker’s team has expended to de novo protein design in the past few years, Rosetta’s ‘fame’ is
associated with its fragment-assembly protein structure prediction approach. Following a
presentation of the main concepts underpinning its foundation, especially sequence-structure
correlation and usage of fragments, we review the main stages of its developments and highlight
the milestones it has achieved in terms of protein structure prediction, particularly in CASP.
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Affiliation(s)
- Jad Abbass
- Department of Computer Science, Lebanese International University, Bekaa, Lebanon
| | - Jean-Christophe Nebel
- Faculty of Science, Engineering and Computing, Kingston University, London, KT1 2EE, United Kingdom
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35
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Ikuta T, Shihoya W, Sugiura M, Yoshida K, Watari M, Tokano T, Yamashita K, Katayama K, Tsunoda SP, Uchihashi T, Kandori H, Nureki O. Structural insights into the mechanism of rhodopsin phosphodiesterase. Nat Commun 2020; 11:5605. [PMID: 33154353 PMCID: PMC7644710 DOI: 10.1038/s41467-020-19376-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 10/07/2020] [Indexed: 02/06/2023] Open
Abstract
Rhodopsin phosphodiesterase (Rh-PDE) is an enzyme rhodopsin belonging to a recently discovered class of microbial rhodopsins with light-dependent enzymatic activity. Rh-PDE consists of the N-terminal rhodopsin domain and C-terminal phosphodiesterase (PDE) domain, connected by 76-residue linker, and hydrolyzes both cAMP and cGMP in a light-dependent manner. Thus, Rh-PDE has potential for the optogenetic manipulation of cyclic nucleotide concentrations, as a complementary tool to rhodopsin guanylyl cyclase and photosensitive adenylyl cyclase. Here we present structural and functional analyses of the Rh-PDE derived from Salpingoeca rosetta. The crystal structure of the rhodopsin domain at 2.6 Å resolution revealed a new topology of rhodopsins, with 8 TMs including the N-terminal extra TM, TM0. Mutational analyses demonstrated that TM0 plays a crucial role in the enzymatic photoactivity. We further solved the crystal structures of the rhodopsin domain (3.5 Å) and PDE domain (2.1 Å) with their connecting linkers, which showed a rough sketch of the full-length Rh-PDE. Integrating these structures, we proposed a model of full-length Rh-PDE, based on the HS-AFM observations and computational modeling of the linker region. These findings provide insight into the photoactivation mechanisms of other 8-TM enzyme rhodopsins and expand the definition of rhodopsins.
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Affiliation(s)
- Tatsuya Ikuta
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo, Tokyo, 113-0033, Japan
| | - Wataru Shihoya
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo, Tokyo, 113-0033, Japan.
| | - Masahiro Sugiura
- Department of Life Science and Applied Chemistry, Nagoya Institute of Technology, Showa-Ku, Nagoya, 466-8555, Japan
| | - Kazuho Yoshida
- Department of Life Science and Applied Chemistry, Nagoya Institute of Technology, Showa-Ku, Nagoya, 466-8555, Japan
| | - Masahito Watari
- Department of Life Science and Applied Chemistry, Nagoya Institute of Technology, Showa-Ku, Nagoya, 466-8555, Japan
| | - Takaya Tokano
- Department of Physics, Nagoya University, Nagoya, 464-8602, Japan
| | - Keitaro Yamashita
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo, Tokyo, 113-0033, Japan
| | - Kota Katayama
- Department of Life Science and Applied Chemistry, Nagoya Institute of Technology, Showa-Ku, Nagoya, 466-8555, Japan
- OptoBioTechnology Research Center, Nagoya Institute of Technology, Showa-Ku, Nagoya, 466-8555, Japan
| | - Satoshi P Tsunoda
- Department of Life Science and Applied Chemistry, Nagoya Institute of Technology, Showa-Ku, Nagoya, 466-8555, Japan
- OptoBioTechnology Research Center, Nagoya Institute of Technology, Showa-Ku, Nagoya, 466-8555, Japan
| | - Takayuki Uchihashi
- Department of Physics, Nagoya University, Nagoya, 464-8602, Japan
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki, 444-8787, Japan
| | - Hideki Kandori
- Department of Life Science and Applied Chemistry, Nagoya Institute of Technology, Showa-Ku, Nagoya, 466-8555, Japan.
- OptoBioTechnology Research Center, Nagoya Institute of Technology, Showa-Ku, Nagoya, 466-8555, Japan.
| | - Osamu Nureki
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo, Tokyo, 113-0033, Japan.
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36
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Park S, Doherty EE, Xie Y, Padyana AK, Fang F, Zhang Y, Karki A, Lebrilla CB, Siegel JB, Beal PA. High-throughput mutagenesis reveals unique structural features of human ADAR1. Nat Commun 2020; 11:5130. [PMID: 33046702 PMCID: PMC7550611 DOI: 10.1038/s41467-020-18862-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 09/11/2020] [Indexed: 01/06/2023] Open
Abstract
Adenosine Deaminases that act on RNA (ADARs) are enzymes that catalyze adenosine to inosine conversion in dsRNA, a common form of RNA editing. Mutations in the human ADAR1 gene are known to cause disease and recent studies have identified ADAR1 as a potential therapeutic target for a subset of cancers. However, efforts to define the mechanistic effects for disease associated ADAR1 mutations and the rational design of ADAR1 inhibitors are limited by a lack of structural information. Here, we describe the combination of high throughput mutagenesis screening studies, biochemical characterization and Rosetta-based structure modeling to identify unique features of ADAR1. Importantly, these studies reveal a previously unknown zinc-binding site on the surface of the ADAR1 deaminase domain which is important for ADAR1 editing activity. Furthermore, we present structural models that explain known properties of this enzyme and make predictions about the role of specific residues in a surface loop unique to ADAR1.
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Affiliation(s)
- SeHee Park
- Department of Chemistry, University of California, Davis, Davis, CA, USA
| | - Erin E Doherty
- Department of Chemistry, University of California, Davis, Davis, CA, USA
| | - Yixuan Xie
- Department of Chemistry, University of California, Davis, Davis, CA, USA
| | | | | | - Yue Zhang
- Department of Chemistry, University of California, Davis, Davis, CA, USA
| | - Agya Karki
- Department of Chemistry, University of California, Davis, Davis, CA, USA
| | - Carlito B Lebrilla
- Department of Chemistry, University of California, Davis, Davis, CA, USA
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, CA, USA
| | - Justin B Siegel
- Department of Chemistry, University of California, Davis, Davis, CA, USA
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, CA, USA
- Genome Center, University of California Davis, Davis, CA, USA
| | - Peter A Beal
- Department of Chemistry, University of California, Davis, Davis, CA, USA.
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37
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Johansson-Åkhe I, Mirabello C, Wallner B. InterPep2: global peptide-protein docking using interaction surface templates. Bioinformatics 2020; 36:2458-2465. [PMID: 31917413 PMCID: PMC7178396 DOI: 10.1093/bioinformatics/btaa005] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 12/16/2019] [Accepted: 01/03/2020] [Indexed: 12/23/2022] Open
Abstract
Motivation Interactions between proteins and peptides or peptide-like intrinsically disordered regions are involved in many important biological processes, such as gene expression and cell life-cycle regulation. Experimentally determining the structure of such interactions is time-consuming and difficult because of the inherent flexibility of the peptide ligand. Although several prediction-methods exist, most are limited in performance or availability. Results InterPep2 is a freely available method for predicting the structure of peptide–protein interactions. Improved performance is obtained by using templates from both peptide–protein and regular protein–protein interactions, and by a random forest trained to predict the DockQ-score for a given template using sequence and structural features. When tested on 252 bound peptide–protein complexes from structures deposited after the complexes used in the construction of the training and templates sets of InterPep2, InterPep2-Refined correctly positioned 67 peptides within 4.0 Å LRMSD among top10, similar to another state-of-the-art template-based method which positioned 54 peptides correctly. However, InterPep2 displays a superior ability to evaluate the quality of its own predictions. On a previously established set of 27 non-redundant unbound-to-bound peptide–protein complexes, InterPep2 performs on-par with leading methods. The extended InterPep2-Refined protocol managed to correctly model 15 of these complexes within 4.0 Å LRMSD among top10, without using templates from homologs. In addition, combining the template-based predictions from InterPep2 with ab initio predictions from PIPER-FlexPepDock resulted in 22% more near-native predictions compared to the best single method (22 versus 18). Availability and implementation The program is available from: http://wallnerlab.org/InterPep2. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Isak Johansson-Åkhe
- Division of Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Claudio Mirabello
- Division of Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Björn Wallner
- Division of Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
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38
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Murase K, Moriwaki Y, Mori T, Liu X, Masaka C, Takada Y, Maesaki R, Mishima M, Fujii S, Hirano Y, Kawabe Z, Nagata K, Terada T, Suzuki G, Watanabe M, Shimizu K, Hakoshima T, Takayama S. Mechanism of self/nonself-discrimination in Brassica self-incompatibility. Nat Commun 2020; 11:4916. [PMID: 33004803 PMCID: PMC7530648 DOI: 10.1038/s41467-020-18698-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 09/07/2020] [Indexed: 01/07/2023] Open
Abstract
Self-incompatibility (SI) is a breeding system that promotes cross-fertilization. In Brassica, pollen rejection is induced by a haplotype-specific interaction between pistil determinant SRK (S receptor kinase) and pollen determinant SP11 (S-locus Protein 11, also named SCR) from the S-locus. Although the structure of the B. rapa S9-SRK ectodomain (eSRK) and S9-SP11 complex has been determined, it remains unclear how SRK discriminates self- and nonself-SP11. Here, we uncover the detailed mechanism of self/nonself-discrimination in Brassica SI by determining the S8-eSRK-S8-SP11 crystal structure and performing molecular dynamics (MD) simulations. Comprehensive binding analysis of eSRK and SP11 structures reveals that the binding free energies are most stable for cognate eSRK-SP11 combinations. Residue-based contribution analysis suggests that the modes of eSRK-SP11 interactions differ between intra- and inter-subgroup (a group of phylogenetically neighboring haplotypes) combinations. Our data establish a model of self/nonself-discrimination in Brassica SI.
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Affiliation(s)
- Kohji Murase
- grid.26999.3d0000 0001 2151 536XDepartment of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, 113-8657 Japan
| | - Yoshitaka Moriwaki
- grid.26999.3d0000 0001 2151 536XDepartment of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, 113-8657 Japan ,grid.26999.3d0000 0001 2151 536XCollaborative Research Institute for Innovative Microbiology, The University of Tokyo, Tokyo, 113-8657 Japan
| | - Tomoyuki Mori
- grid.260493.a0000 0000 9227 2257Graduate School of Biological Sciences, Nara Institute of Science and Technology, Nara, 630-0192 Japan
| | - Xiao Liu
- grid.260493.a0000 0000 9227 2257Graduate School of Biological Sciences, Nara Institute of Science and Technology, Nara, 630-0192 Japan
| | - Chiho Masaka
- grid.260493.a0000 0000 9227 2257Graduate School of Biological Sciences, Nara Institute of Science and Technology, Nara, 630-0192 Japan
| | - Yoshinobu Takada
- grid.69566.3a0000 0001 2248 6943Graduate School of Life Sciences, Tohoku University, Sendai, 980-8577 Japan
| | - Ryoko Maesaki
- grid.265074.20000 0001 1090 2030Graduate School of Science, Tokyo Metropolitan University, Tokyo, 192-0397 Japan
| | - Masaki Mishima
- grid.265074.20000 0001 1090 2030Graduate School of Science, Tokyo Metropolitan University, Tokyo, 192-0397 Japan
| | - Sota Fujii
- grid.26999.3d0000 0001 2151 536XDepartment of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, 113-8657 Japan
| | - Yoshinori Hirano
- grid.260493.a0000 0000 9227 2257Graduate School of Biological Sciences, Nara Institute of Science and Technology, Nara, 630-0192 Japan ,grid.26999.3d0000 0001 2151 536XPresent Address: Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, 113-0033 Japan
| | - Zen Kawabe
- grid.26999.3d0000 0001 2151 536XDepartment of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, 113-8657 Japan
| | - Koji Nagata
- grid.26999.3d0000 0001 2151 536XDepartment of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, 113-8657 Japan
| | - Tohru Terada
- grid.26999.3d0000 0001 2151 536XDepartment of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, 113-8657 Japan ,grid.26999.3d0000 0001 2151 536XCollaborative Research Institute for Innovative Microbiology, The University of Tokyo, Tokyo, 113-8657 Japan ,grid.26999.3d0000 0001 2151 536XAgricultural Bioinformatics Research Unit, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, 113-8657 Japan
| | - Go Suzuki
- grid.412382.e0000 0001 0660 7282Division of Natural Science, Osaka Kyoiku University, Kashiwara, 582-8582 Japan
| | - Masao Watanabe
- grid.69566.3a0000 0001 2248 6943Graduate School of Life Sciences, Tohoku University, Sendai, 980-8577 Japan
| | - Kentaro Shimizu
- grid.26999.3d0000 0001 2151 536XDepartment of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, 113-8657 Japan ,grid.26999.3d0000 0001 2151 536XCollaborative Research Institute for Innovative Microbiology, The University of Tokyo, Tokyo, 113-8657 Japan
| | - Toshio Hakoshima
- grid.260493.a0000 0000 9227 2257Graduate School of Biological Sciences, Nara Institute of Science and Technology, Nara, 630-0192 Japan
| | - Seiji Takayama
- grid.26999.3d0000 0001 2151 536XDepartment of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, 113-8657 Japan
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39
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Pan X, Thompson MC, Zhang Y, Liu L, Fraser JS, Kelly MJS, Kortemme T. Expanding the space of protein geometries by computational design of de novo fold families. Science 2020; 369:1132-1136. [PMID: 32855341 DOI: 10.1126/science.abc0881] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 07/14/2020] [Indexed: 01/03/2023]
Abstract
Naturally occurring proteins vary the precise geometries of structural elements to create distinct shapes optimal for function. We present a computational design method, loop-helix-loop unit combinatorial sampling (LUCS), that mimics nature's ability to create families of proteins with the same overall fold but precisely tunable geometries. Through near-exhaustive sampling of loop-helix-loop elements, LUCS generates highly diverse geometries encompassing those found in nature but also surpassing known structure space. Biophysical characterization showed that 17 (38%) of 45 tested LUCS designs encompassing two different structural topologies were well folded, including 16 with designed non-native geometries. Four experimentally solved structures closely matched the designs. LUCS greatly expands the designable structure space and offers a new paradigm for designing proteins with tunable geometries that may be customizable for novel functions.
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Affiliation(s)
- Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA. .,UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA, USA
| | - Michael C Thompson
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Yang Zhang
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Lin Liu
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - James S Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA
| | - Mark J S Kelly
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA. .,UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,Chan Zuckerberg Biohub, San Francisco, CA, USA
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40
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Khramushin A, Marcu O, Alam N, Shimony O, Padhorny D, Brini E, Dill KA, Vajda S, Kozakov D, Schueler-Furman O. Modeling beta-sheet peptide-protein interactions: Rosetta FlexPepDock in CAPRI rounds 38-45. Proteins 2020; 88:1037-1049. [PMID: 31891416 PMCID: PMC7539656 DOI: 10.1002/prot.25871] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 12/17/2019] [Accepted: 12/26/2019] [Indexed: 01/09/2023]
Abstract
Peptide-protein docking is challenging due to the considerable conformational freedom of the peptide. CAPRI rounds 38-45 included two peptide-protein interactions, both characterized by a peptide forming an additional beta strand of a beta sheet in the receptor. Using the Rosetta FlexPepDock peptide docking protocol we generated top-performing, high-accuracy models for targets 134 and 135, involving an interaction between a peptide derived from L-MAG with DLC8. In addition, we were able to generate the only medium-accuracy models for a particularly challenging target, T121. In contrast to the classical peptide-mediated interaction, in which receptor side chains contact both peptide backbone and side chains, beta-sheet complementation involves a major contribution to binding by hydrogen bonds between main chain atoms. To establish how binding affinity and specificity are established in this special class of peptide-protein interactions, we extracted PeptiDBeta, a benchmark of solved structures of different protein domains that are bound by peptides via beta-sheet complementation, and tested our protocol for global peptide-docking PIPER-FlexPepDock on this dataset. We find that the beta-strand part of the peptide is sufficient to generate approximate and even high resolution models of many interactions, but inclusion of adjacent motif residues often provides additional information necessary to achieve high resolution model quality.
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Affiliation(s)
- Alisa Khramushin
- Department of Microbiologyand Molecular Genetics, Institute
for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University,
Jerusalem, Israel
| | - Orly Marcu
- Department of Microbiologyand Molecular Genetics, Institute
for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University,
Jerusalem, Israel
| | - Nawsad Alam
- Department of Microbiologyand Molecular Genetics, Institute
for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University,
Jerusalem, Israel
| | - Orly Shimony
- Department of Microbiologyand Molecular Genetics, Institute
for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University,
Jerusalem, Israel
| | - Dzmitry Padhorny
- Department of Applied Mathematics and Statistics, Stony
Brook University, New York, New York
- Laufer Center for Physical and Quantitative Biology, Stony
Brook University, New York, New York
| | - Emiliano Brini
- Laufer Center for Physical and Quantitative Biology, Stony
Brook University, New York, New York
| | - Ken A. Dill
- Laufer Center for Physical and Quantitative Biology, Stony
Brook University, New York, New York
- Department of Physics and Astronomy, Stony Brook
University, New York, New York
- Department of Chemistry, Stony Brook University, New York,
New York
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University,
Boston, Massachusetts
- Department of Chemistry, Boston University, Boston,
Massachusetts
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony
Brook University, New York, New York
- Laufer Center for Physical and Quantitative Biology, Stony
Brook University, New York, New York
| | - Ora Schueler-Furman
- Department of Microbiologyand Molecular Genetics, Institute
for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University,
Jerusalem, Israel
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41
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Leman JK, Weitzner BD, Lewis SM, Adolf-Bryfogle J, Alam N, Alford RF, Aprahamian M, Baker D, Barlow KA, Barth P, Basanta B, Bender BJ, Blacklock K, Bonet J, Boyken SE, Bradley P, Bystroff C, Conway P, Cooper S, Correia BE, Coventry B, Das R, De Jong RM, DiMaio F, Dsilva L, Dunbrack R, Ford AS, Frenz B, Fu DY, Geniesse C, Goldschmidt L, Gowthaman R, Gray JJ, Gront D, Guffy S, Horowitz S, Huang PS, Huber T, Jacobs TM, Jeliazkov JR, Johnson DK, Kappel K, Karanicolas J, Khakzad H, Khar KR, Khare SD, Khatib F, Khramushin A, King IC, Kleffner R, Koepnick B, Kortemme T, Kuenze G, Kuhlman B, Kuroda D, Labonte JW, Lai JK, Lapidoth G, Leaver-Fay A, Lindert S, Linsky T, London N, Lubin JH, Lyskov S, Maguire J, Malmström L, Marcos E, Marcu O, Marze NA, Meiler J, Moretti R, Mulligan VK, Nerli S, Norn C, Ó'Conchúir S, Ollikainen N, Ovchinnikov S, Pacella MS, Pan X, Park H, Pavlovicz RE, Pethe M, Pierce BG, Pilla KB, Raveh B, Renfrew PD, Burman SSR, Rubenstein A, Sauer MF, Scheck A, Schief W, Schueler-Furman O, Sedan Y, Sevy AM, Sgourakis NG, Shi L, Siegel JB, Silva DA, Smith S, Song Y, Stein A, Szegedy M, Teets FD, Thyme SB, Wang RYR, Watkins A, Zimmerman L, Bonneau R. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat Methods 2020; 17:665-680. [PMID: 32483333 PMCID: PMC7603796 DOI: 10.1038/s41592-020-0848-2] [Citation(s) in RCA: 395] [Impact Index Per Article: 98.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 04/22/2020] [Indexed: 12/12/2022]
Abstract
The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
| | - Brian D Weitzner
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Steven M Lewis
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biochemistry, Duke University, Durham, NC, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nawsad Alam
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Melanie Aprahamian
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Kyle A Barlow
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA, USA
| | - Patrick Barth
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Benjamin Basanta
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Biological Physics Structure and Design PhD Program, University of Washington, Seattle, WA, USA
| | - Brian J Bender
- Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Kristin Blacklock
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Jaume Bonet
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Scott E Boyken
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Phil Bradley
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris Bystroff
- Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Patrick Conway
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Seth Cooper
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lorna Dsilva
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Roland Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Alexander S Ford
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Brandon Frenz
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Darwin Y Fu
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Caleb Geniesse
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Warsaw, Poland
| | - Sharon Guffy
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Scott Horowitz
- Department of Chemistry & Biochemistry, University of Denver, Denver, CO, USA
- The Knoebel Institute for Healthy Aging, University of Denver, Denver, CO, USA
| | - Po-Ssu Huang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Thomas Huber
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Tim M Jacobs
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - David K Johnson
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Kalli Kappel
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - John Karanicolas
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Hamed Khakzad
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
| | - Karen R Khar
- Cyrus Biotechnology, Seattle, WA, USA
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Sagar D Khare
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Firas Khatib
- Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, MA, USA
| | - Alisa Khramushin
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Indigo C King
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Robert Kleffner
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Brian Koepnick
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Daisuke Kuroda
- Medical Device Development and Regulation Research Center, School of Engineering, University of Tokyo, Tokyo, Japan
- Department of Bioengineering, School of Engineering, University of Tokyo, Tokyo, Japan
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Chemistry, Franklin & Marshall College, Lancaster, PA, USA
| | - Jason K Lai
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Gideon Lapidoth
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Andrew Leaver-Fay
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - Thomas Linsky
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Nir London
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Joseph H Lubin
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lars Malmström
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Enrique Marcos
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Research in Biomedicine Barcelona, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Orly Marcu
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Nicholas A Marze
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Departments of Chemistry, Pharmacology and Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
- Institute for Chemical Biology, Vanderbilt University, Nashville, TN, USA
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Santrupti Nerli
- Department of Computer Science, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Christoffer Norn
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Shane Ó'Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Noah Ollikainen
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Sergey Ovchinnikov
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Michael S Pacella
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Hahnbeom Park
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ryan E Pavlovicz
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Manasi Pethe
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Kala Bharath Pilla
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Barak Raveh
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - P Douglas Renfrew
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Aliza Rubenstein
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Marion F Sauer
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Andreas Scheck
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - William Schief
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yuval Sedan
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Alexander M Sevy
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Lei Shi
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justin B Siegel
- Department of Chemistry, University of California, Davis, Davis, CA, USA
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, California, USA
- Genome Center, University of California, Davis, Davis, CA, USA
| | | | - Shannon Smith
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Yifan Song
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Amelie Stein
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Maria Szegedy
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Frank D Teets
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Summer B Thyme
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ray Yu-Ruei Wang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Andrew Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Lior Zimmerman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
- Department of Computer Science, New York University, New York, NY, USA.
- Center for Data Science, New York University, New York, NY, USA.
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42
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Ferrie JJ, Petersson EJ. A Unified De Novo Approach for Predicting the Structures of Ordered and Disordered Proteins. J Phys Chem B 2020; 124:5538-5548. [PMID: 32525675 DOI: 10.1021/acs.jpcb.0c02924] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
As recognition of the abundance and relevance of intrinsically disordered proteins (IDPs) continues to grow, demand increases for methods that can rapidly predict the conformational ensembles populated by these proteins. To date, IDP simulations have largely been dominated by molecular dynamics (MD) simulations, which require significant compute times and/or complex hardware. Recent developments in MD have afforded methods capable of simulating both ordered and disordered proteins, yet to date, accurate fold prediction from a sequence has been dominated by Monte Carlo (MC)-based methods such as Rosetta. To overcome the limitations of current approaches in IDP simulation using Rosetta while maintaining its utility for modeling folded domains, we developed PyRosetta-based algorithms that allow for the accurate de novo prediction of proteins across all degrees of foldedness along with structural ensembles of disordered proteins. Our simulations have accuracy comparable to state-of-the-art MD with vastly reduced computational demands.
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Affiliation(s)
- John J Ferrie
- Department of Chemistry, University of Pennsylvania, 231 South 34th Street, Philadelphia, Pennsylvania 19104-6323, United States
| | - E James Petersson
- Department of Chemistry, University of Pennsylvania, 231 South 34th Street, Philadelphia, Pennsylvania 19104-6323, United States
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43
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Kim DN, Gront D, Sanbonmatsu KY. Practical Considerations for Atomistic Structure Modeling with Cryo-EM Maps. J Chem Inf Model 2020; 60:2436-2442. [PMID: 32422044 PMCID: PMC7891309 DOI: 10.1021/acs.jcim.0c00090] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We describe common approaches to atomistic structure modeling with single particle analysis derived cryo-EM maps. Several strategies for atomistic model building and atomistic model fitting methods are discussed, including selection criteria and implementation procedures. In covering basic concepts and caveats, this short perspective aims to help facilitate active discussion between scientists at different levels with diverse backgrounds.
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Affiliation(s)
- Doo Nam Kim
- Computational Biology Team, Biological Science Division, Pacific Northwest National Laboratory, Richland, Washington, 99354, United States
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Karissa Y. Sanbonmatsu
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, 87545, United States
- New Mexico Consortium, Los Alamos, New Mexico, 87544, United States
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44
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Koehler Leman J, Weitzner BD, Renfrew PD, Lewis SM, Moretti R, Watkins AM, Mulligan VK, Lyskov S, Adolf-Bryfogle J, Labonte JW, Krys J, Bystroff C, Schief W, Gront D, Schueler-Furman O, Baker D, Bradley P, Dunbrack R, Kortemme T, Leaver-Fay A, Strauss CEM, Meiler J, Kuhlman B, Gray JJ, Bonneau R. Better together: Elements of successful scientific software development in a distributed collaborative community. PLoS Comput Biol 2020; 16:e1007507. [PMID: 32365137 PMCID: PMC7197760 DOI: 10.1371/journal.pcbi.1007507] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, United States of America
- Dept of Biology, New York University, New York, NY, United States of America
| | - Brian D. Weitzner
- Dept of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States of America
- Dept of Biochemistry, University of Washington, Seattle, WA, United States of America
- Institute for Protein Design, University of Washington, Seattle, WA, United States of America
- Lyell Immunopharma, Seattle, WA, United States of America
| | - P. Douglas Renfrew
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, United States of America
| | - Steven M. Lewis
- Dept of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
- Dept of Biochemistry, Duke University, Durham, NC, United States of America
- Cyrus Biotechnology, Seattle, WA United States of America
| | - Rocco Moretti
- Dept of Chemistry, Vanderbilt University, Nashville, TN, United States of America
| | - Andrew M. Watkins
- Dept of Biochemistry, Stanford University School of Medicine, Stanford CA, United States of America
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, United States of America
- Dept of Biochemistry, University of Washington, Seattle, WA, United States of America
- Institute for Protein Design, University of Washington, Seattle, WA, United States of America
| | - Sergey Lyskov
- Dept of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - Jared Adolf-Bryfogle
- Dept of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, United States of America
| | - Jason W. Labonte
- Dept of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States of America
- Dept of Chemistry, Franklin & Marshall College, Lancaster, PA, United States of America
| | - Justyna Krys
- Dept of Chemistry, University of Warsaw, Warsaw, Poland
| | | | - Christopher Bystroff
- Dept of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, United States of America
| | - William Schief
- Dept of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, United States of America
| | - Dominik Gront
- Dept of Chemistry, University of Warsaw, Warsaw, Poland
| | - Ora Schueler-Furman
- Dept of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - David Baker
- Dept of Biochemistry, University of Washington, Seattle, WA, United States of America
- Institute for Protein Design, University of Washington, Seattle, WA, United States of America
| | - Philip Bradley
- Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America
| | - Roland Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia PA, United States of America
| | - Tanja Kortemme
- Dept of Bioengineering and Therapeutic Sciences, University of California San Francisco, CA, United States of America
| | - Andrew Leaver-Fay
- Dept of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Charlie E. M. Strauss
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States of America
| | - Jens Meiler
- Depts of Chemistry, Pharmacology and Biomedical Informatics, Vanderbilt University, Nashville, TN, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States of America
- Institute for Chemical Biology, Vanderbilt University, Nashville, TN, United States of America
- Institute for Drug Discovery, Leipzig University, Leipzig, Germany
| | - Brian Kuhlman
- Dept of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Jeffrey J. Gray
- Dept of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, United States of America
- Dept of Biology, New York University, New York, NY, United States of America
- Dept of Computer Science, New York University, New York, NY, United States of America
- Center for Data Science, New York University, New York, NY, United States of America
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45
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Abbass J, Nebel JC. Enhancing fragment-based protein structure prediction by customising fragment cardinality according to local secondary structure. BMC Bioinformatics 2020; 21:170. [PMID: 32357827 PMCID: PMC7195757 DOI: 10.1186/s12859-020-3491-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 04/13/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Whenever suitable template structures are not available, usage of fragment-based protein structure prediction becomes the only practical alternative as pure ab initio techniques require massive computational resources even for very small proteins. However, inaccuracy of their energy functions and their stochastic nature imposes generation of a large number of decoys to explore adequately the solution space, limiting their usage to small proteins. Taking advantage of the uneven complexity of the sequence-structure relationship of short fragments, we adjusted the fragment insertion process by customising the number of available fragment templates according to the expected complexity of the predicted local secondary structure. Whereas the number of fragments is kept to its default value for coil regions, important and dramatic reductions are proposed for beta sheet and alpha helical regions, respectively. RESULTS The evaluation of our fragment selection approach was conducted using an enhanced version of the popular Rosetta fragment-based protein structure prediction tool. It was modified so that the number of fragment candidates used in Rosetta could be adjusted based on the local secondary structure. Compared to Rosetta's standard predictions, our strategy delivered improved first models, + 24% and + 6% in terms of GDT, when using 2000 and 20,000 decoys, respectively, while reducing significantly the number of fragment candidates. Furthermore, our enhanced version of Rosetta is able to deliver with 2000 decoys a performance equivalent to that produced by standard Rosetta while using 20,000 decoys. We hypothesise that, as the fragment insertion process focuses on the most challenging regions, such as coils, fewer decoys are needed to explore satisfactorily conformation spaces. CONCLUSIONS Taking advantage of the high accuracy of sequence-based secondary structure predictions, we showed the value of that information to customise the number of candidates used during the fragment insertion process of fragment-based protein structure prediction. Experimentations conducted using standard Rosetta showed that, when using the recommended number of decoys, i.e. 20,000, our strategy produces better results. Alternatively, similar results can be achieved using only 2000 decoys. Consequently, we recommend the adoption of this strategy to either improve significantly model quality or reduce processing times by a factor 10.
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Affiliation(s)
- Jad Abbass
- Faculty of Science, Engineering and Computing, Kingston University, London, KT1 2EE UK
- Department of Computer Science, Lebanese International University, Bekaa, Lebanon
| | - Jean-Christophe Nebel
- Faculty of Science, Engineering and Computing, Kingston University, London, KT1 2EE UK
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46
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Barnych B, Singh N, Negrel S, Zhang Y, Magis D, Roux C, Hua X, Ding Z, Morisseau C, Tantillo DJ, Siegel JB, Hammock BD. Development of potent inhibitors of the human microsomal epoxide hydrolase. Eur J Med Chem 2020; 193:112206. [PMID: 32203787 DOI: 10.1016/j.ejmech.2020.112206] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 03/03/2020] [Accepted: 03/03/2020] [Indexed: 11/15/2022]
Abstract
Microsomal epoxide hydrolase (mEH) hydrolyzes a wide range of epoxide containing molecules. Although involved in the metabolism of xenobiotics, recent studies associate mEH with the onset and development of certain disease conditions. This phenomenon is partially attributed to the significant role mEH plays in hydrolyzing endogenous lipid mediators, suggesting more complex and extensive physiological functions. In order to obtain pharmacological tools to further study the biology and therapeutic potential of this enzyme target, we describe the development of highly potent 2-alkylthio acetamide inhibitors of the human mEH with IC50 values in the low nanomolar range. These are around 2 orders of magnitude more potent than previously obtained primary amine, amide and urea-based mEH inhibitors. Experimental assay results and rationalization of binding through docking calculations of inhibitors to a mEH homology model indicate that an amide connected to an alkyl side chain and a benzyl-thio function as key pharmacophore units.
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Affiliation(s)
- Bogdan Barnych
- Department of Entomology and Nematology, UCD Comprehensive Cancer Center, University of California Davis, Davis, CA, 95616, United States
| | - Nalin Singh
- Department of Entomology and Nematology, UCD Comprehensive Cancer Center, University of California Davis, Davis, CA, 95616, United States
| | - Sophie Negrel
- Department of Entomology and Nematology, UCD Comprehensive Cancer Center, University of California Davis, Davis, CA, 95616, United States
| | - Yue Zhang
- Department of Chemistry, University of California Davis, Davis, CA, 95616, United States
| | - Damien Magis
- Department of Entomology and Nematology, UCD Comprehensive Cancer Center, University of California Davis, Davis, CA, 95616, United States
| | - Capucine Roux
- Department of Entomology and Nematology, UCD Comprehensive Cancer Center, University of California Davis, Davis, CA, 95616, United States
| | - Xiude Hua
- Department of Entomology and Nematology, UCD Comprehensive Cancer Center, University of California Davis, Davis, CA, 95616, United States; College of Plant Protection, Nanjing Agricultural University, Nanjing, 210095, China
| | - Zhewen Ding
- Department of Entomology and Nematology, UCD Comprehensive Cancer Center, University of California Davis, Davis, CA, 95616, United States
| | - Christophe Morisseau
- Department of Entomology and Nematology, UCD Comprehensive Cancer Center, University of California Davis, Davis, CA, 95616, United States
| | - Dean J Tantillo
- Department of Chemistry, University of California Davis, Davis, CA, 95616, United States
| | - Justin B Siegel
- Department of Chemistry, University of California Davis, Davis, CA, 95616, United States; Department of Biochemistry and Molecular Medicine, University of California Davis, Davis, CA, 95616, United States; Genome Center, University of California Davis, Davis, CA, 95616, United States
| | - Bruce D Hammock
- Department of Entomology and Nematology, UCD Comprehensive Cancer Center, University of California Davis, Davis, CA, 95616, United States.
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47
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Abstract
Modeling the tertiary structure of protein-protein interaction complex has been well studied over many years, especially in the case where the structures of both binding partners are roughly the same before and after binding. However, the assembly of complexes with less-ordered partners is a much harder problem, and modeling even small amounts of flexibility can pose a challenge. In an extreme case, where one of the binding partners is intrinsically disordered before binding, we have previously shown that by initially disregarding the coupling between windows of these intrinsically disordered proteins (IDPs), we can reliably assemble complexes involving IDPs up to at least 69 residues long. Here, we detail the use of the IDP-LZerD package and protocol.
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48
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Contreras S, Bertolani SJ, Siegel JB. A Benchmark for Homomeric Enzyme Active Site Structure Prediction Highlights the Importance of Accurate Modeling of Protein Symmetry. ACS OMEGA 2019; 4:22356-22362. [PMID: 31909318 PMCID: PMC6941179 DOI: 10.1021/acsomega.9b02636] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 12/04/2019] [Indexed: 05/15/2023]
Abstract
Accurate prediction and modeling of an enzyme's active site are critical for engineering efforts as well as providing insight into an enzyme's naturally occurring function. Previous efforts demonstrated that the integration of constraints enforcing strict geometric orientations between catalytic residues significantly improved the modeling accuracy for the active sites of monomeric enzymes. In this study, a similar approach was explored to evaluate the effect on the active sites of homomeric enzymes. A benchmark of 17 homomeric enzymes with known structures and a bound ligand relevant to the established chemistry were identified from the protein data bank. The enzymes identified span multiple classes as well as symmetries. Unlike what was observed for the monomeric enzymes, upon the application of catalytic geometric constraints, there was no significant improvement observed in modeling accuracy for either the active site of the protein structure or the accuracy of the subsequently docked ligand. Upon further analysis, it is apparent that the symmetric interface being modeled is inaccurate and prevented the active sites from being modeled at atomic-level accuracy. This is consistent with the challenge others have identified in being able to predict de novo protein symmetry. To further improve the accuracy of active site modeling for homomeric proteins, new methodologies to accurately model the symmetric interfaces of these complexes are needed.
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Affiliation(s)
- Stephanie
C. Contreras
- Department
of Chemistry, Department of Biochemistry and Molecular Medicine, and Genome Center, University of California, Davis, Davis, California 95616, United States
| | - Steve J. Bertolani
- Department
of Chemistry, Department of Biochemistry and Molecular Medicine, and Genome Center, University of California, Davis, Davis, California 95616, United States
| | - Justin B. Siegel
- Department
of Chemistry, Department of Biochemistry and Molecular Medicine, and Genome Center, University of California, Davis, Davis, California 95616, United States
- E-mail:
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49
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Close DM, Cooper CJ, Wang X, Chirania P, Gupta M, Ossyra JR, Giannone RJ, Engle N, Tschaplinski TJ, Smith JC, Hedstrom L, Parks JM, Michener JK. Horizontal transfer of a pathway for coumarate catabolism unexpectedly inhibits purine nucleotide biosynthesis. Mol Microbiol 2019; 112:1784-1797. [PMID: 31532038 DOI: 10.1111/mmi.14393] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/16/2019] [Indexed: 11/28/2022]
Abstract
A microbe's ecological niche and biotechnological utility are determined by its specific set of co-evolved metabolic pathways. The acquisition of new pathways, through horizontal gene transfer or genetic engineering, can have unpredictable consequences. Here we show that two different pathways for coumarate catabolism failed to function when initially transferred into Escherichia coli. Using laboratory evolution, we elucidated the factors limiting activity of the newly acquired pathways and the modifications required to overcome these limitations. Both pathways required host mutations to enable effective growth with coumarate, but the necessary mutations differed. In one case, a pathway intermediate inhibited purine nucleotide biosynthesis, and this inhibition was relieved by single amino acid replacements in IMP dehydrogenase. A strain that natively contains this coumarate catabolism pathway, Acinetobacter baumannii, is resistant to inhibition by the relevant intermediate, suggesting that natural pathway transfers have faced and overcome similar challenges. Molecular dynamics simulation of the wild type and a representative single-residue mutant provide insight into the structural and dynamic changes that relieve inhibition. These results demonstrate how deleterious interactions can limit pathway transfer, that these interactions can be traced to specific molecular interactions between host and pathway, and how evolution or engineering can alleviate these limitations.
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Affiliation(s)
- Dan M Close
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Connor J Cooper
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996, USA
| | - Xingyou Wang
- Graduate Program in Chemistry, Brandeis University, 415 South Street, Waltham, MA, 02454, USA
| | - Payal Chirania
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996, USA.,Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA
| | - Madhulika Gupta
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - John R Ossyra
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996, USA
| | - Richard J Giannone
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA.,BioEnergy Science Center, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA.,Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA
| | - Nancy Engle
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.,BioEnergy Science Center, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA.,Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA
| | - Timothy J Tschaplinski
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.,BioEnergy Science Center, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA.,Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA
| | - Jeremy C Smith
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.,Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee Knoxville, Knoxville, Tennessee, 37996, USA
| | - Lizbeth Hedstrom
- Department of Biology, Brandeis University, 415 South Street, Waltham, MA, 02454, USA.,Department of Chemistry, Brandeis University, 415 South Street, Waltham, MA, 02454, USA
| | - Jerry M Parks
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.,Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996, USA
| | - Joshua K Michener
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.,BioEnergy Science Center, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA.,Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA
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50
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Cross KL, Campbell JH, Balachandran M, Campbell AG, Cooper SJ, Griffen A, Heaton M, Joshi S, Klingeman D, Leys E, Yang Z, Parks JM, Podar M. Targeted isolation and cultivation of uncultivated bacteria by reverse genomics. Nat Biotechnol 2019; 37:1314-1321. [PMID: 31570900 PMCID: PMC6858544 DOI: 10.1038/s41587-019-0260-6] [Citation(s) in RCA: 164] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2019] [Accepted: 08/15/2019] [Indexed: 12/16/2022]
Abstract
Most microorganisms from all taxonomic levels are uncultured. Single-cell
genomes and metagenomes continue to increase the known diversity of
Bacteria and Archaea, but while
‘omics can be used to infer physiological or ecological roles for species
in a community, most of those hypothetical roles remain unvalidated. Here we
report an approach to capture specific microorganisms from complex communities
into pure cultures using genome-informed antibody engineering. We apply our
reverse genomics approach to isolate and sequence single cells and to cultivate
three different species-level lineages of human oral Saccharibacteria/TM7. Using
our pure cultures we show that all three saccharibacteria species are epibionts
of diverse Actinobacteria. We also isolate and cultivate human
oral SR1 bacteria, which are members of a lineage of previously uncultured
bacteria. Reverse-genomics-enabled cultivation of microorganisms can be applied
to any species from any environment and has the potential to unlock the
isolation, cultivation and characterization of species from as-yet-uncultured
branches of the microbial tree of life.
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Affiliation(s)
- Karissa L Cross
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.,Department of Microbiology, University of Tennessee, Knoxville, TN, USA
| | - James H Campbell
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.,Department of Natural Sciences, Northwest Missouri State University, Maryville, MO, USA
| | | | - Alisha G Campbell
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.,Genome Science and Technology Program, University of Tennessee, Knoxville, TN, USA.,Department of Natural Sciences, Northwest Missouri State University, Maryville, MO, USA
| | - Sarah J Cooper
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.,Genome Science and Technology Program, University of Tennessee, Knoxville, TN, USA
| | - Ann Griffen
- College of Dentistry, The Ohio State University, Columbus, OH, USA
| | | | - Snehal Joshi
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Dawn Klingeman
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Eugene Leys
- College of Dentistry, The Ohio State University, Columbus, OH, USA
| | - Zamin Yang
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Jerry M Parks
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.,Genome Science and Technology Program, University of Tennessee, Knoxville, TN, USA
| | - Mircea Podar
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA. .,Department of Microbiology, University of Tennessee, Knoxville, TN, USA. .,Genome Science and Technology Program, University of Tennessee, Knoxville, TN, USA.
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