1
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Zhang C, Freddolino L. FURNA: A database for functional annotations of RNA structures. PLoS Biol 2024; 22:e3002476. [PMID: 39074139 DOI: 10.1371/journal.pbio.3002476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 06/24/2024] [Indexed: 07/31/2024] Open
Abstract
Despite the increasing number of 3D RNA structures in the Protein Data Bank, the majority of experimental RNA structures lack thorough functional annotations. As the significance of the functional roles played by noncoding RNAs becomes increasingly apparent, comprehensive annotation of RNA function is becoming a pressing concern. In response to this need, we have developed FURNA (Functions of RNAs), the first database for experimental RNA structures that aims to provide a comprehensive repository of high-quality functional annotations. These include Gene Ontology terms, Enzyme Commission numbers, ligand-binding sites, RNA families, protein-binding motifs, and cross-references to related databases. FURNA is available at https://seq2fun.dcmb.med.umich.edu/furna/ to enable quick discovery of RNA functions from their structures and sequences.
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Affiliation(s)
- Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Lydia Freddolino
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, United States of America
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2
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Zhang C, Zhang X, Freddolino P, Zhang Y. BioLiP2: an updated structure database for biologically relevant ligand-protein interactions. Nucleic Acids Res 2024; 52:D404-D412. [PMID: 37522378 PMCID: PMC10767969 DOI: 10.1093/nar/gkad630] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/03/2023] [Accepted: 07/17/2023] [Indexed: 08/01/2023] Open
Abstract
With the progress of structural biology, the Protein Data Bank (PDB) has witnessed rapid accumulation of experimentally solved protein structures. Since many structures are determined with purification and crystallization additives that are unrelated to a protein's in vivo function, it is nontrivial to identify the subset of protein-ligand interactions that are biologically relevant. We developed the BioLiP2 database (https://zhanggroup.org/BioLiP) to extract biologically relevant protein-ligand interactions from the PDB database. BioLiP2 assesses the functional relevance of the ligands by geometric rules and experimental literature validations. The ligand binding information is further enriched with other function annotations, including Enzyme Commission numbers, Gene Ontology terms, catalytic sites, and binding affinities collected from other databases and a manual literature survey. Compared to its predecessor BioLiP, BioLiP2 offers significantly greater coverage of nucleic acid-protein interactions, and interactions involving large complexes that are unavailable in PDB format. BioLiP2 also integrates cutting-edge structural alignment algorithms with state-of-the-art structure prediction techniques, which for the first time enables composite protein structure and sequence-based searching and significantly enhances the usefulness of the database in structure-based function annotations. With these new developments, BioLiP2 will continue to be an important and comprehensive database for docking, virtual screening, and structure-based protein function analyses.
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Affiliation(s)
- Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xi Zhang
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Peter L Freddolino
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Computer Science, School of Computing, National University of Singapore, 117417, Singapore
- Cancer Science Institute of Singapore, National University of Singapore,117599, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 117596, Singapore
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3
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Zhang C, Freddolino PL. FURNA: a database for function annotations of RNA structures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.19.572314. [PMID: 38187637 PMCID: PMC10769261 DOI: 10.1101/2023.12.19.572314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Despite the increasing number of 3D RNA structures in the Protein Data Bank, the majority of experimental RNA structures lack thorough functional annotations. As the significance of the functional roles played by non-coding RNAs becomes increasingly apparent, comprehensive annotation of RNA function is becoming a pressing concern. In response to this need, we have developed FURNA (Functions of RNAs), the first database for experimental RNA structures that aims to provide a comprehensive repository of high-quality functional annotations. These include Gene Ontology terms, Enzyme Commission numbers, ligand binding sites, RNA families, protein binding motifs, and cross-references to related databases. FURNA is available at https://seq2fun.dcmb.med.umich.edu/furna/ to enable quick discovery of RNA functions from their structures and sequences.
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Affiliation(s)
- Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
| | - P. Lydia Freddolino
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
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4
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Tubiana J, Schneidman-Duhovny D, Wolfson HJ. ScanNet: A web server for structure-based prediction of protein binding sites with geometric deep learning. J Mol Biol 2022; 434:167758. [DOI: 10.1016/j.jmb.2022.167758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 07/18/2022] [Accepted: 07/19/2022] [Indexed: 11/28/2022]
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5
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ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction. Nat Methods 2022; 19:730-739. [DOI: 10.1038/s41592-022-01490-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 04/12/2022] [Indexed: 11/08/2022]
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6
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Rouhani M, Hadi-Alijanvand H. Effect of Lithium Drug on Binding Affinities of Glycogen Synthase Kinase-3 β to Its Network Partners: A New Computational Approach. J Chem Inf Model 2021; 61:5280-5292. [PMID: 34533953 DOI: 10.1021/acs.jcim.1c00952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Finding new methods to study the effect of small molecules on protein interaction networks provides us with invaluable tools in the fields of pharmacodynamics and drug design. Lithium is an antimanic drug that has been used for the treatment of bipolar disorder for more than 60 years. Here, we utilized a new approach to study the effect of lithium as a drug on the protein interaction network of GSK-3β as a hub protein and computed the affinities of GSK-3β to its partners in the presence of lithium or sodium ions. For this purpose, ensembles of GSK-3β protein structures were created in the presence of either lithium or sodium ions using adaptive tempering molecular dynamics simulations. The protein binding patches of GSK-3β for its partners were determined, and finally, the affinity of each binding patch to the related partner was computed for structures of ensembles using a monomer-based approach. Besides, by comparing structural dynamics of GSK-3β during MD simulations in the presence of LiCl and NaCl, we suggested a new mechanism for the inhibitory effect of lithium on GSK-3β.
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Affiliation(s)
- Maryam Rouhani
- Department of Biological Sciences, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan 45137-66731, Iran
| | - Hamid Hadi-Alijanvand
- Department of Biological Sciences, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan 45137-66731, Iran
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7
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Das S, Scholes HM, Sen N, Orengo C. CATH functional families predict functional sites in proteins. Bioinformatics 2021; 37:1099-1106. [PMID: 33135053 PMCID: PMC8150129 DOI: 10.1093/bioinformatics/btaa937] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 09/30/2020] [Accepted: 10/27/2020] [Indexed: 01/12/2023] Open
Abstract
MOTIVATION Identification of functional sites in proteins is essential for functional characterization, variant interpretation and drug design. Several methods are available for predicting either a generic functional site, or specific types of functional site. Here, we present FunSite, a machine learning predictor that identifies catalytic, ligand-binding and protein-protein interaction functional sites using features derived from protein sequence and structure, and evolutionary data from CATH functional families (FunFams). RESULTS FunSite's prediction performance was rigorously benchmarked using cross-validation and a holdout dataset. FunSite outperformed other publicly available functional site prediction methods. We show that conserved residues in FunFams are enriched in functional sites. We found FunSite's performance depends greatly on the quality of functional site annotations and the information content of FunFams in the training data. Finally, we analyze which structural and evolutionary features are most predictive for functional sites. AVAILABILITYAND IMPLEMENTATION https://github.com/UCL/cath-funsite-predictor. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sayoni Das
- PrecisionLife Ltd., Long Hanborough, OX29 8LJ Oxford, UK
| | - Harry M Scholes
- Institute of Structural and Molecular Biology, University College London, WC1E 6BT, London, UK
| | - Neeladri Sen
- Institute of Structural and Molecular Biology, University College London, WC1E 6BT, London, UK
| | - Christine Orengo
- Institute of Structural and Molecular Biology, University College London, WC1E 6BT, London, UK
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8
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Lam SD, Babu MM, Lees J, Orengo CA. Biological impact of mutually exclusive exon switching. PLoS Comput Biol 2021; 17:e1008708. [PMID: 33651795 PMCID: PMC7954323 DOI: 10.1371/journal.pcbi.1008708] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 03/12/2021] [Accepted: 01/14/2021] [Indexed: 12/27/2022] Open
Abstract
Alternative splicing can expand the diversity of proteomes. Homologous mutually exclusive exons (MXEs) originate from the same ancestral exon and result in polypeptides with similar structural properties but altered sequence. Why would some genes switch homologous exons and what are their biological impact? Here, we analyse the extent of sequence, structural and functional variability in MXEs and report the first large scale, structure-based analysis of the biological impact of MXE events from different genomes. MXE-specific residues tend to map to single domains, are highly enriched in surface exposed residues and cluster at or near protein functional sites. Thus, MXE events are likely to maintain the protein fold, but alter specificity and selectivity of protein function. This comprehensive resource of MXE events and their annotations is available at: http://gene3d.biochem.ucl.ac.uk/mxemod/. These findings highlight how small, but significant changes at critical positions on a protein surface are exploited in evolution to alter function.
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Affiliation(s)
- Su Datt Lam
- Institute of Structural and Molecular Biology, University College London, Darwin Building, Gower Street, London, United Kingdom
- Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia
- * E-mail: (SDL); (JL); (CO)
| | - M. Madan Babu
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Structural Biology and Center for Data Driven Discovery, St Jude Children’s Research Hospital, Memphis, Tennessee, United States of America
| | - Jonathan Lees
- Faculty of Health and Life Sciences, Oxford Brookes University, Oxford, United Kingdom
- * E-mail: (SDL); (JL); (CO)
| | - Christine A. Orengo
- Institute of Structural and Molecular Biology, University College London, Darwin Building, Gower Street, London, United Kingdom
- * E-mail: (SDL); (JL); (CO)
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9
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Slater O, Miller B, Kontoyianni M. Decoding Protein-protein Interactions: An Overview. Curr Top Med Chem 2021; 20:855-882. [PMID: 32101126 DOI: 10.2174/1568026620666200226105312] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Revised: 11/27/2019] [Accepted: 11/27/2019] [Indexed: 12/24/2022]
Abstract
Drug discovery has focused on the paradigm "one drug, one target" for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.
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Affiliation(s)
- Olivia Slater
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
| | - Bethany Miller
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
| | - Maria Kontoyianni
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
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10
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Dar KB, Bhat AH, Amin S, Anjum S, Reshi BA, Zargar MA, Masood A, Ganie SA. Exploring Proteomic Drug Targets, Therapeutic Strategies and Protein - Protein Interactions in Cancer: Mechanistic View. Curr Cancer Drug Targets 2020; 19:430-448. [PMID: 30073927 DOI: 10.2174/1568009618666180803104631] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2018] [Revised: 07/17/2018] [Accepted: 07/19/2018] [Indexed: 12/31/2022]
Abstract
Protein-Protein Interactions (PPIs) drive major signalling cascades and play critical role in cell proliferation, apoptosis, angiogenesis and trafficking. Deregulated PPIs are implicated in multiple malignancies and represent the critical targets for treating cancer. Herein, we discuss the key protein-protein interacting domains implicated in cancer notably PDZ, SH2, SH3, LIM, PTB, SAM and PH. These domains are present in numerous enzymes/kinases, growth factors, transcription factors, adaptor proteins, receptors and scaffolding proteins and thus represent essential sites for targeting cancer. This review explores the candidature of various proteins involved in cellular trafficking (small GTPases, molecular motors, matrix-degrading enzymes, integrin), transcription (p53, cMyc), signalling (membrane receptor proteins), angiogenesis (VEGFs) and apoptosis (BCL-2family), which could possibly serve as targets for developing effective anti-cancer regimen. Interactions between Ras/Raf; X-linked inhibitor of apoptosis protein (XIAP)/second mitochondria-derived activator of caspases (Smac/DIABLO); Frizzled (FRZ)/Dishevelled (DVL) protein; beta-catenin/T Cell Factor (TCF) have also been studied as prospective anticancer targets. Efficacy of diverse molecules/ drugs targeting such PPIs although evaluated in various animal models/cell lines, there is an essential need for human-based clinical trials. Therapeutic strategies like the use of biologicals, high throughput screening (HTS) and fragment-based technology could play an imperative role in designing cancer therapeutics. Moreover, bioinformatic/computational strategies based on genome sequence, protein sequence/structure and domain data could serve as competent tools for predicting PPIs. Exploring hot spots in proteomic networks represents another approach for developing targetspecific therapeutics. Overall, this review lays emphasis on a productive amalgamation of proteomics, genomics, biochemistry, and molecular dynamics for successful treatment of cancer.
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Affiliation(s)
- Khalid Bashir Dar
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India.,Department of Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India
| | - Aashiq Hussain Bhat
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India.,Department of Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India
| | - Shajrul Amin
- Department of Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India
| | - Syed Anjum
- Amity Institute of Biotechnology, Amity University, Rajasthan, India
| | - Bilal Ahmad Reshi
- Department of Biotechnology, School of Biological Sciences, University of Kashmir, Srinagar, India
| | - Mohammad Afzal Zargar
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India
| | - Akbar Masood
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India
| | - Showkat Ahmad Ganie
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India
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11
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Sulakhe D, D'Souza M, Wang S, Balasubramanian S, Athri P, Xie B, Canzar S, Agam G, Gilliam TC, Maltsev N. Exploring the functional impact of alternative splicing on human protein isoforms using available annotation sources. Brief Bioinform 2020; 20:1754-1768. [PMID: 29931155 DOI: 10.1093/bib/bby047] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 05/02/2018] [Indexed: 12/30/2022] Open
Abstract
In recent years, the emphasis of scientific inquiry has shifted from whole-genome analyses to an understanding of cellular responses specific to tissue, developmental stage or environmental conditions. One of the central mechanisms underlying the diversity and adaptability of the contextual responses is alternative splicing (AS). It enables a single gene to encode multiple isoforms with distinct biological functions. However, to date, the functions of the vast majority of differentially spliced protein isoforms are not known. Integration of genomic, proteomic, functional, phenotypic and contextual information is essential for supporting isoform-based modeling and analysis. Such integrative proteogenomics approaches promise to provide insights into the functions of the alternatively spliced protein isoforms and provide high-confidence hypotheses to be validated experimentally. This manuscript provides a survey of the public databases supporting isoform-based biology. It also presents an overview of the potential global impact of AS on the human canonical gene functions, molecular interactions and cellular pathways.
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Affiliation(s)
- Dinanath Sulakhe
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA.,Computation Institute, University of Chicago, 5735 S. Ellis Avenue, Chicago, IL, USA
| | - Mark D'Souza
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA
| | - Sheng Wang
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA.,Toyota Technological Institute at Chicago, 6045 S. Kenwood Avenue, Chicago, IL, USA
| | - Sandhya Balasubramanian
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA.,Genentech, Inc. 1 DNA Way, Mail Stop: 35-6J, South San Francisco, CA, USA
| | - Prashanth Athri
- Department of Computer Science and Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, Kasavanahalli, Carmelaram P.O., Bengaluru, Karnataka, India
| | - Bingqing Xie
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA.,Department of Computer Science, Illinois Institute of Technology, Chicago, IL, USA
| | - Stefan Canzar
- Toyota Technological Institute at Chicago, 6045 S. Kenwood Avenue, Chicago, IL, USA.,Gene Center, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Gady Agam
- Department of Computer Science, Illinois Institute of Technology, Chicago, IL, USA
| | - T Conrad Gilliam
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA.,Computation Institute, University of Chicago, 5735 S. Ellis Avenue, Chicago, IL, USA
| | - Natalia Maltsev
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA.,Computation Institute, University of Chicago, 5735 S. Ellis Avenue, Chicago, IL, USA
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12
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Andreani J, Quignot C, Guerois R. Structural prediction of protein interactions and docking using conservation and coevolution. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1470] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Jessica Andreani
- Université Paris‐Saclay CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC) Gif‐sur‐Yvette France
| | - Chloé Quignot
- Université Paris‐Saclay CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC) Gif‐sur‐Yvette France
| | - Raphael Guerois
- Université Paris‐Saclay CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC) Gif‐sur‐Yvette France
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13
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Zhang N, Chen Y, Lu H, Zhao F, Alvarez RV, Goncearenco A, Panchenko AR, Li M. MutaBind2: Predicting the Impacts of Single and Multiple Mutations on Protein-Protein Interactions. iScience 2020; 23:100939. [PMID: 32169820 PMCID: PMC7068639 DOI: 10.1016/j.isci.2020.100939] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 11/21/2019] [Accepted: 02/20/2020] [Indexed: 01/17/2023] Open
Abstract
Missense mutations may affect proteostasis by destabilizing or over-stabilizing protein complexes and changing the pathway flux. Predicting the effects of stabilizing mutations on protein-protein interactions is notoriously difficult because existing experimental sets are skewed toward mutations reducing protein-protein binding affinity and many computational methods fail to correctly evaluate their effects. To address this issue, we developed a method MutaBind2, which estimates the impacts of single as well as multiple mutations on protein-protein interactions. MutaBind2 employs only seven features, and the most important of them describe interactions of proteins with the solvent, evolutionary conservation of the site, and thermodynamic stability of the complex and each monomer. This approach shows a distinct improvement especially in evaluating the effects of mutations increasing binding affinity. MutaBind2 can be used for finding disease driver mutations, designing stable protein complexes, and discovering new protein-protein interaction inhibitors.
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Affiliation(s)
- Ning Zhang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Yuting Chen
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Haoyu Lu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Feiyang Zhao
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Roberto Vera Alvarez
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA
| | - Alexander Goncearenco
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA
| | - Anna R Panchenko
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.
| | - Minghui Li
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China.
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14
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ProtCID: a data resource for structural information on protein interactions. Nat Commun 2020; 11:711. [PMID: 32024829 PMCID: PMC7002494 DOI: 10.1038/s41467-020-14301-4] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 12/13/2019] [Indexed: 12/16/2022] Open
Abstract
Structural information on the interactions of proteins with other molecules is plentiful, and for some proteins and protein families, there may be 100s of available structures. It can be very difficult for a scientist who is not trained in structural bioinformatics to access this information comprehensively. Previously, we developed the Protein Common Interface Database (ProtCID), which provided clusters of the interfaces of full-length protein chains as a means of identifying biological assemblies. Because proteins consist of domains that act as modular functional units, we have extended the analysis in ProtCID to the individual domain level. This has greatly increased the number of large protein-protein clusters in ProtCID, enabling the generation of hypotheses on the structures of biological assemblies of many systems. The analysis of domain families allows us to extend ProtCID to the interactions of domains with peptides, nucleic acids, and ligands. ProtCID provides complete annotations and coordinate sets for every cluster. The authors previously developed the Protein Common Interface Database (ProtCID), which compares and clusters the interfaces of pairs of full-length protein chains with defined Pfam domain architectures in different PDB entries to identify biological assemblies. Here the authors extend ProtCID to the clustering of domain-domain interactions that also allows analyzing domain interactions with peptides, nucleic acids, and ligands.
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15
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Hadi-Alijanvand H. Complex Stability is Encoded in Binding Patch Softness: a Monomer-Based Approach to Predict Inter-Subunit Affinity of Protein Dimers. J Proteome Res 2019; 19:409-423. [PMID: 31795635 DOI: 10.1021/acs.jproteome.9b00594] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Knowledge about the structure and stability of protein-protein interactions is vital to decipher the behavior of protein systems. Prediction of protein complexes' stability is an interesting topic in the field of structural biology. There are some promising published computational approaches that predict the affinity between subunits of protein dimers using 3D structures of both subunits. In the current study, we classify protein complexes with experimentally measured affinities into distinct classes with different mean affinities. By predicting the mechanical stiffness of the protein binding patch (PBP) region on a single subunit, we successfully predict the assigned affinity class of the PBP in the classification step. Now to predict the experimentally measured affinity between protein monomers in solution, we just need the 3D structure of the suggested PBP on one subunit of the proposed dimer. We designed the SEPAS software and have made the software freely available for academic non-commercial research purposes at " http://biophysics.ir/affinity ". SEPAS predicts the stability of the intended dimer in a classwise manner by utilizing the computed mechanical stiffness of the introduced binding site on one subunit with the minimum accuracy of 0.72.
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Affiliation(s)
- Hamid Hadi-Alijanvand
- Department of Biological Sciences , Institute for Advanced Studies in Basic Sciences (IASBS) , Zanjan 45137-66731 , Iran
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16
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Kim D, Han SK, Lee K, Kim I, Kong J, Kim S. Evolutionary coupling analysis identifies the impact of disease-associated variants at less-conserved sites. Nucleic Acids Res 2019; 47:e94. [PMID: 31199866 PMCID: PMC6895274 DOI: 10.1093/nar/gkz536] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Revised: 05/03/2019] [Accepted: 06/05/2019] [Indexed: 12/20/2022] Open
Abstract
Genome-wide association studies have discovered a large number of genetic variants in human patients with the disease. Thus, predicting the impact of these variants is important for sorting disease-associated variants (DVs) from neutral variants. Current methods to predict the mutational impacts depend on evolutionary conservation at the mutation site, which is determined using homologous sequences and based on the assumption that variants at well-conserved sites have high impacts. However, many DVs at less-conserved but functionally important sites cannot be predicted by the current methods. Here, we present a method to find DVs at less-conserved sites by predicting the mutational impacts using evolutionary coupling analysis. Functionally important and evolutionarily coupled sites often have compensatory variants on cooperative sites to avoid loss of function. We found that our method identified known intolerant variants in a diverse group of proteins. Furthermore, at less-conserved sites, we identified DVs that were not identified using conservation-based methods. These newly identified DVs were frequently found at protein interaction interfaces, where species-specific mutations often alter interaction specificity. This work presents a means to identify less-conserved DVs and provides insight into the relationship between evolutionarily coupled sites and human DVs.
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Affiliation(s)
- Donghyo Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea
| | - Seong Kyu Han
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea
| | - Kwanghwan Lee
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea
| | - Inhae Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea
| | - JungHo Kong
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea
| | - Sanguk Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea
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17
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Murugan A, Prathiviraj R, Mothay D, Chellapandi P. Substrate-imprinted docking of Agrobacterium tumefaciens uronate dehydrogenase for increased substrate selectivity. Int J Biol Macromol 2019; 140:1214-1225. [PMID: 31472210 DOI: 10.1016/j.ijbiomac.2019.08.194] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 08/15/2019] [Accepted: 08/22/2019] [Indexed: 11/18/2022]
Abstract
Agrobacterium tumefaciens uronate dehydrogenase (AtuUdh) belongs to the short-chain dehydrogenase superfamily, specifically those acting on the CH-OH group of donor with NAD+ or NADP+ as acceptor. It is apparently required for the production of D-glucaric acid. AtuUdh-catalyzed reaction is reversible with dual substrate-specific activity (D-galacturonic acid and D-glucuronic acid) in nature. In our study, 34 mutants were pre-screened from 155 mutants generated from AtuUdh (wild-type) and selected 10 structurally stable mutants with increased substrate selectivity. The specificity, efficiency, and selectivity of these mutants for different substrates and cofactors were predicted from 121 docked models using a substrate-imprinted docking approach. Q14F, S36L, and S75T mutants have shown a high binding affinity to D-glucuronic acid and its substrate intermediates such as D-glucaro-1,4-lactone and D-glucaro-1,5-lactone. These mutants exhibited a low binding affinity to the substrate and cofactor required for D-galactaric acid. D34S, N112E and S165E mutants found to show a high selectivity of D-galacturonic acid and its substrate intermediates for D-galactaric acid production. Ser75, Ser165, and Arg174 are active residues playing an imperative role in the substrate selectivity and also contributed in the conjecture the mechanism of transition state stabilization catalyzed by AtuUdh mutants. The present approach was used to reveal the substrate binding mechanism of AtuUdh mutants for a better understanding of the structural basis for selectivity and function.
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Affiliation(s)
- A Murugan
- Molecular Systems Engineering Lab, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli 620024, Tamil Nadu, India
| | - R Prathiviraj
- Molecular Systems Engineering Lab, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli 620024, Tamil Nadu, India
| | - Dipti Mothay
- Molecular Systems Engineering Lab, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli 620024, Tamil Nadu, India
| | - P Chellapandi
- Molecular Systems Engineering Lab, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli 620024, Tamil Nadu, India.
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18
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Pagel KA, Antaki D, Lian A, Mort M, Cooper DN, Sebat J, Iakoucheva LM, Mooney SD, Radivojac P. Pathogenicity and functional impact of non-frameshifting insertion/deletion variation in the human genome. PLoS Comput Biol 2019; 15:e1007112. [PMID: 31199787 PMCID: PMC6594643 DOI: 10.1371/journal.pcbi.1007112] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 06/26/2019] [Accepted: 05/17/2019] [Indexed: 11/19/2022] Open
Abstract
Differentiation between phenotypically neutral and disease-causing genetic variation remains an open and relevant problem. Among different types of variation, non-frameshifting insertions and deletions (indels) represent an understudied group with widespread phenotypic consequences. To address this challenge, we present a machine learning method, MutPred-Indel, that predicts pathogenicity and identifies types of functional residues impacted by non-frameshifting insertion/deletion variation. The model shows good predictive performance as well as the ability to identify impacted structural and functional residues including secondary structure, intrinsic disorder, metal and macromolecular binding, post-translational modifications, allosteric sites, and catalytic residues. We identify structural and functional mechanisms impacted preferentially by germline variation from the Human Gene Mutation Database, recurrent somatic variation from COSMIC in the context of different cancers, as well as de novo variants from families with autism spectrum disorder. Further, the distributions of pathogenicity prediction scores generated by MutPred-Indel are shown to differentiate highly recurrent from non-recurrent somatic variation. Collectively, we present a framework to facilitate the interrogation of both pathogenicity and the functional effects of non-frameshifting insertion/deletion variants. The MutPred-Indel webserver is available at http://mutpred.mutdb.org/.
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Affiliation(s)
- Kymberleigh A. Pagel
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, United States of America
| | - Danny Antaki
- Department of Psychiatry, University of California San Diego, La Jolla, California, United States of America
| | - AoJie Lian
- Department of Psychiatry, University of California San Diego, La Jolla, California, United States of America
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Matthew Mort
- Institute of Medical Genetics, Cardiff University, Cardiff, United Kingdom
| | - David N. Cooper
- Institute of Medical Genetics, Cardiff University, Cardiff, United Kingdom
| | - Jonathan Sebat
- Department of Psychiatry, University of California San Diego, La Jolla, California, United States of America
| | - Lilia M. Iakoucheva
- Department of Psychiatry, University of California San Diego, La Jolla, California, United States of America
| | - Sean D. Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, United States of America
| | - Predrag Radivojac
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, United States of America
- Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts, United States of America
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19
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Pharmacological and molecular dynamics analyses of differences in inhibitor binding to human and nematode PDE4: Implications for management of parasitic nematodes. PLoS One 2019; 14:e0214554. [PMID: 30917179 PMCID: PMC6436744 DOI: 10.1371/journal.pone.0214554] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Accepted: 03/14/2019] [Indexed: 11/19/2022] Open
Abstract
Novel chemical controls are needed that selectively target human, animal, and plant parasitic nematodes with reduced adverse effects on the host or the environment. We hypothesize that the phosphodiesterase (PDE) enzyme family represents a potential target for development of novel nematicides and anthelmintics. To test this, we identified six PDE families present in the nematode phylum that are orthologous to six of the eleven human PDE families. We characterized the binding interactions of family-selective PDE inhibitors with human and C. elegans PDE4 in conjunction with molecular dynamics (MD) simulations to evaluate differences in binding interactions of these inhibitors within the PDE4 catalytic domain. We observed that roflumilast (human PDE4-selective inhibitor) and zardaverine (selective for human PDE3 and PDE4) were 159- and 77-fold less potent, respectively, in inhibiting C. elegans PDE4. The pan-specific PDE inhibitor isobutyl methyl xanthine (IBMX) had similar affinity for nematode and human PDE4. Of 32 residues within 5 Å of the ligand binding site, five revealed significant differences in non-bonded interaction energies (van der Waals and electrostatic interaction energies) that could account for the differential binding affinities of roflumilast and zardaverine. One site (Phe506 in the human PDE4D3 amino acid sequence corresponding to Tyr253 in C. elegans PDE4) is predicted to alter the binding conformation of roflumilast and zardaverine (but not IBMX) into a less energetically favorable state for the nematode enzyme. The pharmacological differences in sensitivity to PDE4 inhibitors in conjunction with differences in the amino acids comprising the inhibitor binding sites of human and C. elegans PDE4 catalytic domains together support the feasibility of designing the next generation of anthelmintics/nematicides that could selectively bind to nematode PDEs.
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20
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Reyes-Espinosa F, Juárez-Saldivar A, Palos I, Herrera-Mayorga V, García-Pérez C, Rivera G. In Silico Analysis of Homologous Heterodimers of Cruzipain-Chagasin from Structural Models Built by Homology. Int J Mol Sci 2019; 20:ijms20061320. [PMID: 30875920 PMCID: PMC6470822 DOI: 10.3390/ijms20061320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 03/08/2019] [Accepted: 03/11/2019] [Indexed: 12/04/2022] Open
Abstract
The present study gives an overview of the binding energetics of the homologous heterodimers of cruzipain−chagasin based on the binding energy (ΔGb) prediction obtained with FoldX. This analysis involves a total of 70 homologous models of the cruzipain−chagasin complex which were constructed by homology from the combinatory variation of nine papain-like cysteine peptidase structures and seven cysteine protease inhibitor structures (as chagasin-like and cystatin-like inhibitors). Only 32 systems have been evaluated experimentally, ΔGbexperimental values previously reported. Therefore, the result of the multiple analysis in terms of the thermodynamic parameters, are shown as relative energy |ΔΔG| = |ΔGbfromFoldX − ΔGbexperimental|. Nine models were identified that recorded |ΔΔG| < 1.3, five models to 2.8 > |ΔΔG| > 1.3 and the other 18 models, values of |ΔΔG| > 2.8. The energetic analysis of the contribution of ΔH and ΔS to ΔGb to the 14-molecular model presents a ΔGb mostly ΔH-driven at neutral pH and at an ionic strength (I) of 0.15 M. The dependence of ΔGb(I,pH) at 298 K to the cruzipain−chagasin complex predicts a linear dependence of ΔGb(I). The computational protocol allowed the identification and prediction of thermodynamics binding energy parameters for cruzipain−chagasin-like heterodimers.
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Affiliation(s)
- Francisco Reyes-Espinosa
- Laboratorio de Biotecnología Farmacéutica, Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Boulevard del Maestro s/n esq. Elías Piña, Col. Narciso Mendoza, Reynosa 88710, Mexico.
| | - Alfredo Juárez-Saldivar
- Laboratorio de Biotecnología Farmacéutica, Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Boulevard del Maestro s/n esq. Elías Piña, Col. Narciso Mendoza, Reynosa 88710, Mexico.
| | - Isidro Palos
- Unidad Académica Multidisciplinaria Reynosa-Rodhe, Universidad Autónoma Tamaulipas, Carr. Reynosa-San Fernando, Reynosa 88779, Mexico.
| | - Verónica Herrera-Mayorga
- Laboratorio de Biotecnología Farmacéutica, Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Boulevard del Maestro s/n esq. Elías Piña, Col. Narciso Mendoza, Reynosa 88710, Mexico.
- Departamento de Ingeniería Bioquímica, Unidad Académica Multidisciplinaria Mante, Universidad Autónoma Tamaulipas, Blvd. Enrique Cárdenas González 1201, Mante 89840, Mexico.
| | - Carlos García-Pérez
- Scientific Computing Research Unit, Helmholtz Zentrum München, 85764 Munich, Germany.
| | - Gildardo Rivera
- Laboratorio de Biotecnología Farmacéutica, Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Boulevard del Maestro s/n esq. Elías Piña, Col. Narciso Mendoza, Reynosa 88710, Mexico.
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21
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Ashford P, Pang CSM, Moya-García AA, Adeyelu T, Orengo CA. A CATH domain functional family based approach to identify putative cancer driver genes and driver mutations. Sci Rep 2019; 9:263. [PMID: 30670742 PMCID: PMC6343001 DOI: 10.1038/s41598-018-36401-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Accepted: 11/13/2018] [Indexed: 12/31/2022] Open
Abstract
Tumour sequencing identifies highly recurrent point mutations in cancer driver genes, but rare functional mutations are hard to distinguish from large numbers of passengers. We developed a novel computational platform applying a multi-modal approach to filter out passengers and more robustly identify putative driver genes. The primary filter identifies enrichment of cancer mutations in CATH functional families (CATH-FunFams) – structurally and functionally coherent sets of evolutionary related domains. Using structural representatives from CATH-FunFams, we subsequently seek enrichment of mutations in 3D and show that these mutation clusters have a very significant tendency to lie close to known functional sites or conserved sites predicted using CATH-FunFams. Our third filter identifies enrichment of putative driver genes in functionally coherent protein network modules confirmed by literature analysis to be cancer associated. Our approach is complementary to other domain enrichment approaches exploiting Pfam families, but benefits from more functionally coherent groupings of domains. Using a set of mutations from 22 cancers we detect 151 putative cancer drivers, of which 79 are not listed in cancer resources and include recently validated cancer associated genes EPHA7, DCC netrin-1 receptor and zinc-finger protein ZNF479.
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Affiliation(s)
- Paul Ashford
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK
| | - Camilla S M Pang
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK
| | - Aurelio A Moya-García
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK.,Laboratorio de Biología Molecular del Cáncer, Centro de Investigaciones Médico-Sanitarias (CIMES), Universidad de Málaga, Málaga, Spain
| | - Tolulope Adeyelu
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK
| | - Christine A Orengo
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK.
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22
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PremPDI estimates and interprets the effects of missense mutations on protein-DNA interactions. PLoS Comput Biol 2018; 14:e1006615. [PMID: 30533007 PMCID: PMC6303081 DOI: 10.1371/journal.pcbi.1006615] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 12/21/2018] [Accepted: 11/01/2018] [Indexed: 01/01/2023] Open
Abstract
Protein-DNA interactions play important roles in regulations of many vital cellular processes, including transcription, translation, DNA replication and recombination. Sequence variants occurring in these DNA binding proteins that alter protein-DNA interactions may cause significant perturbations or complete abolishment of function, potentially leading to diseases. Developing a mechanistic understanding of impacts of variants on protein-DNA interactions becomes a persistent need. To address this need we introduce a new computational method PremPDI that predicts the effect of single missense mutation in the protein on the protein-DNA interaction and calculates the quantitative binding affinity change. The PremPDI method is based on molecular mechanics force fields and fast side-chain optimization algorithms with parameters optimized on experimental sets of 219 mutations from 49 protein-DNA complexes. PremPDI yields a very good agreement between predicted and experimental values with Pearson correlation coefficient of 0.71 and root-mean-square error of 0.86 kcal mol-1. The PremPDI server could map mutations on a structural protein-DNA complex, calculate the associated changes in binding affinity, determine the deleterious effect of a mutation, and produce a mutant structural model for download. PremPDI can be applied to many tasks, such as determination of potential damaging mutations in cancer and other diseases. PremPDI is available at http://lilab.jysw.suda.edu.cn/research/PremPDI/. Developing methods for accurate prediction of effects of amino acid substitutions on protein-DNA interactions is important for a wide range of biomedical applications such as understanding disease-causing mechanism of missense mutations and guiding protein engineering. Very few methods have been developed for predicting the effects of mutations on protein-DNA binding affinity. Here we report a new computational method, PRedicts the Effects of single Mutations on Protein-DNA Interactions (PremPDI). The core of the PremPDI method is based on molecular mechanics force fields and fast side-chain optimization algorithms that makes the PremPDI algorithm efficient and being fast enough to handle large number of cases. The performance of the PremPDI protocol was tested against experimentally determined binding free energy changes of 219 mutations from 49 protein-DNA complexes and yields very good correlation coefficient. The PremPDI webserver is available to the community at http://lilab.jysw.suda.edu.cn/research/PremPDI/.
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23
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Mura C, Draizen EJ, Bourne PE. Structural biology meets data science: does anything change? Curr Opin Struct Biol 2018; 52:95-102. [PMID: 30267935 DOI: 10.1016/j.sbi.2018.09.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 08/31/2018] [Accepted: 09/07/2018] [Indexed: 01/22/2023]
Abstract
Data science has emerged from the proliferation of digital data, coupled with advances in algorithms, software and hardware (e.g., GPU computing). Innovations in structural biology have been driven by similar factors, spurring us to ask: can these two fields impact one another in deep and hitherto unforeseen ways? We posit that the answer is yes. New biological knowledge lies in the relationships between sequence, structure, function and disease, all of which play out on the stage of evolution, and data science enables us to elucidate these relationships at scale. Here, we consider the above question from the five key pillars of data science: acquisition, engineering, analytics, visualization and policy, with an emphasis on machine learning as the premier analytics approach.
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Affiliation(s)
- Cameron Mura
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Eli J Draizen
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Philip E Bourne
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Data Science Institute, University of Virginia, Charlottesville, VA 22904, USA.
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24
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Computational Approaches to Prioritize Cancer Driver Missense Mutations. Int J Mol Sci 2018; 19:ijms19072113. [PMID: 30037003 PMCID: PMC6073793 DOI: 10.3390/ijms19072113] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Revised: 07/02/2018] [Accepted: 07/05/2018] [Indexed: 12/31/2022] Open
Abstract
Cancer is a complex disease that is driven by genetic alterations. There has been a rapid development of genome-wide techniques during the last decade along with a significant lowering of the cost of gene sequencing, which has generated widely available cancer genomic data. However, the interpretation of genomic data and the prediction of the association of genetic variations with cancer and disease phenotypes still requires significant improvement. Missense mutations, which can render proteins non-functional and provide a selective growth advantage to cancer cells, are frequently detected in cancer. Effects caused by missense mutations can be pinpointed by in silico modeling, which makes it more feasible to find a treatment and reverse the effect. Specific human phenotypes are largely determined by stability, activity, and interactions between proteins and other biomolecules that work together to execute specific cellular functions. Therefore, analysis of missense mutations’ effects on proteins and their complexes would provide important clues for identifying functionally important missense mutations, understanding the molecular mechanisms of cancer progression and facilitating treatment and prevention. Herein, we summarize the major computational approaches and tools that provide not only the classification of missense mutations as cancer drivers or passengers but also the molecular mechanisms induced by driver mutations. This review focuses on the discussion of annotation and prediction methods based on structural and biophysical data, analysis of somatic cancer missense mutations in 3D structures of proteins and their complexes, predictions of the effects of missense mutations on protein stability, protein-protein and protein-nucleic acid interactions, and assessment of conformational changes in protein conformations induced by mutations.
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25
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Hadi-Alijanvand H, Rouhani M. Partner-Specific Prediction of Protein-Dimer Stability from Unbound Structure of Monomer. J Chem Inf Model 2018; 58:733-745. [PMID: 29444397 DOI: 10.1021/acs.jcim.7b00606] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Protein complexes play deterministic roles in live entities in sensing, compiling, controlling, and responding to external and internal stimuli. Thermodynamic stability is an important property of protein complexes; having knowledge about complex stability helps us to understand the basics of protein assembly-related diseases and the mechanism of protein assembly clearly. Enormous protein-protein interactions, detected by high-throughput methods, necessitate finding fast methods for predicting the stability of protein assemblies in a quantitative and qualitative manner. The existing methods of predicting complex stability need knowledge about the three-dimensional (3D) structure of the intended protein complex. Here, we introduce a new method for predicting dissociation free energy of subunits by analyzing the structural and topological properties of a protein binding patch on a single subunit of the desired protein complex. The method needs the 3D structure of just one subunit and the information about the position of the intended binding site on the surface of that subunit to predict dimer stability in a classwise manner. The patterns of structural and topological properties of a protein binding patch are decoded by recurrence quantification analysis. Nonparametric discrimination is then utilized to predict the stability class of the intended dimer with accuracy greater than 85%.
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Affiliation(s)
- Hamid Hadi-Alijanvand
- Department of Biological Sciences , Institute for Advanced Studies in Basic Sciences (IASBS) , Zanjan , 45137-66731 , Iran
| | - Maryam Rouhani
- Department of Biological Sciences , Institute for Advanced Studies in Basic Sciences (IASBS) , Zanjan , 45137-66731 , Iran
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26
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Neuwald AF, Aravind L, Altschul SF. Inferring joint sequence-structural determinants of protein functional specificity. eLife 2018; 7. [PMID: 29336305 PMCID: PMC5770160 DOI: 10.7554/elife.29880] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Accepted: 12/22/2017] [Indexed: 01/05/2023] Open
Abstract
Residues responsible for allostery, cooperativity, and other subtle but functionally important interactions remain difficult to detect. To aid such detection, we employ statistical inference based on the assumption that residues distinguishing a protein subgroup from evolutionarily divergent subgroups often constitute an interacting functional network. We identify such networks with the aid of two measures of statistical significance. One measure aids identification of divergent subgroups based on distinguishing residue patterns. For each subgroup, a second measure identifies structural interactions involving pattern residues. Such interactions are derived either from atomic coordinates or from Direct Coupling Analysis scores, used as surrogates for structural distances. Applying this approach to N-acetyltransferases, P-loop GTPases, RNA helicases, synaptojanin-superfamily phosphatases and nucleases, and thymine/uracil DNA glycosylases yielded results congruent with biochemical understanding of these proteins, and also revealed striking sequence-structural features overlooked by other methods. These and similar analyses can aid the design of drugs targeting allosteric sites.
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Affiliation(s)
- Andrew F Neuwald
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, United States.,Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, United States
| | - L Aravind
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, United States
| | - Stephen F Altschul
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, United States
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27
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Barradas-Bautista D, Rosell M, Pallara C, Fernández-Recio J. Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems. PROTEIN-PROTEIN INTERACTIONS IN HUMAN DISEASE, PART A 2018; 110:203-249. [DOI: 10.1016/bs.apcsb.2017.06.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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28
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PDB-wide identification of biological assemblies from conserved quaternary structure geometry. Nat Methods 2017; 15:67-72. [DOI: 10.1038/nmeth.4510] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 10/17/2017] [Indexed: 02/07/2023]
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29
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Mutations at protein-protein interfaces: Small changes over big surfaces have large impacts on human health. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2017; 128:3-13. [DOI: 10.1016/j.pbiomolbio.2016.10.002] [Citation(s) in RCA: 107] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2016] [Revised: 10/15/2016] [Accepted: 10/19/2016] [Indexed: 12/22/2022]
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30
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Moya-García A, Adeyelu T, Kruger FA, Dawson NL, Lees JG, Overington JP, Orengo C, Ranea JAG. Structural and Functional View of Polypharmacology. Sci Rep 2017; 7:10102. [PMID: 28860623 PMCID: PMC5579063 DOI: 10.1038/s41598-017-10012-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Accepted: 08/02/2017] [Indexed: 02/06/2023] Open
Abstract
Protein domains mediate drug-protein interactions and this principle can guide the design of multi-target drugs i.e. polypharmacology. In this study, we associate multi-target drugs with CATH functional families through the overrepresentation of targets of those drugs in CATH functional families. Thus, we identify CATH functional families that are currently enriched in drugs (druggable CATH functional families) and we use the network properties of these druggable protein families to analyse their association with drug side effects. Analysis of selected druggable CATH functional families, enriched in drug targets, show that relatives exhibit highly conserved drug binding sites. Furthermore, relatives within druggable CATH functional families occupy central positions in a human protein functional network, cluster together forming network neighbourhoods and are less likely to be within proteins associated with drug side effects. Our results demonstrate that CATH functional families can be used to identify drug-target interactions, opening a new research direction in target identification.
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Affiliation(s)
- Aurelio Moya-García
- University College London, Institute of Structural and Molecular Biology, London, UK.
- Department of Molecular Biology and Biochemistry, Universidad de Malaga, 29071, Málaga Spain, CIBER de Enfermedades Raras (CIBERER), 29071, Málaga, Spain.
| | - Tolulope Adeyelu
- University College London, Institute of Structural and Molecular Biology, London, UK
| | - Felix A Kruger
- European Molecular Laboratory - European Bioinformatics Institute, Hinxton, UK
- BenevolentAI, Churchway 40, NW1 1LW, London, UK
| | - Natalie L Dawson
- University College London, Institute of Structural and Molecular Biology, London, UK
| | - Jon G Lees
- University College London, Institute of Structural and Molecular Biology, London, UK
| | - John P Overington
- European Molecular Laboratory - European Bioinformatics Institute, Hinxton, UK
- Medicines Discovery Catapult, Mereside, Alderley Park, Alderley Edge, Cheshire, SK10 4TG, UK
| | - Christine Orengo
- University College London, Institute of Structural and Molecular Biology, London, UK
| | - Juan A G Ranea
- Department of Molecular Biology and Biochemistry, Universidad de Málaga, 29071, Málaga, Spain
- CIBER de Enfermedades Raras (CIBERER), 29071, Málaga, Spain
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31
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Chaudhari R, Tan Z, Huang B, Zhang S. Computational polypharmacology: a new paradigm for drug discovery. Expert Opin Drug Discov 2017; 12:279-291. [PMID: 28067061 PMCID: PMC7241838 DOI: 10.1080/17460441.2017.1280024] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
INTRODUCTION Over the past couple of years, the cost of drug development has sharply increased along with the high rate of clinical trial failures. Such increase in expenses is partially due to the inability of the "one drug - one target" approach to predict drug side effects and toxicities. To tackle this issue, an alternative approach, known as polypharmacology, is being adopted to study small molecule interactions with multiple targets. Apart from developing more potent and effective drugs, this approach allows for studies of off-target activities and the facilitation of drug repositioning. Although exhaustive polypharmacology studies in-vitro or in-vivo are not practical, computational methods of predicting unknown targets or side effects are being developed. Areas covered: This article describes various computational approaches that have been developed to study polypharmacology profiles of small molecules. It also provides a brief description of the algorithms used in these state-of-the-art methods. Expert opinion: Recent success in computational prediction of multi-targeting drugs has established polypharmacology as a promising alternative approach to tackle some of the daunting complications in drug discovery. This will not only help discover more effective agents, but also present tremendous opportunities to study novel target pharmacology and facilitate drug repositioning efforts in the pharmaceutical industry.
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Affiliation(s)
- Rajan Chaudhari
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
| | - Zhi Tan
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
- The University of Texas Graduate School of Biomedical Sciences, Houston, TX 77030
| | - Beibei Huang
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
| | - Shuxing Zhang
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
- The University of Texas Graduate School of Biomedical Sciences, Houston, TX 77030
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32
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Li M, Goncearenco A, Panchenko AR. Annotating Mutational Effects on Proteins and Protein Interactions: Designing Novel and Revisiting Existing Protocols. Methods Mol Biol 2017; 1550:235-260. [PMID: 28188534 PMCID: PMC5388446 DOI: 10.1007/978-1-4939-6747-6_17] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
In this review we describe a protocol to annotate the effects of missense mutations on proteins, their functions, stability, and binding. For this purpose we present a collection of the most comprehensive databases which store different types of sequencing data on missense mutations, we discuss their relationships, possible intersections, and unique features. Next, we suggest an annotation workflow using the state-of-the art methods and highlight their usability, advantages, and limitations for different cases. Finally, we address a particularly difficult problem of deciphering the molecular mechanisms of mutations on proteins and protein complexes to understand the origins and mechanisms of diseases.
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Affiliation(s)
- Minghui Li
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Alexander Goncearenco
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Anna R Panchenko
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, 20894, USA.
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33
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Hamzeh-Mivehroud M, Sokouti B, Dastmalchi S. Molecular Docking at a Glance. Oncology 2017. [DOI: 10.4018/978-1-5225-0549-5.ch030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.
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Affiliation(s)
| | | | - Siavoush Dastmalchi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Iran & School of Pharmacy, Tabriz University of Medical Sciences, Iran
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34
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Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows. Methods Mol Biol 2017; 1647:221-236. [PMID: 28809006 DOI: 10.1007/978-1-4939-7201-2_15] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
We describe a computational protocol to aid the design of small molecule and peptide drugs that target protein-protein interactions, particularly for anti-cancer therapy. To achieve this goal, we explore multiple strategies, including finding binding hot spots, incorporating chemical similarity and bioactivity data, and sampling similar binding sites from homologous protein complexes. We demonstrate how to combine existing interdisciplinary resources with examples of semi-automated workflows. Finally, we discuss several major problems, including the occurrence of drug-resistant mutations, drug promiscuity, and the design of dual-effect inhibitors.
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35
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Glantz-Gashai Y, Meirson T, Samson AO. Normal Modes Expose Active Sites in Enzymes. PLoS Comput Biol 2016; 12:e1005293. [PMID: 28002427 PMCID: PMC5225006 DOI: 10.1371/journal.pcbi.1005293] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2015] [Revised: 01/10/2017] [Accepted: 12/07/2016] [Indexed: 01/10/2023] Open
Abstract
Accurate prediction of active sites is an important tool in bioinformatics. Here we present an improved structure based technique to expose active sites that is based on large changes of solvent accessibility accompanying normal mode dynamics. The technique which detects EXPOsure of active SITes through normal modEs is named EXPOSITE. The technique is trained using a small 133 enzyme dataset and tested using a large 845 enzyme dataset, both with known active site residues. EXPOSITE is also tested in a benchmark protein ligand dataset (PLD) comprising 48 proteins with and without bound ligands. EXPOSITE is shown to successfully locate the active site in most instances, and is found to be more accurate than other structure-based techniques. Interestingly, in several instances, the active site does not correspond to the largest pocket. EXPOSITE is advantageous due to its high precision and paves the way for structure based prediction of active site in enzymes. In this paper, we present an improved technique to predict active sites in enzymes. Our technique is based on changes of solvent accessibility that accompany normal mode dynamics. We assert the technique strength using several enzyme datasets with known catalytic residues. We show the technique successfully locates the active site in most cases, and consistently surpasses the accuracy of other techniques. We show how the technique is advantageous and paves the way for high precision prediction of active sites.
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Affiliation(s)
| | - Tomer Meirson
- Faculty of Medicine in the Galilee, Bar Ilan University, Safed, Israel
| | - Abraham O. Samson
- Faculty of Medicine in the Galilee, Bar Ilan University, Safed, Israel
- * E-mail:
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36
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Structural constraints-based evaluation of immunogenic avirulent toxins from Clostridium botulinum C2 and C3 toxins as subunit vaccines. INFECTION GENETICS AND EVOLUTION 2016; 44:17-27. [PMID: 27320793 DOI: 10.1016/j.meegid.2016.06.029] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 09/21/2015] [Revised: 05/26/2016] [Accepted: 06/13/2016] [Indexed: 12/11/2022]
Abstract
Clostridium botulinum (group-III) is an anaerobic bacterium producing C2 and C3 toxins in addition to botulinum neurotoxins in avian and mammalian cells. C2 and C3 toxins are members of bacterial ADP-ribosyltransferase superfamily, which modify the eukaryotic cell surface proteins by ADP-ribosylation reaction. Herein, the mutant proteins with lack of catalytic and pore forming function derived from C2 (C2I and C2II) and C3 toxins were computationally evaluated to understand their structure-function integrity. We have chosen many structural constraints including local structural environment, folding process, backbone conformation, conformational dynamic sub-space, NAD-binding specificity and antigenic determinants for screening of suitable avirulent toxins. A total of 20 avirulent mutants were identified out of 23 mutants, which were experimentally produced by site-directed mutagenesis. No changes in secondary structural elements in particular to α-helices and β-sheets and also in fold rate of all-β classes. Structural stability was maintained by reordered hydrophobic and hydrogen bonding patterns. Molecular dynamic studies suggested that coupled mutations may restrain the binding affinity to NAD(+) or protein substrate upon structural destabilization. Avirulent toxins of this study have stable energetic backbone conformation with a common blue print of folding process. Molecular docking studies revealed that avirulent mutants formed more favorable hydrogen bonding with the side-chain of amino acids near to conserved NAD-binding core, despite of restraining NAD-binding specificity. Thus, structural constraints in the avirulent toxins would determine their immunogenic nature for the prioritization of protein-based subunit vaccine/immunogens to avian and veterinary animals infected with C. botulinum.
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37
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Li M, Simonetti FL, Goncearenco A, Panchenko AR. MutaBind estimates and interprets the effects of sequence variants on protein-protein interactions. Nucleic Acids Res 2016; 44:W494-501. [PMID: 27150810 PMCID: PMC4987923 DOI: 10.1093/nar/gkw374] [Citation(s) in RCA: 91] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Accepted: 04/23/2016] [Indexed: 11/13/2022] Open
Abstract
Proteins engage in highly selective interactions with their macromolecular partners. Sequence variants that alter protein binding affinity may cause significant perturbations or complete abolishment of function, potentially leading to diseases. There exists a persistent need to develop a mechanistic understanding of impacts of variants on proteins. To address this need we introduce a new computational method MutaBind to evaluate the effects of sequence variants and disease mutations on protein interactions and calculate the quantitative changes in binding affinity. The MutaBind method uses molecular mechanics force fields, statistical potentials and fast side-chain optimization algorithms. The MutaBind server maps mutations on a structural protein complex, calculates the associated changes in binding affinity, determines the deleterious effect of a mutation, estimates the confidence of this prediction and produces a mutant structural model for download. MutaBind can be applied to a large number of problems, including determination of potential driver mutations in cancer and other diseases, elucidation of the effects of sequence variants on protein fitness in evolution and protein design. MutaBind is available at http://www.ncbi.nlm.nih.gov/projects/mutabind/.
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Affiliation(s)
- Minghui Li
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA
| | | | - Alexander Goncearenco
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA
| | - Anna R Panchenko
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA
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38
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Keskin O, Tuncbag N, Gursoy A. Predicting Protein–Protein Interactions from the Molecular to the Proteome Level. Chem Rev 2016; 116:4884-909. [DOI: 10.1021/acs.chemrev.5b00683] [Citation(s) in RCA: 207] [Impact Index Per Article: 25.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
| | - Nurcan Tuncbag
- Graduate
School of Informatics, Department of Health Informatics, Middle East Technical University, 06800 Ankara, Turkey
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39
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Bakail M, Ochsenbein F. Targeting protein–protein interactions, a wide open field for drug design. CR CHIM 2016. [DOI: 10.1016/j.crci.2015.12.004] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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40
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Esmaielbeiki R, Krawczyk K, Knapp B, Nebel JC, Deane CM. Progress and challenges in predicting protein interfaces. Brief Bioinform 2016; 17:117-31. [PMID: 25971595 PMCID: PMC4719070 DOI: 10.1093/bib/bbv027] [Citation(s) in RCA: 100] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Revised: 03/18/2015] [Indexed: 12/31/2022] Open
Abstract
The majority of biological processes are mediated via protein-protein interactions. Determination of residues participating in such interactions improves our understanding of molecular mechanisms and facilitates the development of therapeutics. Experimental approaches to identifying interacting residues, such as mutagenesis, are costly and time-consuming and thus, computational methods for this purpose could streamline conventional pipelines. Here we review the field of computational protein interface prediction. We make a distinction between methods which address proteins in general and those targeted at antibodies, owing to the radically different binding mechanism of antibodies. We organize the multitude of currently available methods hierarchically based on required input and prediction principles to provide an overview of the field.
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41
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Ghoorah AW, Devignes MD, Smaïl-Tabbone M, Ritchie DW. Protein docking using case-based reasoning. Proteins 2015; 81:2150-8. [PMID: 24123156 DOI: 10.1002/prot.24433] [Citation(s) in RCA: 86] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Protein docking algorithms aim to calculate the three-dimensional (3D) structure of a protein complex starting from its unbound components. Although ab initio docking algorithms are improving, there is a growing need to use homology modeling techniques to exploit the rapidly increasing volumes of structural information that now exist. However, most current homology modeling approaches involve finding a pair of complete single-chain structures in a homologous protein complex to use as a 3D template, despite the fact that protein complexes are often formed from one or more domain-domain interactions (DDIs). To model 3D protein complexes by domain-domain homology, we have developed a case-based reasoning approach called KBDOCK which systematically identifies and reuses domain family binding sites from our database of nonredundant DDIs. When tested on 54 protein complexes from the Protein Docking Benchmark, our approach provides a near-perfect way to model single-domain protein complexes when full-homology templates are available, and it extends our ability to model more difficult cases when only partial or incomplete templates exist. These promising early results highlight the need for a new and diverse docking benchmark set, specifically designed to assess homology docking approaches.
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Affiliation(s)
- Anisah W Ghoorah
- Department of Obstetrics and Gynaecology, University of Tuebingen, Tuebingen, Germany
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42
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Chaudhari R, Li Z. PyMine: a PyMOL plugin to integrate and visualize data for drug discovery. BMC Res Notes 2015; 8:517. [PMID: 26429562 PMCID: PMC4590260 DOI: 10.1186/s13104-015-1483-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2015] [Accepted: 09/21/2015] [Indexed: 11/10/2022] Open
Abstract
Background Tremendous amount of chemical and biological data are being generated by various high-throughput biotechnologies that could facilitate modern drug discovery. However, lack of integration makes it very challenging for individual scientists to access and understand all the data related to a specific protein of interest. Findings To overcome this challenge, we developed PyMine, a PyMOL plugin that retrieves chemical, structural, pathway and other related biological data of a receptor and small molecules from a variety of high-quality databases and presents them in a graphic and uniformed way. Conclusions Developed as an interactive and user-friendly tool, PyMine can be used as a central data-hub for users to access and visualize multiple types of data and to generate new ideas intuitively for structure-based molecule design.
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Affiliation(s)
- Rajan Chaudhari
- Department of Chemistry & Biochemistry, University of the Sciences in Philadelphia, Philadelphia, PA, 19104, USA.
| | - Zhijun Li
- Department of Chemistry & Biochemistry, University of the Sciences in Philadelphia, Philadelphia, PA, 19104, USA.
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43
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Goncearenco A, Shaytan AK, Shoemaker BA, Panchenko AR. Structural Perspectives on the Evolutionary Expansion of Unique Protein-Protein Binding Sites. Biophys J 2015. [PMID: 26213149 DOI: 10.1016/j.bpj.2015.06.056] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
Abstract
Structures of protein complexes provide atomistic insights into protein interactions. Human proteins represent a quarter of all structures in the Protein Data Bank; however, available protein complexes cover less than 10% of the human proteome. Although it is theoretically possible to infer interactions in human proteins based on structures of homologous protein complexes, it is still unclear to what extent protein interactions and binding sites are conserved, and whether protein complexes from remotely related species can be used to infer interactions and binding sites. We considered biological units of protein complexes and clustered protein-protein binding sites into similarity groups based on their structure and sequence, which allowed us to identify unique binding sites. We showed that the growth rate of the number of unique binding sites in the Protein Data Bank was much slower than the growth rate of the number of structural complexes. Next, we investigated the evolutionary roots of unique binding sites and identified the major phyletic branches with the largest expansion in the number of novel binding sites. We found that many binding sites could be traced to the universal common ancestor of all cellular organisms, whereas relatively few binding sites emerged at the major evolutionary branching points. We analyzed the physicochemical properties of unique binding sites and found that the most ancient sites were the largest in size, involved many salt bridges, and were the most compact and least planar. In contrast, binding sites that appeared more recently in the evolution of eukaryotes were characterized by a larger fraction of polar and aromatic residues, and were less compact and more planar, possibly due to their more transient nature and roles in signaling processes.
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Affiliation(s)
- Alexander Goncearenco
- Computational Biology Branch of the National Center for Biotechnology Information, Bethesda, Maryland
| | - Alexey K Shaytan
- Computational Biology Branch of the National Center for Biotechnology Information, Bethesda, Maryland
| | - Benjamin A Shoemaker
- Computational Biology Branch of the National Center for Biotechnology Information, Bethesda, Maryland
| | - Anna R Panchenko
- Computational Biology Branch of the National Center for Biotechnology Information, Bethesda, Maryland.
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44
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Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors. Molecules 2015; 20:11569-603. [PMID: 26111183 PMCID: PMC6272567 DOI: 10.3390/molecules200611569] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Revised: 06/02/2015] [Accepted: 06/15/2015] [Indexed: 02/06/2023] Open
Abstract
Blocking protein-protein interactions (PPI) using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers.
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45
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Shaytan AK, Landsman D, Panchenko AR. Nucleosome adaptability conferred by sequence and structural variations in histone H2A-H2B dimers. Curr Opin Struct Biol 2015; 32:48-57. [PMID: 25731851 DOI: 10.1016/j.sbi.2015.02.004] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Revised: 01/27/2015] [Accepted: 02/06/2015] [Indexed: 12/15/2022]
Abstract
Nucleosome variability is essential for their functions in compacting the chromatin structure and regulation of transcription, replication and cell reprogramming. The DNA molecule in nucleosomes is wrapped around an octamer composed of four types of core histones (H3, H4, H2A, H2B). Nucleosomes represent dynamic entities and may change their conformation, stability and binding properties by employing different sets of histone variants or by becoming post-translationally modified. There are many variants of histones H2A and H2B. Specific H2A and H2B variants may preferentially associate with each other resulting in different combinations of variants and leading to the increased combinatorial complexity of nucleosomes. In addition, the H2A-H2B dimer can be recognized and substituted by chaperones/remodelers as a distinct unit, can assemble independently and is stable during nucleosome unwinding. In this review we discuss how sequence and structural variations in H2A-H2B dimers may provide necessary complexity and confer the nucleosome functional variability.
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Affiliation(s)
- Alexey K Shaytan
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - David Landsman
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Anna R Panchenko
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.
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46
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Aumentado-Armstrong TT, Istrate B, Murgita RA. Algorithmic approaches to protein-protein interaction site prediction. Algorithms Mol Biol 2015; 10:7. [PMID: 25713596 PMCID: PMC4338852 DOI: 10.1186/s13015-015-0033-9] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2014] [Accepted: 01/07/2015] [Indexed: 12/19/2022] Open
Abstract
Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.
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47
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Solomon O, Bazak L, Levanon EY, Amariglio N, Unger R, Rechavi G, Eyal E. Characterizing of functional human coding RNA editing from evolutionary, structural, and dynamic perspectives. Proteins 2014; 82:3117-31. [DOI: 10.1002/prot.24672] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2014] [Revised: 07/28/2014] [Accepted: 08/11/2014] [Indexed: 12/29/2022]
Affiliation(s)
- Oz Solomon
- Cancer Research Center; Chaim Sheba Medical Center; Tel Hashomer 52621 Ramat Gan Israel
- The Everard & Mina Goodman Faculty of Life Sciences; Bar-Ilan University; Ramat Gan 52900 Israel
| | - Lily Bazak
- The Everard & Mina Goodman Faculty of Life Sciences; Bar-Ilan University; Ramat Gan 52900 Israel
| | - Erez Y. Levanon
- The Everard & Mina Goodman Faculty of Life Sciences; Bar-Ilan University; Ramat Gan 52900 Israel
| | - Ninette Amariglio
- Cancer Research Center; Chaim Sheba Medical Center; Tel Hashomer 52621 Ramat Gan Israel
| | - Ron Unger
- The Everard & Mina Goodman Faculty of Life Sciences; Bar-Ilan University; Ramat Gan 52900 Israel
| | - Gideon Rechavi
- Cancer Research Center; Chaim Sheba Medical Center; Tel Hashomer 52621 Ramat Gan Israel
- Sackler School of Medicine; Tel Aviv University; Tel Aviv 69978 Israel
| | - Eran Eyal
- Cancer Research Center; Chaim Sheba Medical Center; Tel Hashomer 52621 Ramat Gan Israel
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48
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Abstract
The past decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information and to analyze this information so as to infer both the functions of individual molecules and how they interact to modulate the behavior of biological systems. Here, we review these techniques, focusing on the construction of physical protein-protein interaction networks, and highlighting approaches that incorporate protein structure, which is becoming an increasingly important component of systems-level computational techniques. We also discuss how network analyses are being applied to enhance our basic understanding of biological systems and their disregulation, as well as how these networks are being used in drug development.
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Affiliation(s)
- Donald Petrey
- Center for Computational Biology and Bioinformatics, Department of Systems Biology
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49
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Advances in Human Biology: Combining Genetics and Molecular Biophysics to Pave the Way for Personalized Diagnostics and Medicine. ACTA ACUST UNITED AC 2014. [DOI: 10.1155/2014/471836] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Advances in several biology-oriented initiatives such as genome sequencing and structural genomics, along with the progress made through traditional biological and biochemical research, have opened up a unique opportunity to better understand the molecular effects of human diseases. Human DNA can vary significantly from person to person and determines an individual’s physical characteristics and their susceptibility to diseases. Armed with an individual’s DNA sequence, researchers and physicians can check for defects known to be associated with certain diseases by utilizing various databases. However, for unclassified DNA mutations or in order to reveal molecular mechanism behind the effects, the mutations have to be mapped onto the corresponding networks and macromolecular structures and then analyzed to reveal their effect on the wild type properties of biological processes involved. Predicting the effect of DNA mutations on individual’s health is typically referred to as personalized or companion diagnostics. Furthermore, once the molecular mechanism of the mutations is revealed, the patient should be given drugs which are the most appropriate for the individual genome, referred to as pharmacogenomics. Altogether, the shift in focus in medicine towards more genomic-oriented practices is the foundation of personalized medicine. The progress made in these rapidly developing fields is outlined.
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50
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Andreani J, Guerois R. Evolution of protein interactions: From interactomes to interfaces. Arch Biochem Biophys 2014; 554:65-75. [DOI: 10.1016/j.abb.2014.05.010] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2014] [Revised: 04/28/2014] [Accepted: 05/12/2014] [Indexed: 12/16/2022]
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