1
|
Nandigrami P, Fiser A. Assessing the functional impact of protein binding site definition. Protein Sci 2024; 33:e5026. [PMID: 38757384 PMCID: PMC11099757 DOI: 10.1002/pro.5026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 05/01/2024] [Accepted: 05/03/2024] [Indexed: 05/18/2024]
Abstract
Many biomedical applications, such as classification of binding specificities or bioengineering, depend on the accurate definition of protein binding interfaces. Depending on the choice of method used, substantially different sets of residues can be classified as belonging to the interface of a protein. A typical approach used to verify these definitions is to mutate residues and measure the impact of these changes on binding. Besides the lack of exhaustive data, this approach also suffers from the fundamental problem that a mutation introduces an unknown amount of alteration into an interface, which potentially alters the binding characteristics of the interface. In this study we explore the impact of alternative binding site definitions on the ability of a protein to recognize its cognate ligand using a pharmacophore approach, which does not affect the interface. The study also shows that methods for protein binding interface predictions should perform above approximately F-score = 0.7 accuracy level to capture the biological function of a protein.
Collapse
Affiliation(s)
- Prithviraj Nandigrami
- Departments of Systems and Computational Biology, and BiochemistryAlbert Einstein College of MedicineBronxNew YorkUSA
| | - Andras Fiser
- Departments of Systems and Computational Biology, and BiochemistryAlbert Einstein College of MedicineBronxNew YorkUSA
| |
Collapse
|
2
|
Dapkūnas J, Timinskas A, Olechnovič K, Tomkuvienė M, Venclovas Č. PPI3D: a web server for searching, analyzing and modeling protein-protein, protein-peptide and protein-nucleic acid interactions. Nucleic Acids Res 2024:gkae278. [PMID: 38619046 DOI: 10.1093/nar/gkae278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 03/19/2024] [Accepted: 04/03/2024] [Indexed: 04/16/2024] Open
Abstract
Structure-resolved protein interactions with other proteins, peptides and nucleic acids are key for understanding molecular mechanisms. The PPI3D web server enables researchers to query preprocessed and clustered structural data, analyze the results and make homology-based inferences for protein interactions. PPI3D offers three interaction exploration modes: (i) all interactions for proteins homologous to the query, (ii) interactions between two proteins or their homologs and (iii) interactions within a specific PDB entry. The server allows interactive analysis of the identified interactions in both summarized and detailed manner. This includes protein annotations, structures, the interface residues and the corresponding contact surface areas. In addition, users can make inferences about residues at the interaction interface for the query protein(s) from the sequence alignments and homology models. The weekly updated PPI3D database includes all the interaction interfaces and binding sites from PDB, clustered based on both protein sequence and structural similarity, yielding non-redundant datasets without loss of alternative interaction modes. Consequently, the PPI3D users avoid being flooded with redundant information, a typical situation for intensely studied proteins. Furthermore, PPI3D provides a possibility to download user-defined sets of interaction interfaces and analyze them locally. The PPI3D web server is available at https://bioinformatics.lt/ppi3d.
Collapse
Affiliation(s)
- Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Saulėtekio av. 7, Vilnius LT-10257, Lithuania
| | - Albertas Timinskas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Saulėtekio av. 7, Vilnius LT-10257, Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Saulėtekio av. 7, Vilnius LT-10257, Lithuania
- Univ. Grenoble Alpes, CNRS, Grenoble INP, LJK, 38000 Grenoble, France
| | - Miglė Tomkuvienė
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Saulėtekio av. 7, Vilnius LT-10257, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Saulėtekio av. 7, Vilnius LT-10257, Lithuania
| |
Collapse
|
3
|
Liu X, Luo Y, Li P, Song S, Peng J. Deep geometric representations for modeling effects of mutations on protein-protein binding affinity. PLoS Comput Biol 2021; 17:e1009284. [PMID: 34347784 PMCID: PMC8366979 DOI: 10.1371/journal.pcbi.1009284] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 08/16/2021] [Accepted: 07/17/2021] [Indexed: 11/19/2022] Open
Abstract
Modeling the impact of amino acid mutations on protein-protein interaction plays a crucial role in protein engineering and drug design. In this study, we develop GeoPPI, a novel structure-based deep-learning framework to predict the change of binding affinity upon mutations. Based on the three-dimensional structure of a protein, GeoPPI first learns a geometric representation that encodes topology features of the protein structure via a self-supervised learning scheme. These representations are then used as features for training gradient-boosting trees to predict the changes of protein-protein binding affinity upon mutations. We find that GeoPPI is able to learn meaningful features that characterize interactions between atoms in protein structures. In addition, through extensive experiments, we show that GeoPPI achieves new state-of-the-art performance in predicting the binding affinity changes upon both single- and multi-point mutations on six benchmark datasets. Moreover, we show that GeoPPI can accurately estimate the difference of binding affinities between a few recently identified SARS-CoV-2 antibodies and the receptor-binding domain (RBD) of the S protein. These results demonstrate the potential of GeoPPI as a powerful and useful computational tool in protein design and engineering. Our code and datasets are available at: https://github.com/Liuxg16/GeoPPI. Estimating the binding affinities of protein-protein interactions (PPIs) is crucial to understand protein function and design new functional proteins. Since the experimental measurement in wet-labs is labor-intensive and time-consuming, fast and accurate in silico approaches have received much attention. Although considerable efforts have been made in this direction, predicting the effects of mutations on the protein-protein binding affinity is still a challenging research problem. In this work, we introduce GeoPPI, a novel computational approach that uses deep geometric representations of protein complexes to predict the effects of mutations on the binding affinity. The geometric representations are first learned via a self-supervised learning scheme and then integrated with gradient-boosting trees to accomplish the prediction. We find that the learned representations encode meaningful patterns underlying the interactions between atoms in protein structures. Also, extensive tests on major benchmark datasets show that GeoPPI has made an important improvement over the existing methods in predicting the effects of mutations on the binding affinity.
Collapse
Affiliation(s)
- Xianggen Liu
- Laboratory for Brain and Intelligence and Department of Biomedical Engineering, Tsinghua University, Beijing, China
- School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China
- Beijing Innovation Center for Future Chip, Tsinghua University, Beijing, China
| | - Yunan Luo
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Pengyong Li
- Laboratory for Brain and Intelligence and Department of Biomedical Engineering, Tsinghua University, Beijing, China
- Beijing Innovation Center for Future Chip, Tsinghua University, Beijing, China
| | - Sen Song
- Laboratory for Brain and Intelligence and Department of Biomedical Engineering, Tsinghua University, Beijing, China
- Beijing Innovation Center for Future Chip, Tsinghua University, Beijing, China
- * E-mail: (JP); (SS)
| | - Jian Peng
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- * E-mail: (JP); (SS)
| |
Collapse
|
4
|
Sun J, Frishman D. Improved sequence-based prediction of interaction sites in α-helical transmembrane proteins by deep learning. Comput Struct Biotechnol J 2021; 19:1512-1530. [PMID: 33815689 PMCID: PMC7985279 DOI: 10.1016/j.csbj.2021.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 03/02/2021] [Accepted: 03/02/2021] [Indexed: 11/10/2022] Open
Abstract
Fast and accurate prediction of transmembrane protein interaction sites. First ever computational survey of interaction sites in membrane proteins. 10-30% of amino acid positions predicted to be involved in interactions.
Interactions between transmembrane (TM) proteins are fundamental for a wide spectrum of cellular functions, but precise molecular details of these interactions remain largely unknown due to the scarcity of experimentally determined three-dimensional complex structures. Computational techniques are therefore required for a large-scale annotation of interaction sites in TM proteins. Here, we present a novel deep-learning approach, DeepTMInter, for sequence-based prediction of interaction sites in α-helical TM proteins based on their topological, physiochemical, and evolutionary properties. Using a combination of ultra-deep residual neural networks with a stacked generalization ensemble technique DeepTMInter significantly outperforms existing methods, achieving the AUC/AUCPR values of 0.689/0.598. Across the main functional families of human transmembrane proteins, the percentage of amino acid sites predicted to be involved in interactions typically ranges between 10% and 25%, and up to 30% in ion channels. DeepTMInter is available as a standalone package at https://github.com/2003100127/deeptminter. The training and benchmarking datasets are available at https://data.mendeley.com/datasets/2t8kgwzp35.
Collapse
Affiliation(s)
- Jianfeng Sun
- Department of Bioinformatics, Wissenschaftzentrum Weihenstephan, Technical University of Munich, Maximus-von-Imhof-Forum 3, 85354 Freising, Germany
| | - Dmitrij Frishman
- Department of Bioinformatics, Wissenschaftzentrum Weihenstephan, Technical University of Munich, Maximus-von-Imhof-Forum 3, 85354 Freising, Germany
| |
Collapse
|
5
|
Roy AA, Dhawanjewar AS, Sharma P, Singh G, Madhusudhan MS. Protein Interaction Z Score Assessment (PIZSA): an empirical scoring scheme for evaluation of protein-protein interactions. Nucleic Acids Res 2020; 47:W331-W337. [PMID: 31114890 PMCID: PMC6602501 DOI: 10.1093/nar/gkz368] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 04/24/2019] [Accepted: 05/15/2019] [Indexed: 11/24/2022] Open
Abstract
Our web server, PIZSA (http://cospi.iiserpune.ac.in/pizsa), assesses the likelihood of protein–protein interactions by assigning a Z Score computed from interface residue contacts. Our score takes into account the optimal number of atoms that mediate the interaction between pairs of residues and whether these contacts emanate from the main chain or side chain. We tested the score on 174 native interactions for which 100 decoys each were constructed using ZDOCK. The native structure scored better than any of the decoys in 146 cases and was able to rank within the 95th percentile in 162 cases. This easily outperforms a competing method, CIPS. We also benchmarked our scoring scheme on 15 targets from the CAPRI dataset and found that our method had results comparable to that of CIPS. Further, our method is able to analyse higher order protein complexes without the need to explicitly identify chains as receptors or ligands. The PIZSA server is easy to use and could be used to score any input three-dimensional structure and provide a residue pair-wise break up of the results. Attractively, our server offers a platform for users to upload their own potentials and could serve as an ideal testing ground for this class of scoring schemes.
Collapse
Affiliation(s)
- Ankit A Roy
- Indian Institute of Science Education and Research, Pune, Dr Homi Bhabha Road, Pashan, Pune 411008, India
| | - Abhilesh S Dhawanjewar
- Indian Institute of Science Education and Research, Pune, Dr Homi Bhabha Road, Pashan, Pune 411008, India.,presently at School of Biological Sciences, University of Nebraska, Lincoln, NE 68588, USA
| | - Parichit Sharma
- Indian Institute of Science Education and Research, Pune, Dr Homi Bhabha Road, Pashan, Pune 411008, India.,presently at School of Informatics, Computing & Engineering, Department of Computer Science, Indiana University, Bloomington, IN 47408, USA
| | - Gulzar Singh
- Indian Institute of Science Education and Research, Pune, Dr Homi Bhabha Road, Pashan, Pune 411008, India
| | - M S Madhusudhan
- Indian Institute of Science Education and Research, Pune, Dr Homi Bhabha Road, Pashan, Pune 411008, India
| |
Collapse
|
6
|
Abstract
There is a large gap between the numbers of known protein-protein interactions and the corresponding experimentally solved structures of protein complexes. Fortunately, this gap can be in part bridged by computational structure modeling methods. Currently, template-based modeling is the most accurate means to predict both individual protein structures and protein complexes. One of the major issues in template-based modeling is to identify homologous structures that could be utilized as templates. To simplify this task, we have developed the PPI3D web server. The server is not only able to search for homologous protein complexes, but also provides means to analyze identified interactions and to model protein complexes. In recent CASP and CAPRI experiments, PPI3D proved to be a useful tool for homology modeling of multimeric proteins. In this chapter, we provide a brief description of the PPI3D web server capabilities and how to use the server for modeling of protein complexes.
Collapse
Affiliation(s)
- Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania.
| |
Collapse
|
7
|
Monzon AM, Carraro M, Chiricosta L, Reggiani F, Han J, Ozturk K, Wang Y, Miller M, Bromberg Y, Capriotti E, Savojardo C, Babbi G, Martelli PL, Casadio R, Katsonis P, Lichtarge O, Carter H, Kousi M, Katsanis N, Andreoletti G, Moult J, Brenner SE, Ferrari C, Leonardi E, Tosatto SCE. Performance of computational methods for the evaluation of pericentriolar material 1 missense variants in CAGI-5. Hum Mutat 2019; 40:1474-1485. [PMID: 31260570 PMCID: PMC7354699 DOI: 10.1002/humu.23856] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 05/30/2019] [Accepted: 06/23/2019] [Indexed: 12/11/2022]
Abstract
The CAGI-5 pericentriolar material 1 (PCM1) challenge aimed to predict the effect of 38 transgenic human missense mutations in the PCM1 protein implicated in schizophrenia. Participants were provided with 16 benign variants (negative controls), 10 hypomorphic, and 12 loss of function variants. Six groups participated and were asked to predict the probability of effect and standard deviation associated to each mutation. Here, we present the challenge assessment. Prediction performance was evaluated using different measures to conclude in a final ranking which highlights the strengths and weaknesses of each group. The results show a great variety of predictions where some methods performed significantly better than others. Benign variants played an important role as negative controls, highlighting predictors biased to identify disease phenotypes. The best predictor, Bromberg lab, used a neural-network-based method able to discriminate between neutral and non-neutral single nucleotide polymorphisms. The CAGI-5 PCM1 challenge allowed us to evaluate the state of the art techniques for interpreting the effect of novel variants for a difficult target protein.
Collapse
Affiliation(s)
| | - Marco Carraro
- Department of Biomedical Sciences, University of Padua, Padua, Italy
| | - Luigi Chiricosta
- Department of Biomedical Sciences, University of Padua, Padua, Italy
| | - Francesco Reggiani
- Department of Biomedical Sciences, University of Padua, Padua, Italy
- Department of Information Engineering, University of Padua, Padua, Italy
| | - James Han
- Department of Medicine, University of California San Diego, La Jolla, California
| | - Kivilcim Ozturk
- Department of Medicine, University of California San Diego, La Jolla, California
| | - Yanran Wang
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey
| | - Maximilian Miller
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey
- Institute for Advanced Study, Technical University of Munich (TUM), Munich, Germany
| | - Emidio Capriotti
- Department of Pharmacy and Biotechnology, BioFolD Unit, University of Bologna, Bologna, Italy
| | - Castrense Savojardo
- Department of Pharmacy and Biotechnology, Biocomputing Group, University of Bologna, Bologna, Italy
| | - Giulia Babbi
- Department of Pharmacy and Biotechnology, Biocomputing Group, University of Bologna, Bologna, Italy
| | - Pier L Martelli
- Department of Pharmacy and Biotechnology, Biocomputing Group, University of Bologna, Bologna, Italy
| | - Rita Casadio
- Department of Pharmacy and Biotechnology, Biocomputing Group, University of Bologna, Bologna, Italy
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Hannah Carter
- Department of Medicine, University of California San Diego, La Jolla, California
| | - Maria Kousi
- MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Nicholas Katsanis
- Center for Human Disease Modeling, Duke University Medical Center, Durham, North Carolina
| | - Gaia Andreoletti
- Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA
| | - John Moult
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Steven E Brenner
- Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA
| | - Carlo Ferrari
- Department of Information Engineering, University of Padua, Padua, Italy
| | | | - Silvio C E Tosatto
- Department of Biomedical Sciences, University of Padua, Padua, Italy
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California
| |
Collapse
|
8
|
Dapkūnas J, Olechnovič K, Venclovas Č. Structural modeling of protein complexes: Current capabilities and challenges. Proteins 2019; 87:1222-1232. [PMID: 31294859 DOI: 10.1002/prot.25774] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 06/21/2019] [Accepted: 07/06/2019] [Indexed: 12/27/2022]
Abstract
Proteins frequently interact with each other, and the knowledge of structures of the corresponding protein complexes is necessary to understand how they function. Computational methods are increasingly used to provide structural models of protein complexes. Not surprisingly, community-wide Critical Assessment of protein Structure Prediction (CASP) experiments have recently started monitoring the progress in this research area. We participated in CASP13 with the aim to evaluate our current capabilities in modeling of protein complexes and to gain a better understanding of factors that exert the largest impact on these capabilities. To model protein complexes in CASP13, we applied template-based modeling, free docking and hybrid techniques that enabled us to generate models of the topmost quality for 27 of 42 multimers. If templates for protein complexes could be identified, we modeled the structures with reasonable accuracy by straightforward homology modeling. If only partial templates were available, it was nevertheless possible to predict the interaction interfaces correctly or to generate acceptable models for protein complexes by combining template-based modeling with docking. If no templates were available, we used rigid-body docking with limited success. However, in some free docking models, despite the incorrect subunit orientation and missed interface contacts, the approximate location of protein binding sites was identified correctly. Apparently, our overall performance in docking was limited by the quality of monomer models and by the imperfection of scoring methods. The impact of human intervention on our results in modeling of protein complexes was significant indicating the need for improvements of automatic methods.
Collapse
Affiliation(s)
- Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| |
Collapse
|
9
|
Zeng B, Hönigschmid P, Frishman D. Residue co-evolution helps predict interaction sites in α-helical membrane proteins. J Struct Biol 2019; 206:156-169. [DOI: 10.1016/j.jsb.2019.02.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 01/30/2019] [Accepted: 02/13/2019] [Indexed: 11/29/2022]
|
10
|
Zhao J, Krystofiak ES, Ballesteros A, Cui R, Van Itallie CM, Anderson JM, Fenollar-Ferrer C, Kachar B. Multiple claudin-claudin cis interfaces are required for tight junction strand formation and inherent flexibility. Commun Biol 2018; 1:50. [PMID: 30271933 PMCID: PMC6123731 DOI: 10.1038/s42003-018-0051-5] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Accepted: 04/03/2018] [Indexed: 02/07/2023] Open
Abstract
Tight junctions consist of a network of sealing strands that create selective ion permeability barriers between adjoining epithelial or endothelial cells. The current model for tight junction strands consists of paired rows of claudins (Cldn) coupled by a cis interface (X-1) derived from crystalline Cldn15. Here we show that tight junction strands exhibit a broad range of lateral bending, indicating diversity in cis interactions. By combining protein–protein docking, coevolutionary analysis, molecular dynamics, and a mutagenesis screen, we identify a new Cldn–Cldn cis interface (Cis-1) that shares interacting residues with X-1 but has an ~ 17° lateral rotation between monomers. In addition, we found that a missense mutation in a Cldn14 that causes deafness and contributes stronger to Cis-1 than to X-1 prevents strand formation in cultured cells. Our results suggest that Cis-1 contributes to the inherent structural flexibility of tight junction strands and is required for maintaining permeability barrier function and hearing. Jun Zhao, Evan S. Krystofiak, and colleagues identified a new cis interface (Cis-1) essential for the formation of normal tight junctions. This study suggests that Cis-1 contributes to maintaining structural flexibility of tight junction strands for proper ion balance and hearing.
Collapse
Affiliation(s)
- Jun Zhao
- Laboratory of Cell Structure and Dynamics, National Institute on Deafness and Other Communication Disorders, 35A Convent Drive, Bethesda, MD, 20892, USA.,Cancer and Inflammation Program, National Cancer Institute, Frederick, MD, 21702, USA
| | - Evan S Krystofiak
- Laboratory of Cell Structure and Dynamics, National Institute on Deafness and Other Communication Disorders, 35A Convent Drive, Bethesda, MD, 20892, USA
| | - Angela Ballesteros
- Laboratory of Cell Structure and Dynamics, National Institute on Deafness and Other Communication Disorders, 35A Convent Drive, Bethesda, MD, 20892, USA.,Molecular Physiology and Biophysics Section, National Institute of Neurological Disorders and Stroke, Bethesda, MD, 20892, USA
| | - Runjia Cui
- Laboratory of Cell Structure and Dynamics, National Institute on Deafness and Other Communication Disorders, 35A Convent Drive, Bethesda, MD, 20892, USA
| | - Christina M Van Itallie
- Laboratory of Tight Junction Structure and Function, National Heart, Lung, and Blood Institute, 50 South Drive, Bethesda, MD, 20892, USA
| | - James M Anderson
- Laboratory of Tight Junction Structure and Function, National Heart, Lung, and Blood Institute, 50 South Drive, Bethesda, MD, 20892, USA
| | - Cristina Fenollar-Ferrer
- Computational Structural Biology Unit, National Institute of Neurological Disorders and Stroke, 35 Convent Drive, Bethesda, MD, 20892, USA. .,Laboratory of Molecular & Cellular Neurobiology, National Institute of Mental Health, 35 Convent Drive, Bethesda, MD, 20892, USA.
| | - Bechara Kachar
- Laboratory of Cell Structure and Dynamics, National Institute on Deafness and Other Communication Disorders, 35A Convent Drive, Bethesda, MD, 20892, USA.
| |
Collapse
|
11
|
Lagarde N, Carbone A, Sacquin-Mora S. Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions. Proteins 2018; 86:723-737. [DOI: 10.1002/prot.25506] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 03/23/2018] [Accepted: 04/07/2018] [Indexed: 02/06/2023]
Affiliation(s)
- Nathalie Lagarde
- Laboratoire de Biochimie Théorique, CNRS UPR9080, Institut de Biologie Physico-Chimique, University Paris Diderot, Sorbonne Paris Cité, 13 rue Pierre et Marie Curie; Paris 75005 France
| | - Alessandra Carbone
- Laboratoire de Biologie Computationnelle et Quantitative, CNRS UMR7238, UPMC Univ-Paris 6, Sorbonne Université, 4 place Jussieu; Paris 75005 France
- Institut Universitaire de France; Paris 75005 France
| | - Sophie Sacquin-Mora
- Laboratoire de Biochimie Théorique, CNRS UPR9080, Institut de Biologie Physico-Chimique, University Paris Diderot, Sorbonne Paris Cité, 13 rue Pierre et Marie Curie; Paris 75005 France
| |
Collapse
|
12
|
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]
|
13
|
Dapkunas J, Timinskas A, Olechnovic K, Margelevicius M, Diciunas R, Venclovas C. The PPI3D web server for searching, analyzing and modeling protein-protein interactions in the context of 3D structures. Bioinformatics 2017; 33:935-937. [PMID: 28011769 DOI: 10.1093/bioinformatics/btw756] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Accepted: 11/22/2016] [Indexed: 12/21/2022] Open
Abstract
Summary The PPI3D web server is focused on searching and analyzing the structural data on protein-protein interactions. Reducing the data redundancy by clustering and analyzing the properties of interaction interfaces using Voronoi tessellation makes this software a highly effective tool for addressing different questions related to protein interactions. Availability and Implementation The server is freely accessible at http://bioinformatics.lt/software/ppi3d/ . Contact ceslovas.venclovas@bti.vu.lt. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Justas Dapkunas
- Institute of Biotechnology, Vilnius University, Vilnius, Lithuania
| | | | - Kliment Olechnovic
- Institute of Biotechnology, Vilnius University, Vilnius, Lithuania.,Faculty of Mathematics and Informatics, Vilnius University, Vilnius, Lithuania
| | | | - Rytis Diciunas
- Institute of Biotechnology, Vilnius University, Vilnius, Lithuania
| | | |
Collapse
|
14
|
Dapkūnas J, Olechnovič K, Venclovas Č. Modeling of protein complexes in CAPRI Round 37 using template-based approach combined with model selection. Proteins 2017; 86 Suppl 1:292-301. [DOI: 10.1002/prot.25378] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 08/25/2017] [Accepted: 09/10/2017] [Indexed: 01/14/2023]
Affiliation(s)
- Justas Dapkūnas
- Institute of Biotechnology, Vilnius University, Saulėtekio 7; Vilnius LT-10257 Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Vilnius University, Saulėtekio 7; Vilnius LT-10257 Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Vilnius University, Saulėtekio 7; Vilnius LT-10257 Lithuania
| |
Collapse
|
15
|
Carraro M, Minervini G, Giollo M, Bromberg Y, Capriotti E, Casadio R, Dunbrack R, Elefanti L, Fariselli P, Ferrari C, Gough J, Katsonis P, Leonardi E, Lichtarge O, Menin C, Martelli PL, Niroula A, Pal LR, Repo S, Scaini MC, Vihinen M, Wei Q, Xu Q, Yang Y, Yin Y, Zaucha J, Zhao H, Zhou Y, Brenner SE, Moult J, Tosatto SCE. Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI. Hum Mutat 2017; 38:1042-1050. [PMID: 28440912 PMCID: PMC5561474 DOI: 10.1002/humu.23235] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Revised: 04/17/2017] [Accepted: 04/19/2017] [Indexed: 12/31/2022]
Abstract
Correct phenotypic interpretation of variants of unknown significance for cancer-associated genes is a diagnostic challenge as genetic screenings gain in popularity in the next-generation sequencing era. The Critical Assessment of Genome Interpretation (CAGI) experiment aims to test and define the state of the art of genotype-phenotype interpretation. Here, we present the assessment of the CAGI p16INK4a challenge. Participants were asked to predict the effect on cellular proliferation of 10 variants for the p16INK4a tumor suppressor, a cyclin-dependent kinase inhibitor encoded by the CDKN2A gene. Twenty-two pathogenicity predictors were assessed with a variety of accuracy measures for reliability in a medical context. Different assessment measures were combined in an overall ranking to provide more robust results. The R scripts used for assessment are publicly available from a GitHub repository for future use in similar assessment exercises. Despite a limited test-set size, our findings show a variety of results, with some methods performing significantly better. Methods combining different strategies frequently outperform simpler approaches. The best predictor, Yang&Zhou lab, uses a machine learning method combining an empirical energy function measuring protein stability with an evolutionary conservation term. The p16INK4a challenge highlights how subtle structural effects can neutralize otherwise deleterious variants.
Collapse
Affiliation(s)
- Marco Carraro
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | | | - Manuel Giollo
- Department of Biomedical Sciences, University of Padova, Padova, Italy.,Department of Information Engineering, University of Padova, Padova, Italy
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey.,Department of Genetics, Rutgers University, Piscataway, New Jersey.,Technical University of Munich Institute for Advanced Study (TUM-IAS), Garching/Munich, Germany
| | - Emidio Capriotti
- BioFolD Unit, Department of Biological, Geological, and Environmental Sciences (BiGeA), University of Bologna, Bologna, Italy
| | - Rita Casadio
- Biocomputing Group, Department of Biological, Geological, and Environmental Sciences (BiGeA), University of Bologna, Bologna, Italy
| | - Roland Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, Pennsylvania
| | - Lisa Elefanti
- Immunology and Molecular Oncology Unit, Veneto Institute of Oncology, Padua, Italy
| | - Pietro Fariselli
- Department of Comparative Biomedicine and Food Science, University of Padua, viale dell'Università 16, 35020, Legnaro (PD), Italy
| | - Carlo Ferrari
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Julian Gough
- Department of Computer Science, University of Bristol, Bristol, UK
| | - Panagiotis Katsonis
- Department of Human and Molecular Genetics, Baylor College of Medicine, Houston, Texas
| | - Emanuela Leonardi
- Department of Woman and Child Health, University of Padova, Padova, Italy
| | - Olivier Lichtarge
- Department of Human and Molecular Genetics, Baylor College of Medicine, Houston, Texas.,Department of Biochemistry & Molecular Biology, Baylor College of Medicine, Houston, Texas.,Department of Pharmacology, Baylor College of Medicine, Houston, Texas.,Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas
| | - Chiara Menin
- Immunology and Molecular Oncology Unit, Veneto Institute of Oncology, Padua, Italy
| | - Pier Luigi Martelli
- BioFolD Unit, Department of Biological, Geological, and Environmental Sciences (BiGeA), University of Bologna, Bologna, Italy
| | - Abhishek Niroula
- Protein Structure and Bioinformatics Group, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Lipika R Pal
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland
| | - Susanna Repo
- EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Maria Chiara Scaini
- Immunology and Molecular Oncology Unit, Veneto Institute of Oncology, Padua, Italy
| | - Mauno Vihinen
- Protein Structure and Bioinformatics Group, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Qiong Wei
- Biocomputing Group, Department of Biological, Geological, and Environmental Sciences (BiGeA), University of Bologna, Bologna, Italy
| | - Qifang Xu
- Biocomputing Group, Department of Biological, Geological, and Environmental Sciences (BiGeA), University of Bologna, Bologna, Italy
| | - Yuedong Yang
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Gold Coast, Queensland, Australia
| | - Yizhou Yin
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland.,Computational Biology, Bioinformatics and Genomics, Biological Sciences Graduate Program, University of Maryland, College Park, Maryland
| | - Jan Zaucha
- Department of Computer Science, University of Bristol, Bristol, UK
| | - Huiying Zhao
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia
| | - Yaoqi Zhou
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Gold Coast, Queensland, Australia
| | - Steven E Brenner
- Department of Plant and Microbial Biology, University of California, Berkeley, California
| | - John Moult
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland.,Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Silvio C E Tosatto
- Department of Biomedical Sciences, University of Padova, Padova, Italy.,CNR Institute of Neuroscience, Padova, Italy
| |
Collapse
|
16
|
Pfeiffenberger E, Chaleil RA, Moal IH, Bates PA. A machine learning approach for ranking clusters of docked protein-protein complexes by pairwise cluster comparison. Proteins 2017; 85:528-543. [PMID: 27935158 PMCID: PMC5396268 DOI: 10.1002/prot.25218] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Revised: 11/14/2016] [Accepted: 11/21/2016] [Indexed: 01/28/2023]
Abstract
Reliable identification of near-native poses of docked protein-protein complexes is still an unsolved problem. The intrinsic heterogeneity of protein-protein interactions is challenging for traditional biophysical or knowledge based potentials and the identification of many false positive binding sites is not unusual. Often, ranking protocols are based on initial clustering of docked poses followed by the application of an energy function to rank each cluster according to its lowest energy member. Here, we present an approach of cluster ranking based not only on one molecular descriptor (e.g., an energy function) but also employing a large number of descriptors that are integrated in a machine learning model, whereby, an extremely randomized tree classifier based on 109 molecular descriptors is trained. The protocol is based on first locally enriching clusters with additional poses, the clusters are then characterized using features describing the distribution of molecular descriptors within the cluster, which are combined into a pairwise cluster comparison model to discriminate near-native from incorrect clusters. The results show that our approach is able to identify clusters containing near-native protein-protein complexes. In addition, we present an analysis of the descriptors with respect to their power to discriminate near native from incorrect clusters and how data transformations and recursive feature elimination can improve the ranking performance. Proteins 2017; 85:528-543. © 2016 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
| | | | - Iain H. Moal
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute, Wellcome Trust Genome Campus, HinxtonCambridgeCB10 1SDUK
| | - Paul A. Bates
- Biomolecular Modelling LaboratoryThe Francis Crick InstituteLondonNW1 1ATUK
| |
Collapse
|
17
|
Vamparys L, Laurent B, Carbone A, Sacquin-Mora S. Great interactions: How binding incorrect partners can teach us about protein recognition and function. Proteins 2016; 84:1408-21. [PMID: 27287388 PMCID: PMC5516155 DOI: 10.1002/prot.25086] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Revised: 06/01/2016] [Accepted: 06/02/2016] [Indexed: 12/29/2022]
Abstract
Protein–protein interactions play a key part in most biological processes and understanding their mechanism is a fundamental problem leading to numerous practical applications. The prediction of protein binding sites in particular is of paramount importance since proteins now represent a major class of therapeutic targets. Amongst others methods, docking simulations between two proteins known to interact can be a useful tool for the prediction of likely binding patches on a protein surface. From the analysis of the protein interfaces generated by a massive cross‐docking experiment using the 168 proteins of the Docking Benchmark 2.0, where all possible protein pairs, and not only experimental ones, have been docked together, we show that it is also possible to predict a protein's binding residues without having any prior knowledge regarding its potential interaction partners. Evaluating the performance of cross‐docking predictions using the area under the specificity‐sensitivity ROC curve (AUC) leads to an AUC value of 0.77 for the complete benchmark (compared to the 0.5 AUC value obtained for random predictions). Furthermore, a new clustering analysis performed on the binding patches that are scattered on the protein surface show that their distribution and growth will depend on the protein's functional group. Finally, in several cases, the binding‐site predictions resulting from the cross‐docking simulations will lead to the identification of an alternate interface, which corresponds to the interaction with a biomolecular partner that is not included in the original benchmark. Proteins 2016; 84:1408–1421. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Lydie Vamparys
- Laboratoire De Biochimie Théorique, CNRS UPR 9080, Institut De Biologie Physico-Chimique, 13 Rue Pierre Et Marie Curie, Paris, 75005, France
| | - Benoist Laurent
- Laboratoire De Biochimie Théorique, CNRS UPR 9080, Institut De Biologie Physico-Chimique, 13 Rue Pierre Et Marie Curie, Paris, 75005, France
| | - Alessandra Carbone
- Sorbonne Universités, UPMC Univ-Paris 6, CNRS UMR7238, Laboratoire De Biologie Computationnelle Et Quantitative, 15 Rue De L'Ecole De Médecine, Paris, 75006, France.,Institut Universitaire De France, Paris, 75005, France
| | - Sophie Sacquin-Mora
- Laboratoire De Biochimie Théorique, CNRS UPR 9080, Institut De Biologie Physico-Chimique, 13 Rue Pierre Et Marie Curie, Paris, 75005, France.
| |
Collapse
|
18
|
Zhang H, Chang YC, Huang Q, Brennan ML, Wu J. Structural and Functional Analysis of a Talin Triple-Domain Module Suggests an Alternative Talin Autoinhibitory Configuration. Structure 2016; 24:721-729. [PMID: 27150043 DOI: 10.1016/j.str.2016.02.020] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2015] [Revised: 02/23/2016] [Accepted: 02/23/2016] [Indexed: 11/17/2022]
Abstract
Talin plays an important role in regulating integrin-mediated signaling. Talin function is autoinhibited by intramolecular interactions between the integrin-binding F3 domain and the autoinhibitory domain (R9). We determined the crystal structure of a triple-domain fragment, R7R8R9, which contains R9 and the RIAM (Rap1-interacting adaptor molecule) binding domain (R8). The structure reveals a crystallographic contact between R9 and a symmetrically related R8 domain, representing a homodimeric interaction in talin. Strikingly, we demonstrated that the α5 helix of R9 also interacts with the F3 domain, despite no interdomain contact involving the α5 helix in the crystal structure of an F2F3:R9 autoinhibitory complex reported previously. Mutations on the α5 helix significantly diminish the F3:R9 association and lead to elevated talin activity. Our results offer biochemical and functional evidence of the existence of a new talin autoinhibitory configuration, thus providing a more comprehensive understanding of talin autoinhibition, regulation, and quaternary structure assembly.
Collapse
Affiliation(s)
- Hao Zhang
- Molecular Therapeutics Program, Fox Chase Cancer Center, Philadelphia, PA 19111, USA
| | - Yu-Chung Chang
- Molecular Therapeutics Program, Fox Chase Cancer Center, Philadelphia, PA 19111, USA
| | | | - Mark L Brennan
- Molecular Therapeutics Program, Fox Chase Cancer Center, Philadelphia, PA 19111, USA
| | - Jinhua Wu
- Molecular Therapeutics Program, Fox Chase Cancer Center, Philadelphia, PA 19111, USA.
| |
Collapse
|
19
|
Sudha G, Singh P, Swapna LS, Srinivasan N. Weak conservation of structural features in the interfaces of homologous transient protein-protein complexes. Protein Sci 2015; 24:1856-73. [PMID: 26311309 DOI: 10.1002/pro.2792] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Revised: 08/13/2015] [Accepted: 08/17/2015] [Indexed: 12/21/2022]
Abstract
Residue types at the interface of protein-protein complexes (PPCs) are known to be reasonably well conserved. However, we show, using a dataset of known 3-D structures of homologous transient PPCs, that the 3-D location of interfacial residues and their interaction patterns are only moderately and poorly conserved, respectively. Another surprising observation is that a residue at the interface that is conserved is not necessarily in the interface in the homolog. Such differences in homologous complexes are manifested by substitution of the residues that are spatially proximal to the conserved residue and structural differences at the interfaces as well as differences in spatial orientations of the interacting proteins. Conservation of interface location and the interaction pattern at the core of the interfaces is higher than at the periphery of the interface patch. Extents of variability of various structural features reported here for homologous transient PPCs are higher than the variation in homologous permanent homomers. Our findings suggest that straightforward extrapolation of interfacial nature and inter-residue interaction patterns from template to target could lead to serious errors in the modeled complex structure. Understanding the evolution of interfaces provides insights to improve comparative modeling of PPC structures.
Collapse
Affiliation(s)
- Govindarajan Sudha
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, 560012, Karnataka, India
| | - Prashant Singh
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, 560012, Karnataka, India
| | - Lakshmipuram S Swapna
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, 560012, Karnataka, India
| | | |
Collapse
|
20
|
Sudha G, Nussinov R, Srinivasan N. An overview of recent advances in structural bioinformatics of protein-protein interactions and a guide to their principles. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2014; 116:141-50. [PMID: 25077409 DOI: 10.1016/j.pbiomolbio.2014.07.004] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2014] [Accepted: 07/13/2014] [Indexed: 12/20/2022]
Abstract
Rich data bearing on the structural and evolutionary principles of protein-protein interactions are paving the way to a better understanding of the regulation of function in the cell. This is particularly the case when these interactions are considered in the framework of key pathways. Knowledge of the interactions may provide insights into the mechanisms of crucial 'driver' mutations in oncogenesis. They also provide the foundation toward the design of protein-protein interfaces and inhibitors that can abrogate their formation or enhance them. The main features to learn from known 3-D structures of protein-protein complexes and the extensive literature which analyzes them computationally and experimentally include the interaction details which permit undertaking structure-based drug discovery, the evolution of complexes and their interactions, the consequences of alterations such as post-translational modifications, ligand binding, disease causing mutations, host pathogen interactions, oligomerization, aggregation and the roles of disorder, dynamics, allostery and more to the protein and the cell. This review highlights some of the recent advances in these areas, including design, inhibition and prediction of protein-protein complexes. The field is broad, and much work has been carried out in these areas, making it challenging to cover it in its entirety. Much of this is due to the fast increase in the number of molecules whose structures have been determined experimentally and the vast increase in computational power. Here we provide a concise overview.
Collapse
Affiliation(s)
- Govindarajan Sudha
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India.
| | - Ruth Nussinov
- Cancer and Inflammation Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., National Cancer Institute, Frederick, MD 21702, USA; Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | | |
Collapse
|
21
|
Goncearenco A, Shoemaker BA, Zhang D, Sarychev A, Panchenko AR. Coverage of protein domain families with structural protein-protein interactions: current progress and future trends. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2014; 116:187-93. [PMID: 24931138 DOI: 10.1016/j.pbiomolbio.2014.05.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2013] [Revised: 04/14/2014] [Accepted: 05/17/2014] [Indexed: 11/16/2022]
Abstract
Protein interactions have evolved into highly precise and regulated networks adding an immense layer of complexity to cellular systems. The most accurate atomistic description of protein binding sites can be obtained directly from structures of protein complexes. The availability of structurally characterized protein interfaces significantly improves our understanding of interactomes, and the progress in structural characterization of protein-protein interactions (PPIs) can be measured by calculating the structural coverage of protein domain families. We analyze the coverage of protein domain families (defined according to CDD and Pfam databases) by structures, structural protein-protein complexes and unique protein binding sites. Structural PPI coverage of currently available protein families is about 30% without any signs of saturation in coverage growth dynamics. Given the current growth rates of domain databases and structural PPI deposition, complete domain coverage with PPIs is not expected in the near future. As a result of this study we identify families without any protein-protein interaction evidence (listed on a supporting website http://www.ncbi.nlm.nih.gov/Structure/ibis/coverage/) and propose them as potential targets for structural studies with a focus on protein interactions.
Collapse
Affiliation(s)
- Alexander Goncearenco
- Computational Biology Branch of the National Center for Biotechnology Information in Bethesda, Maryland, United States
| | - Benjamin A Shoemaker
- Computational Biology Branch of the National Center for Biotechnology Information in Bethesda, Maryland, United States
| | - Dachuan Zhang
- Computational Biology Branch of the National Center for Biotechnology Information in Bethesda, Maryland, United States
| | - Alexey Sarychev
- Computational Biology Branch of the National Center for Biotechnology Information in Bethesda, Maryland, United States
| | - Anna R Panchenko
- Computational Biology Branch of the National Center for Biotechnology Information in Bethesda, Maryland, United States.
| |
Collapse
|
22
|
BioAssemblyModeler (BAM): user-friendly homology modeling of protein homo- and heterooligomers. PLoS One 2014; 9:e98309. [PMID: 24922057 PMCID: PMC4055448 DOI: 10.1371/journal.pone.0098309] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2013] [Accepted: 04/30/2014] [Indexed: 01/11/2023] Open
Abstract
Many if not most proteins function in oligomeric assemblies of one or more protein sequences. The Protein Data Bank provides coordinates for biological assemblies for each entry, at least 60% of which are dimers or larger assemblies. BioAssemblyModeler (BAM) is a graphical user interface to the basic steps in homology modeling of protein homooligomers and heterooligomers from the biological assemblies provided in the PDB. BAM takes as input up to six different protein sequences and begins by assigning Pfam domains to the target sequences. The program utilizes a complete assignment of Pfam domains to sequences in the PDB, PDBfam (http://dunbrack2.fccc.edu/protcid/pdbfam), to obtain templates that contain any or all of the domains assigned to the target sequence(s). The contents of the biological assemblies of potential templates are provided, and alignments of the target sequences to the templates are produced with a profile-profile alignment algorithm. BAM provides for visual examination and mouse-editing of the alignments supported by target and template secondary structure information and a 3D viewer of the template biological assembly. Side-chain coordinates for a model of the biological assembly are built with the program SCWRL4. A built-in protocol navigation system guides the user through all stages of homology modeling from input sequences to a three-dimensional model of the target complex. Availability: http://dunbrack.fccc.edu/BAM.
Collapse
|
23
|
Yachdav G, Kloppmann E, Kajan L, Hecht M, Goldberg T, Hamp T, Hönigschmid P, Schafferhans A, Roos M, Bernhofer M, Richter L, Ashkenazy H, Punta M, Schlessinger A, Bromberg Y, Schneider R, Vriend G, Sander C, Ben-Tal N, Rost B. PredictProtein--an open resource for online prediction of protein structural and functional features. Nucleic Acids Res 2014; 42:W337-43. [PMID: 24799431 PMCID: PMC4086098 DOI: 10.1093/nar/gku366] [Citation(s) in RCA: 435] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
PredictProtein is a meta-service for sequence analysis that has been predicting
structural and functional features of proteins since 1992. Queried with a
protein sequence it returns: multiple sequence alignments, predicted aspects of
structure (secondary structure, solvent accessibility, transmembrane helices
(TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered
regions) and function. The service incorporates analysis methods for the
identification of functional regions (ConSurf), homology-based inference of Gene
Ontology terms (metastudent), comprehensive subcellular localization prediction
(LocTree3), protein–protein binding sites (ISIS2),
protein–polynucleotide binding sites (SomeNA) and predictions of the
effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our
goal has always been to develop a system optimized to meet the demands of
experimentalists not highly experienced in bioinformatics. To this end, the
PredictProtein results are presented as both text and a series of intuitive,
interactive and visually appealing figures. The web server and sources are
available at http://ppopen.rostlab.org.
Collapse
Affiliation(s)
- Guy Yachdav
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany Biosof LLC, New York, NY 10001, USA TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Edda Kloppmann
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany New York Consortium on Membrane Protein Structure (NYCOMPS), Columbia University, New York, NY 10032, USA
| | - Laszlo Kajan
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Maximilian Hecht
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Tatyana Goldberg
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Tobias Hamp
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Peter Hönigschmid
- Department of Genome Oriented Bioinformatics, Technische Universität München, Wissenschaftszentrum Weihenstephan, Freising 85354, Germany
| | - Andrea Schafferhans
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Manfred Roos
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Michael Bernhofer
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Lothar Richter
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Haim Ashkenazy
- The Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, 69978 Tel Aviv, Israel
| | - Marco Punta
- Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK Institute for Food and Plant Sciences WZW-Weihenstephan, Alte Akademie 8, Freising 85350, Germany
| | - Avner Schlessinger
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Yana Bromberg
- Biosof LLC, New York, NY 10001, USA Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Reinhard Schneider
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08901, USA
| | - Gerrit Vriend
- Luxembourg University & Luxembourg Centre for Systems Biomedicine, 4362 Belval, Luxembourg
| | - Chris Sander
- CMBI, NCMLS, Radboudumc Nijmegen Medical Centre, 6525 GA Nijmegen, The Netherlands
| | - Nir Ben-Tal
- Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, 10065 NY, USA
| | - Burkhard Rost
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany Biosof LLC, New York, NY 10001, USA New York Consortium on Membrane Protein Structure (NYCOMPS), Columbia University, New York, NY 10032, USA The Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, 69978 Tel Aviv, Israel Department of Biochemistry and Molecular Biophysics & New York Consortium on Membrane Protein Structure (NYCOMPS), Columbia University, New York, NY 10032, USA Institute for Advanced Study (TUM-IAS), Garching/Munich 85748, Germany
| |
Collapse
|
24
|
Cukuroglu E, Gursoy A, Nussinov R, Keskin O. Non-redundant unique interface structures as templates for modeling protein interactions. PLoS One 2014; 9:e86738. [PMID: 24475173 PMCID: PMC3903793 DOI: 10.1371/journal.pone.0086738] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2013] [Accepted: 12/18/2013] [Indexed: 01/16/2023] Open
Abstract
Improvements in experimental techniques increasingly provide structural data relating to protein-protein interactions. Classification of structural details of protein-protein interactions can provide valuable insights for modeling and abstracting design principles. Here, we aim to cluster protein-protein interactions by their interface structures, and to exploit these clusters to obtain and study shared and distinct protein binding sites. We find that there are 22604 unique interface structures in the PDB. These unique interfaces, which provide a rich resource of structural data of protein-protein interactions, can be used for template-based docking. We test the specificity of these non-redundant unique interface structures by finding protein pairs which have multiple binding sites. We suggest that residues with more than 40% relative accessible surface area should be considered as surface residues in template-based docking studies. This comprehensive study of protein interface structures can serve as a resource for the community. The dataset can be accessed at http://prism.ccbb.ku.edu.tr/piface.
Collapse
Affiliation(s)
- Engin Cukuroglu
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Istanbul, Turkey
| | - Attila Gursoy
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Istanbul, Turkey
| | - Ruth Nussinov
- National Cancer Institute, Cancer and Inflammation Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., National Cancer Institute, Frederick, Maryland, United States of America
- Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ozlem Keskin
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Istanbul, Turkey
| |
Collapse
|
25
|
Agius R, Torchala M, Moal IH, Fernández-Recio J, Bates PA. Characterizing changes in the rate of protein-protein dissociation upon interface mutation using hotspot energy and organization. PLoS Comput Biol 2013; 9:e1003216. [PMID: 24039569 PMCID: PMC3764008 DOI: 10.1371/journal.pcbi.1003216] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2013] [Accepted: 07/25/2013] [Indexed: 12/21/2022] Open
Abstract
Predicting the effects of mutations on the kinetic rate constants of protein-protein interactions is central to both the modeling of complex diseases and the design of effective peptide drug inhibitors. However, while most studies have concentrated on the determination of association rate constants, dissociation rates have received less attention. In this work we take a novel approach by relating the changes in dissociation rates upon mutation to the energetics and architecture of hotspots and hotregions, by performing alanine scans pre- and post-mutation. From these scans, we design a set of descriptors that capture the change in hotspot energy and distribution. The method is benchmarked on 713 kinetically characterized mutations from the SKEMPI database. Our investigations show that, with the use of hotspot descriptors, energies from single-point alanine mutations may be used for the estimation of off-rate mutations to any residue type and also multi-point mutations. A number of machine learning models are built from a combination of molecular and hotspot descriptors, with the best models achieving a Pearson's Correlation Coefficient of 0.79 with experimental off-rates and a Matthew's Correlation Coefficient of 0.6 in the detection of rare stabilizing mutations. Using specialized feature selection models we identify descriptors that are highly specific and, conversely, broadly important to predicting the effects of different classes of mutations, interface regions and complexes. Our results also indicate that the distribution of the critical stability regions across protein-protein interfaces is a function of complex size more strongly than interface area. In addition, mutations at the rim are critical for the stability of small complexes, but consistently harder to characterize. The relationship between hotregion size and the dissociation rate is also investigated and, using hotspot descriptors which model cooperative effects within hotregions, we show how the contribution of hotregions of different sizes, changes under different cooperative effects.
Collapse
Affiliation(s)
- Rudi Agius
- Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, United Kingdom
| | - Mieczyslaw Torchala
- Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, United Kingdom
| | - Iain H. Moal
- Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center, Barcelona, Spain
| | - Juan Fernández-Recio
- Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center, Barcelona, Spain
| | - Paul A. Bates
- Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, United Kingdom
| |
Collapse
|
26
|
Csermely P, Korcsmáros T, Kiss HJM, London G, Nussinov R. Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 2013; 138:333-408. [PMID: 23384594 PMCID: PMC3647006 DOI: 10.1016/j.pharmthera.2013.01.016] [Citation(s) in RCA: 506] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 01/22/2013] [Indexed: 02/02/2023]
Abstract
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.
Collapse
Affiliation(s)
- Peter Csermely
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary.
| | | | | | | | | |
Collapse
|