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Jarończyk M. Software for Predicting Binding Free Energy of Protein-Protein Complexes and Their Mutants. Methods Mol Biol 2024; 2780:139-147. [PMID: 38987468 DOI: 10.1007/978-1-0716-3985-6_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
Protein-protein binding affinity prediction is important for understanding complex biochemical pathways and to uncover protein interaction networks. Quantitative estimation of the binding affinity changes caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses. The binding free energies of protein-protein complexes can be predicted using several computational tools. This chapter is a summary of software developed for the prediction of binding free energies for protein-protein complexes and their mutants.
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2
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Hong X, Tong X, Xie J, Liu P, Liu X, Song Q, Liu S, Liu S. An updated dataset and a structure-based prediction model for protein-RNA binding affinity. Proteins 2023; 91:1245-1253. [PMID: 37186412 DOI: 10.1002/prot.26503] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 03/08/2023] [Accepted: 04/12/2023] [Indexed: 05/17/2023]
Abstract
Understanding the process of protein-RNA interaction is essential for structural biology. The thermodynamic process is an important part to uncover the protein-RNA interaction mechanism. The regulatory networks between protein and RNA in organisms are dominated by the binding or dissociation in the cells. Therefore, determining the binding affinity for protein-RNA complexes can help us to understand the regulation mechanism of protein-RNA interaction. Since it is time-consuming and labor-intensive to determine the binding affinity for protein-RNA complexes by experimental methods, it is necessary and urgent to develop computational methods to predict that. To develop a binding affinity prediction model, first we update the dataset of protein-RNA binding affinity benchmark (PRBAB), which includes 145 complexes now. Second, we extract the structural features based on complex structure, and then we analyze and select the representative structural features to train the regression model. Third, we random select the subset from the PRBAB2.0 to fit the protein-RNA binding affinity determined by experiment. In the end, we tested our model on the nonredundant PDBbind dataset, and the results showed that Pearson correlation coefficient r = .57 and RMSE = 2.51 kcal/mol. The Pearson correlation coefficient achieves 0.7 while removing 5 complex structures with modified residues/nucleotides and metal ions. While testing on ProNAB, the results showed that 71.60% of the prediction achieves Pearson correlation coefficient r = .61 and RMSE = 1.56 kcal/mol with experiment values.
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Affiliation(s)
- Xu Hong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xiaoxue Tong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Juan Xie
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Pinyu Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xudong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qi Song
- Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei University of Technology, Wuhan, China
| | - Sen Liu
- Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei University of Technology, Wuhan, China
| | - Shiyong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
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3
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Biswas G, Mukherjee D, Dutta N, Ghosh P, Basu S. EnCPdock: a web-interface for direct conjoint comparative analyses of complementarity and binding energetics in inter-protein associations. J Mol Model 2023; 29:239. [PMID: 37423912 DOI: 10.1007/s00894-023-05626-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 06/20/2023] [Indexed: 07/11/2023]
Abstract
CONTEXT Protein-protein interaction (PPI) is a key component linked to virtually all cellular processes. Be it an enzyme catalysis ('classic type functions' of proteins) or a signal transduction ('non-classic'), proteins generally function involving stable or quasi-stable multi-protein associations. The physical basis for such associations is inherent in the combined effect of shape and electrostatic complementarities (Sc, EC) of the interacting protein partners at their interface, which provides indirect probabilistic estimates of the stability and affinity of the interaction. While Sc is a necessary criterion for inter-protein associations, EC can be favorable as well as disfavored (e.g., in transient interactions). Estimating equilibrium thermodynamic parameters (∆Gbinding, Kd) by experimental means is costly and time consuming, thereby opening windows for computational structural interventions. Attempts to empirically probe ∆Gbinding from coarse-grain structural descriptors (primarily, surface area based terms) have lately been overtaken by physics-based, knowledge-based and their hybrid approaches (MM/PBSA, FoldX, etc.) that directly compute ∆Gbinding without involving intermediate structural descriptors. METHODS Here, we present EnCPdock ( https://www.scinetmol.in/EnCPdock/ ), a user-friendly web-interface for the direct conjoint comparative analyses of complementarity and binding energetics in proteins. EnCPdock returns an AI-predicted ∆Gbinding computed by combining complementarity (Sc, EC) and other high-level structural descriptors (input feature vectors), and renders a prediction accuracy comparable to the state-of-the-art. EnCPdock further locates a PPI complex in terms of its {Sc, EC} values (taken as an ordered pair) in the two-dimensional complementarity plot (CP). In addition, it also generates mobile molecular graphics of the interfacial atomic contact network for further analyses. EnCPdock also furnishes individual feature trends along with the relative probability estimates (Prfmax) of the obtained feature-scores with respect to the events of their highest observed frequencies. Together, these functionalities are of real practical use for structural tinkering and intervention as might be relevant in the design of targeted protein-interfaces. Combining all its features and applications, EnCPdock presents a unique online tool that should be beneficial to structural biologists and researchers across related fraternities.
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Affiliation(s)
- Gargi Biswas
- Department of Chemistry and Structural Biology, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Debasish Mukherjee
- Institute of Molecular Biology gGmbH (IMB), Ackermannweg 4, 55128, Mainz, Germany
| | - Nalok Dutta
- Dept of Biochemical Engineering, Faculty of Engineering Science, University College London, London, WC1E 6BT, UK
| | - Prithwi Ghosh
- Department of Botany, Narajole Raj College, Vidyasagar University, Midnapore, 721211, India
| | - Sankar Basu
- Department of Microbiology, Asutosh College (affiliated with University of Calcutta), 92, Shyama Prasad Mukherjee Rd, Bhowanipore, 700026, Kolkata, India.
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4
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Yang YX, Huang JY, Wang P, Zhu BT. AREA-AFFINITY: A Web Server for Machine Learning-Based Prediction of Protein-Protein and Antibody-Protein Antigen Binding Affinities. J Chem Inf Model 2023. [PMID: 37235532 DOI: 10.1021/acs.jcim.2c01499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Protein-Protein binding affinity reflects the binding strength between the binding partners. The prediction of protein-protein binding affinity is important for elucidating protein functions and also for designing protein-based therapeutics. The geometric characteristics such as area (both interface and surface areas) in the structure of a protein-protein complex play an important role in determining protein-protein interactions and their binding affinity. Here, we present a free web server for academic use, AREA-AFFINITY, for prediction of protein-protein or antibody-protein antigen binding affinity based on interface and surface areas in the structure of a protein-protein complex. AREA-AFFINITY implements 60 effective area-based protein-protein affinity predictive models and 37 effective area-based models specific for antibody-protein antigen binding affinity prediction developed in our recent studies. These models take into consideration the roles of interface and surface areas in binding affinity by using areas classified according to different amino acid types with different biophysical nature. The models with the best performances integrate machine learning methods such as neural network or random forest. These newly developed models have superior or comparable performance compared to the commonly used existing methods. AREA-AFFINITY is available for free at: https://affinity.cuhk.edu.cn/.
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Affiliation(s)
- Yong Xiao Yang
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, China
| | - Jin Yan Huang
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, China
| | - Pan Wang
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, China
| | - Bao Ting Zhu
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, China
- Shenzhen Bay Laboratory, Shenzhen, 518055, China
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5
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Yang YX, Wang P, Zhu BT. Relative importance of interface and surface areas in protein-protein binding affinity prediction: A machine learning analysis based on linear regression and artificial neural network. Biophys Chem 2022; 283:106762. [DOI: 10.1016/j.bpc.2022.106762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 01/11/2022] [Accepted: 01/14/2022] [Indexed: 11/02/2022]
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6
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Béreux S, Delmas B, Cazals F. Boosting the analysis of protein interfaces with multiple interface string alignments: Illustration on the spikes of coronaviruses. Proteins 2021; 90:848-857. [PMID: 34779026 DOI: 10.1002/prot.26279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 06/24/2021] [Accepted: 10/19/2021] [Indexed: 11/11/2022]
Abstract
We introduce multiple interface string alignment (MISA), a visualization tool to display coherently various sequence and structure based statistics at protein-protein interfaces (SSE elements, buried surface area, Δ ASA , B factor values, etc). The amino acids supporting these annotations are obtained from Voronoi interface models. The benefit of MISA is to collate annotated sequences of (homologous) chains found in different biological contexts, that is, bound with different partners or unbound. The aggregated views MISA/SSE, MISA/BSA, MISA/ΔASA, and so forth, make it trivial to identify commonalities and differences between chains, to infer key interface residues, and to understand where conformational changes occur upon binding. As such, they should prove of key relevance for knowledge-based annotations of protein databases such as the Protein Data Bank. Illustrations are provided on the receptor binding domain of coronaviruses, in complex with their cognate partner or (neutralizing) antibodies. MISA computed with a minimal number of structures complement and enrich findings previously reported. The corresponding package is available from the Structural Bioinformatics Library (http://sbl.inria.frand https://sbl.inria.fr/doc/Multiple_interface_string_alignment-user-manual.html).
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7
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Frutiger A, Tanno A, Hwu S, Tiefenauer RF, Vörös J, Nakatsuka N. Nonspecific Binding-Fundamental Concepts and Consequences for Biosensing Applications. Chem Rev 2021; 121:8095-8160. [PMID: 34105942 DOI: 10.1021/acs.chemrev.1c00044] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Nature achieves differentiation of specific and nonspecific binding in molecular interactions through precise control of biomolecules in space and time. Artificial systems such as biosensors that rely on distinguishing specific molecular binding events in a sea of nonspecific interactions have struggled to overcome this issue. Despite the numerous technological advancements in biosensor technologies, nonspecific binding has remained a critical bottleneck due to the lack of a fundamental understanding of the phenomenon. To date, the identity, cause, and influence of nonspecific binding remain topics of debate within the scientific community. In this review, we discuss the evolution of the concept of nonspecific binding over the past five decades based upon the thermodynamic, intermolecular, and structural perspectives to provide classification frameworks for biomolecular interactions. Further, we introduce various theoretical models that predict the expected behavior of biosensors in physiologically relevant environments to calculate the theoretical detection limit and to optimize sensor performance. We conclude by discussing existing practical approaches to tackle the nonspecific binding challenge in vitro for biosensing platforms and how we can both address and harness nonspecific interactions for in vivo systems.
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Affiliation(s)
- Andreas Frutiger
- Laboratory of Biosensors and Bioelectronics, Institute for Biomedical Engineering, ETH Zürich, Zürich CH-8092, Switzerland
| | - Alexander Tanno
- Laboratory of Biosensors and Bioelectronics, Institute for Biomedical Engineering, ETH Zürich, Zürich CH-8092, Switzerland
| | - Stephanie Hwu
- Laboratory of Biosensors and Bioelectronics, Institute for Biomedical Engineering, ETH Zürich, Zürich CH-8092, Switzerland
| | - Raphael F Tiefenauer
- Laboratory of Biosensors and Bioelectronics, Institute for Biomedical Engineering, ETH Zürich, Zürich CH-8092, Switzerland
| | - János Vörös
- Laboratory of Biosensors and Bioelectronics, Institute for Biomedical Engineering, ETH Zürich, Zürich CH-8092, Switzerland
| | - Nako Nakatsuka
- Laboratory of Biosensors and Bioelectronics, Institute for Biomedical Engineering, ETH Zürich, Zürich CH-8092, Switzerland
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8
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Mei LC, Hao GF, Yang GF. Computational methods for predicting hotspots at protein-RNA interfaces. WILEY INTERDISCIPLINARY REVIEWS-RNA 2021; 13:e1675. [PMID: 34080311 DOI: 10.1002/wrna.1675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 05/13/2021] [Accepted: 05/14/2021] [Indexed: 11/10/2022]
Abstract
Protein-RNA interactions play essential roles in many critical biological events. A comprehensive understanding of the mechanisms underlying these interactions is helpful when studying cellular activities and therapeutic applications. Hotspots are a small portion of residues contributing much toward protein-RNA binding affinity. In pharmaceutical research, the hotspot residues are seen as the best option for designing small molecules to target proteins of therapeutic interest. With the accumulation of experimental data about protein-RNA interactions, computational methods have been produced for hotspot prediction on a large scale. In this review, we first present an overview of the existing databases for protein-RNA binding data. Furthermore, we outline the most adopted computational methods for hotspots prediction in protein-RNA interactions. Finally, we discuss the applications of hotspot prediction. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA Methods > RNA Analyses In Vitro and In Silico.
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Affiliation(s)
- Long-Can Mei
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, China.,International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan, China
| | - Ge-Fei Hao
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, China.,International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan, China.,State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Research and Development Center for Fine Chemicals, Guizhou University, Guiyang, China
| | - Guang-Fu Yang
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, China.,International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan, China.,Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, China
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9
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Guest JD, Vreven T, Zhou J, Moal I, Jeliazkov JR, Gray JJ, Weng Z, Pierce BG. An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants. Structure 2021; 29:606-621.e5. [PMID: 33539768 DOI: 10.1016/j.str.2021.01.005] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 11/15/2020] [Accepted: 01/11/2021] [Indexed: 01/04/2023]
Abstract
Accurate predictive modeling of antibody-antigen complex structures and structure-based antibody design remain major challenges in computational biology, with implications for biotherapeutics, immunity, and vaccines. Through a systematic search for high-resolution structures of antibody-antigen complexes and unbound antibody and antigen structures, in conjunction with identification of experimentally determined binding affinities, we have assembled a non-redundant set of test cases for antibody-antigen docking and affinity prediction. This benchmark more than doubles the number of antibody-antigen complexes and corresponding affinities available in our previous benchmarks, providing an unprecedented view of the determinants of antibody recognition and insights into molecular flexibility. Initial assessments of docking and affinity prediction tools highlight the challenges posed by this diverse set of cases, which includes camelid nanobodies, therapeutic monoclonal antibodies, and broadly neutralizing antibodies targeting viral glycoproteins. This dataset will enable development of advanced predictive modeling and design methods for this therapeutically relevant class of protein-protein interactions.
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Affiliation(s)
- Johnathan D Guest
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA; Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
| | - Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Jing Zhou
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Iain Moal
- Computational Sciences, GlaxoSmithKline Research and Development, Stevenage SG1 2NY, UK
| | - Jeliazko R Jeliazkov
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA; Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA.
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10
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Nithin C, Mukherjee S, Bahadur RP. A structure-based model for the prediction of protein-RNA binding affinity. RNA (NEW YORK, N.Y.) 2019; 25:1628-1645. [PMID: 31395671 PMCID: PMC6859855 DOI: 10.1261/rna.071779.119] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Accepted: 08/05/2019] [Indexed: 05/28/2023]
Abstract
Protein-RNA recognition is highly affinity-driven and regulates a wide array of cellular functions. In this study, we have curated a binding affinity data set of 40 protein-RNA complexes, for which at least one unbound partner is available in the docking benchmark. The data set covers a wide affinity range of eight orders of magnitude as well as four different structural classes. On average, we find the complexes with single-stranded RNA have the highest affinity, whereas the complexes with the duplex RNA have the lowest. Nevertheless, free energy gain upon binding is the highest for the complexes with ribosomal proteins and the lowest for the complexes with tRNA with an average of -5.7 cal/mol/Å2 in the entire data set. We train regression models to predict the binding affinity from the structural and physicochemical parameters of protein-RNA interfaces. The best fit model with the lowest maximum error is provided with three interface parameters: relative hydrophobicity, conformational change upon binding and relative hydration pattern. This model has been used for predicting the binding affinity on a test data set, generated using mutated structures of yeast aspartyl-tRNA synthetase, for which experimentally determined ΔG values of 40 mutations are available. The predicted ΔGempirical values highly correlate with the experimental observations. The data set provided in this study should be useful for further development of the binding affinity prediction methods. Moreover, the model developed in this study enhances our understanding on the structural basis of protein-RNA binding affinity and provides a platform to engineer protein-RNA interfaces with desired affinity.
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Affiliation(s)
- Chandran Nithin
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | - Sunandan Mukherjee
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | - Ranjit Prasad Bahadur
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
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11
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An account of solvent accessibility in protein-RNA recognition. Sci Rep 2018; 8:10546. [PMID: 30002431 PMCID: PMC6043566 DOI: 10.1038/s41598-018-28373-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 06/21/2018] [Indexed: 01/16/2023] Open
Abstract
Protein–RNA recognition often induces conformational changes in binding partners. Consequently, the solvent accessible surface area (SASA) buried in contact estimated from the co-crystal structures may differ from that calculated using their unbound forms. To evaluate the change in accessibility upon binding, we compare SASA of 126 protein-RNA complexes between bound and unbound forms. We observe, in majority of cases the interface of both the binding partners gain accessibility upon binding, which is often associated with either large domain movements or secondary structural transitions in RNA-binding proteins (RBPs), and binding-induced conformational changes in RNAs. At the non-interface region, majority of RNAs lose accessibility upon binding, however, no such preference is observed for RBPs. Side chains of RBPs have major contribution in change in accessibility. In case of flexible binding, we find a moderate correlation between the binding free energy and change in accessibility at the interface. Finally, we introduce a parameter, the ratio of gain to loss of accessibility upon binding, which can be used to identify the native solution among the flexible docking models. Our findings provide fundamental insights into the relationship between flexibility and solvent accessibility, and advance our understanding on binding induced folding in protein-RNA recognition.
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12
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Bier D, Mittal S, Bravo-Rodriguez K, Sowislok A, Guillory X, Briels J, Heid C, Bartel M, Wettig B, Brunsveld L, Sanchez-Garcia E, Schrader T, Ottmann C. The Molecular Tweezer CLR01 Stabilizes a Disordered Protein-Protein Interface. J Am Chem Soc 2017; 139:16256-16263. [PMID: 29039919 PMCID: PMC5691318 DOI: 10.1021/jacs.7b07939] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Indexed: 12/13/2022]
Abstract
Protein regions that are involved in protein-protein interactions (PPIs) very often display a high degree of intrinsic disorder, which is reduced during the recognition process. A prime example is binding of the rigid 14-3-3 adapter proteins to their numerous partner proteins, whose recognition motifs undergo an extensive disorder-to-order transition. In this context, it is highly desirable to control this entropy-costly process using tailored stabilizing agents. This study reveals how the molecular tweezer CLR01 tunes the 14-3-3/Cdc25CpS216 protein-protein interaction. Protein crystallography, biophysical affinity determination and biomolecular simulations unanimously deliver a remarkable finding: a supramolecular "Janus" ligand can bind simultaneously to a flexible peptidic PPI recognition motif and to a well-structured adapter protein. This binding fills a gap in the protein-protein interface, "freezes" one of the conformational states of the intrinsically disordered Cdc25C protein partner and enhances the apparent affinity of the interaction. This is the first structural and functional proof of a supramolecular ligand targeting a PPI interface and stabilizing the binding of an intrinsically disordered recognition motif to a rigid partner protein.
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Affiliation(s)
- David Bier
- Laboratory
of Chemical Biology, Department of Biomedical Engineering and Institute
for Complex Molecular Systems, Eindhoven
University of Technology, Den Dolech 2, 5612 AZ Eindhoven, The Netherlands
- Department
of Chemistry, University of Duisburg-Essen, Universitätsstrasse 7, 45117 Essen, Germany
| | - Sumit Mittal
- Max-Planck-Institut
für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470 Mülheim an der Ruhr, Germany
| | - Kenny Bravo-Rodriguez
- Max-Planck-Institut
für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470 Mülheim an der Ruhr, Germany
| | - Andrea Sowislok
- Department
of Chemistry, University of Duisburg-Essen, Universitätsstrasse 7, 45117 Essen, Germany
| | - Xavier Guillory
- Laboratory
of Chemical Biology, Department of Biomedical Engineering and Institute
for Complex Molecular Systems, Eindhoven
University of Technology, Den Dolech 2, 5612 AZ Eindhoven, The Netherlands
- Department
of Chemistry, University of Duisburg-Essen, Universitätsstrasse 7, 45117 Essen, Germany
| | - Jeroen Briels
- Laboratory
of Chemical Biology, Department of Biomedical Engineering and Institute
for Complex Molecular Systems, Eindhoven
University of Technology, Den Dolech 2, 5612 AZ Eindhoven, The Netherlands
- Department
of Chemistry, University of Duisburg-Essen, Universitätsstrasse 7, 45117 Essen, Germany
| | - Christian Heid
- Department
of Chemistry, University of Duisburg-Essen, Universitätsstrasse 7, 45117 Essen, Germany
| | - Maria Bartel
- Laboratory
of Chemical Biology, Department of Biomedical Engineering and Institute
for Complex Molecular Systems, Eindhoven
University of Technology, Den Dolech 2, 5612 AZ Eindhoven, The Netherlands
| | - Burkhard Wettig
- Department
of Chemistry, University of Duisburg-Essen, Universitätsstrasse 7, 45117 Essen, Germany
| | - Luc Brunsveld
- Laboratory
of Chemical Biology, Department of Biomedical Engineering and Institute
for Complex Molecular Systems, Eindhoven
University of Technology, Den Dolech 2, 5612 AZ Eindhoven, The Netherlands
| | - Elsa Sanchez-Garcia
- Max-Planck-Institut
für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470 Mülheim an der Ruhr, Germany
| | - Thomas Schrader
- Department
of Chemistry, University of Duisburg-Essen, Universitätsstrasse 7, 45117 Essen, Germany
| | - Christian Ottmann
- Laboratory
of Chemical Biology, Department of Biomedical Engineering and Institute
for Complex Molecular Systems, Eindhoven
University of Technology, Den Dolech 2, 5612 AZ Eindhoven, The Netherlands
- Department
of Chemistry, University of Duisburg-Essen, Universitätsstrasse 7, 45117 Essen, Germany
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13
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Smith JK, Jiang S, Pfaendtner J. Redefining the Protein-Protein Interface: Coarse Graining and Combinatorics for an Improved Understanding of Amino Acid Contributions to the Protein-Protein Binding Affinity. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2017; 33:11511-11517. [PMID: 28850233 DOI: 10.1021/acs.langmuir.7b02438] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The ability to intervene in biological pathways has for decades been limited by the lack of a quantitative description of protein-protein interactions (PPIs). Herein we generate and compare millions of simple PPI models for insight into the mechanisms of specific recognition and binding. We use a coarse-grained approach whereby amino acids are counted in the interface, and these counts are used as binding affinity predictors. We perform lasso regression, a modern regression technique aimed at interpretability, with every possible amino acid combination (over 106 unique feature sets) to select only those amino acid predictors that provide more information than noise. This approach circumvents arbitrary binning and assumptions about the binding environment that obscure other binding affinity models. Aggregated analysis of these models trained at various interfacial cutoff distances informs the roles of specific amino acids in different binding contexts. We find that a simple amino acid count model outperforms detailed intermolecular contact and binned residue type models. We identify the prevalence of serine, glycine, and tryptophan in the interface as particularly important for predicting binding affinity across a range of distance cutoffs. Although current sample size limitations prevent a robust consensus model for binding affinity prediction, our approach underscores the relevance of a residue-based description of the protein-protein interface to increase our understanding of specific interactions.
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Affiliation(s)
- Josh K Smith
- Department of Chemical Engineering, University of Washington , Seattle, Washington 98195, United States
| | - Shaoyi Jiang
- Department of Chemical Engineering, University of Washington , Seattle, Washington 98195, United States
| | - Jim Pfaendtner
- Department of Chemical Engineering, University of Washington , Seattle, Washington 98195, United States
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14
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Marillet S, Lefranc MP, Boudinot P, Cazals F. Novel Structural Parameters of Ig-Ag Complexes Yield a Quantitative Description of Interaction Specificity and Binding Affinity. Front Immunol 2017; 8:34. [PMID: 28232828 PMCID: PMC5298999 DOI: 10.3389/fimmu.2017.00034] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 01/09/2017] [Indexed: 11/13/2022] Open
Abstract
Antibody–antigen complexes challenge our understanding, as analyses to date failed to unveil the key determinants of binding affinity and interaction specificity. We partially fill this gap based on novel quantitative analyses using two standardized databases, the IMGT/3Dstructure-DB and the structure affinity benchmark. First, we introduce a statistical analysis of interfaces which enables the classification of ligand types (protein, peptide, and chemical; cross-validated classification error of 9.6%) and yield binding affinity predictions of unprecedented accuracy (median absolute error of 0.878 kcal/mol). Second, we exploit the contributions made by CDRs in terms of position at the interface and atomic packing properties to show that in general, VH CDR3 and VL CDR3 make dominant contributions to the binding affinity, a fact also shown to be consistent with the enthalpy–entropy compensation associated with preconfiguration of CDR3. Our work suggests that the affinity prediction problem could be partially solved from databases of high resolution crystal structures of complexes with known affinity.
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Affiliation(s)
- Simon Marillet
- VIM, INRA and Université Paris-Saclay, Jouy-en-josas, France; Université Côte d'Azur and Inria, Sophia Antipolis, France
| | | | - Pierre Boudinot
- VIM, INRA and Université Paris-Saclay , Jouy-en-josas , France
| | - Frédéric Cazals
- Université Côte d'Azur and Inria , Sophia Antipolis , France
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15
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Integrating computational methods and experimental data for understanding the recognition mechanism and binding affinity of protein-protein complexes. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2017; 128:33-38. [PMID: 28069340 DOI: 10.1016/j.pbiomolbio.2017.01.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2016] [Revised: 01/04/2017] [Accepted: 01/05/2017] [Indexed: 01/09/2023]
Abstract
Protein-protein interactions perform several functions inside the cell. Understanding the recognition mechanism and binding affinity of protein-protein complexes is a challenging problem in experimental and computational biology. In this review, we focus on two aspects (i) understanding the recognition mechanism and (ii) predicting the binding affinity. The first part deals with computational techniques for identifying the binding site residues and the contribution of important interactions for understanding the recognition mechanism of protein-protein complexes in comparison with experimental observations. The second part is devoted to the methods developed for discriminating high and low affinity complexes, and predicting the binding affinity of protein-protein complexes using three-dimensional structural information and just from the amino acid sequence. The overall view enhances our understanding of the integration of experimental data and computational methods, recognition mechanism of protein-protein complexes and the binding affinity.
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16
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Computational Approaches for Predicting Binding Partners, Interface Residues, and Binding Affinity of Protein-Protein Complexes. Methods Mol Biol 2017; 1484:237-253. [PMID: 27787830 DOI: 10.1007/978-1-4939-6406-2_16] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Studying protein-protein interactions leads to a better understanding of the underlying principles of several biological pathways. Cost and labor-intensive experimental techniques suggest the need for computational methods to complement them. Several such state-of-the-art methods have been reported for analyzing diverse aspects such as predicting binding partners, interface residues, and binding affinity for protein-protein complexes with reliable performance. However, there are specific drawbacks for different methods that indicate the need for their improvement. This review highlights various available computational algorithms for analyzing diverse aspects of protein-protein interactions and endorses the necessity for developing new robust methods for gaining deep insights about protein-protein interactions.
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17
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Gromiha MM, Yugandhar K, Jemimah S. Protein-protein interactions: scoring schemes and binding affinity. Curr Opin Struct Biol 2016; 44:31-38. [PMID: 27866112 DOI: 10.1016/j.sbi.2016.10.016] [Citation(s) in RCA: 80] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Revised: 09/30/2016] [Accepted: 10/25/2016] [Indexed: 01/16/2023]
Abstract
Protein-protein interactions mediate several cellular functions, which can be understood from the information obtained using the three-dimensional structures of protein-protein complexes and binding affinity data. This review focuses on computational aspects of predicting the best native-like complex structure and binding affinities. The first part covers the prediction of protein-protein complex structures and the advantages of conformational searching and scoring functions in protein-protein docking. The second part is devoted to various aspects of protein-protein interaction thermodynamics, such as databases for binding affinities and other thermodynamic parameters, computational methods to predict the binding affinity using either the three-dimensional structures of complexes or amino acid sequences, and change in binding affinities of the complexes upon mutations. We provide the latest developments on protein-protein docking and binding affinity studies along with a list of available computational resources for understanding protein-protein interactions.
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Affiliation(s)
- M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India.
| | - K Yugandhar
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
| | - Sherlyn Jemimah
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
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18
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Sirin S, Apgar JR, Bennett EM, Keating AE. AB-Bind: Antibody binding mutational database for computational affinity predictions. Protein Sci 2016; 25:393-409. [PMID: 26473627 PMCID: PMC4815335 DOI: 10.1002/pro.2829] [Citation(s) in RCA: 87] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2015] [Revised: 10/09/2015] [Accepted: 10/12/2015] [Indexed: 12/26/2022]
Abstract
Antibodies (Abs) are a crucial component of the immune system and are often used as diagnostic and therapeutic agents. The need for high-affinity and high-specificity antibodies in research and medicine is driving the development of computational tools for accelerating antibody design and discovery. We report a diverse set of antibody binding data with accompanying structures that can be used to evaluate methods for modeling antibody interactions. Our Antibody-Bind (AB-Bind) database includes 1101 mutants with experimentally determined changes in binding free energies (ΔΔG) across 32 complexes. Using the AB-Bind data set, we evaluated the performance of protein scoring potentials in their ability to predict changes in binding free energies upon mutagenesis. Numerical correlations between computed and observed ΔΔG values were low (r = 0.16-0.45), but the potentials exhibited predictive power for classifying variants as improved vs weakened binders. Performance was evaluated using the area under the curve (AUC) for receiver operator characteristic (ROC) curves; the highest AUC values for 527 mutants with |ΔΔG| > 1.0 kcal/mol were 0.81, 0.87, and 0.88 using STATIUM, FoldX, and Discovery Studio scoring potentials, respectively. Some methods could also enrich for variants with improved binding affinity; FoldX and Discovery Studio were able to correctly rank 42% and 30%, respectively, of the 80 most improved binders (those with ΔΔG < -1.0 kcal/mol) in the top 5% of the database. This modest predictive performance has value but demonstrates the continuing need to develop and improve protein energy functions for affinity prediction.
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Affiliation(s)
- Sarah Sirin
- Department of BiologyMassachusetts Institute of TechnologyCambridgeMassachusetts02139
| | - James R. Apgar
- Global Biotherapeutics Technologies, Pfizer Inc610 Main StreetCambridgeMassachusetts02139
| | - Eric M. Bennett
- Global Biotherapeutics Technologies, Pfizer Inc610 Main StreetCambridgeMassachusetts02139
| | - Amy E. Keating
- Department of BiologyMassachusetts Institute of TechnologyCambridgeMassachusetts02139
- Department of Biological EngineeringMassachusetts Institute of TechnologyCambridgeMassachusetts02139
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19
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Broom HR, Vassall KA, Rumfeldt JAO, Doyle CM, Tong MS, Bonner JM, Meiering EM. Combined Isothermal Titration and Differential Scanning Calorimetry Define Three-State Thermodynamics of fALS-Associated Mutant Apo SOD1 Dimers and an Increased Population of Folded Monomer. Biochemistry 2016; 55:519-33. [PMID: 26710831 DOI: 10.1021/acs.biochem.5b01187] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Many proteins are naturally homooligomers, homodimers most frequently. The overall stability of oligomeric proteins may be described in terms of the stability of the constituent monomers and the stability of their association; together, these stabilities determine the populations of different monomer and associated species, which generally have different roles in the function or dysfunction of the protein. Here we show how a new combined calorimetry approach, using isothermal titration calorimetry to define monomer association energetics together with differential scanning calorimetry to measure total energetics of oligomer unfolding, can be used to analyze homodimeric unmetalated (apo) superoxide dismutase (SOD1) and determine the effects on the stability of structurally diverse mutations associated with amyotrophic lateral sclerosis (ALS). Despite being located throughout the protein, all mutations studied weaken the dimer interface, while concomitantly either decreasing or increasing the marginal stability of the monomer. Analysis of the populations of dimer, monomer, and unfolded monomer under physiological conditions of temperature, pH, and protein concentration shows that all mutations promote the formation of folded monomers. These findings may help rationalize the key roles proposed for monomer forms of SOD1 in neurotoxic aggregation in ALS, as well as roles for other forms of SOD1. Thus, the results obtained here provide a valuable approach for the quantitative analysis of homooligomeric protein stabilities, which can be used to elucidate the natural and aberrant roles of different forms of these proteins and to improve methods for predicting protein stabilities.
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Affiliation(s)
- Helen R Broom
- Department of Chemistry, University of Waterloo , Waterloo, Ontario N2L 3G1, Canada
| | - Kenrick A Vassall
- Department of Chemistry, University of Waterloo , Waterloo, Ontario N2L 3G1, Canada
| | - Jessica A O Rumfeldt
- Department of Chemistry, University of Waterloo , Waterloo, Ontario N2L 3G1, Canada
| | - Colleen M Doyle
- Department of Chemistry, University of Waterloo , Waterloo, Ontario N2L 3G1, Canada
| | - Ming Sze Tong
- Department of Chemistry, University of Waterloo , Waterloo, Ontario N2L 3G1, Canada
| | - Julia M Bonner
- Department of Chemistry, University of Waterloo , Waterloo, Ontario N2L 3G1, Canada
| | - Elizabeth M Meiering
- Department of Chemistry, University of Waterloo , Waterloo, Ontario N2L 3G1, Canada
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20
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Marillet S, Boudinot P, Cazals F. High-resolution crystal structures leverage protein binding affinity predictions. Proteins 2015; 84:9-20. [DOI: 10.1002/prot.24946] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Revised: 09/25/2015] [Accepted: 10/12/2015] [Indexed: 12/14/2022]
Affiliation(s)
- Simon Marillet
- Virologie Et Immunologie Moléculaires; INRA; Jouy-en-Josas France
- INRIA Sophia-Antipolis-Méditerraée, Algorithms-Biology-Structure; Sophia-Antipolis France
| | - Pierre Boudinot
- Virologie Et Immunologie Moléculaires; INRA; Jouy-en-Josas France
| | - Frédéric Cazals
- INRIA Sophia-Antipolis-Méditerraée, Algorithms-Biology-Structure; Sophia-Antipolis France
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21
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Chakravarty D, Janin J, Robert CH, Chakrabarti P. Changes in protein structure at the interface accompanying complex formation. IUCRJ 2015; 2:643-52. [PMID: 26594372 PMCID: PMC4645109 DOI: 10.1107/s2052252515015250] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Accepted: 08/16/2015] [Indexed: 06/05/2023]
Abstract
Protein interactions are essential in all biological processes. The changes brought about in the structure when a free component forms a complex with another molecule need to be characterized for a proper understanding of molecular recognition as well as for the successful implementation of docking algorithms. Here, unbound (U) and bound (B) forms of protein structures from the Protein-Protein Interaction Affinity Database are compared in order to enumerate the changes that occur at the interface atoms/residues in terms of the solvent-accessible surface area (ASA), secondary structure, temperature factors (B factors) and disorder-to-order transitions. It is found that the interface atoms optimize contacts with the atoms in the partner protein, which leads to an increase in their ASA in the bound interface in the majority (69%) of the proteins when compared with the unbound interface, and this is independent of the root-mean-square deviation between the U and B forms. Changes in secondary structure during the transition indicate a likely extension of helices and strands at the expense of turns and coils. A reduction in flexibility during complex formation is reflected in the decrease in B factors of the interface residues on going from the U form to the B form. There is, however, no distinction in flexibility between the interface and the surface in the monomeric structure, thereby highlighting the potential problem of using B factors for the prediction of binding sites in the unbound form for docking another protein. 16% of the proteins have missing (disordered) residues in the U form which are observed (ordered) in the B form, mostly with an irregular conformation; the data set also shows differences in the composition of interface and non-interface residues in the disordered polypeptide segments as well as differences in their surface burial.
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Affiliation(s)
- Devlina Chakravarty
- Department of Biochemistry, Bose Institute , P-1/12 CIT Scheme VIIM, Kolkata 700 054, India
| | - Joël Janin
- IBBMC, CNRS UMR 8619, Universite Paris-Sud 11 , Orsay, France
| | - Charles H Robert
- CNRS Laboratoire de Biochimie Theorique, Institut de Biologie Physico-Chimique (IBPC), Universite Paris Diderot, Sorbonne Paris Cité , 13 Rue Pierre et Marie Curie, 75005 Paris, France
| | - Pinak Chakrabarti
- Department of Biochemistry, Bose Institute , P-1/12 CIT Scheme VIIM, Kolkata 700 054, India
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22
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Broom HR, Rumfeldt JAO, Vassall KA, Meiering EM. Destabilization of the dimer interface is a common consequence of diverse ALS-associated mutations in metal free SOD1. Protein Sci 2015; 24:2081-9. [PMID: 26362407 DOI: 10.1002/pro.2803] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Revised: 09/02/2015] [Accepted: 09/11/2015] [Indexed: 11/08/2022]
Abstract
Neurotoxic misfolding of Cu, Zn-superoxide dismutase (SOD1) is implicated in causing amyotrophic lateral sclerosis, a devastating and incurable neurodegenerative disease. Disease-linked mutations in SOD1 have been proposed to promote misfolding and aggregation by decreasing protein stability and increasing the proportion of less folded forms of the protein. Here we report direct measurement of the thermodynamic effects of chemically and structurally diverse mutations on the stability of the dimer interface for metal free (apo) SOD1 using isothermal titration calorimetry and size exclusion chromatography. Remarkably, all mutations studied, even ones distant from the dimer interface, decrease interface stability, and increase the population of monomeric SOD1. We interpret the thermodynamic data to mean that substantial structural perturbations accompany dimer dissociation, resulting in the formation of poorly packed and malleable dissociated monomers. These findings provide key information for understanding the mechanisms and energetics underlying normal maturation of SOD1, as well as toxic SOD1 misfolding pathways associated with disease. Furthermore, accurate prediction of protein-protein association remains very difficult, especially when large structural changes are involved in the process, and our findings provide a quantitative set of data for such cases, to improve modelling of protein association.
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Affiliation(s)
- Helen R Broom
- Department of Chemistry, Guelph-Waterloo Centre for Graduate Work in Chemistry and Biochemistry, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada
| | - Jessica A O Rumfeldt
- Department of Chemistry, Guelph-Waterloo Centre for Graduate Work in Chemistry and Biochemistry, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada
| | - Kenrick A Vassall
- Department of Chemistry, Guelph-Waterloo Centre for Graduate Work in Chemistry and Biochemistry, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada
| | - Elizabeth M Meiering
- Department of Chemistry, Guelph-Waterloo Centre for Graduate Work in Chemistry and Biochemistry, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada
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23
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Vreven T, Moal IH, Vangone A, Pierce BG, Kastritis PL, Torchala M, Chaleil R, Jiménez-García B, Bates PA, Fernandez-Recio J, Bonvin AMJJ, Weng Z. Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2. J Mol Biol 2015; 427:3031-41. [PMID: 26231283 PMCID: PMC4677049 DOI: 10.1016/j.jmb.2015.07.016] [Citation(s) in RCA: 248] [Impact Index Per Article: 27.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Revised: 07/17/2015] [Accepted: 07/17/2015] [Indexed: 01/31/2023]
Abstract
We present an updated and integrated version of our widely used protein-protein docking and binding affinity benchmarks. The benchmarks consist of non-redundant, high-quality structures of protein-protein complexes along with the unbound structures of their components. Fifty-five new complexes were added to the docking benchmark, 35 of which have experimentally measured binding affinities. These updated docking and affinity benchmarks now contain 230 and 179 entries, respectively. In particular, the number of antibody-antigen complexes has increased significantly, by 67% and 74% in the docking and affinity benchmarks, respectively. We tested previously developed docking and affinity prediction algorithms on the new cases. Considering only the top 10 docking predictions per benchmark case, a prediction accuracy of 38% is achieved on all 55 cases and up to 50% for the 32 rigid-body cases only. Predicted affinity scores are found to correlate with experimental binding energies up to r=0.52 overall and r=0.72 for the rigid complexes.
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Affiliation(s)
- Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Iain H Moal
- Joint BSC-CRG-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center, C/Jordi Girona 29, 08034 Barcelona, Spain
| | - Anna Vangone
- Bijvoet Center for Biomolecular Research, Faculty of Science, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Brian G Pierce
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Panagiotis L Kastritis
- Bijvoet Center for Biomolecular Research, Faculty of Science, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Mieczyslaw Torchala
- Biomolecular Modelling Laboratory, The Francis Crick Institute, Lincoln's Inn Fields Laboratory, London WC2A 3LY, United Kingdom
| | - Raphael Chaleil
- Biomolecular Modelling Laboratory, The Francis Crick Institute, Lincoln's Inn Fields Laboratory, London WC2A 3LY, United Kingdom
| | - Brian Jiménez-García
- Joint BSC-CRG-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center, C/Jordi Girona 29, 08034 Barcelona, Spain
| | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, Lincoln's Inn Fields Laboratory, London WC2A 3LY, United Kingdom.
| | - Juan Fernandez-Recio
- Joint BSC-CRG-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center, C/Jordi Girona 29, 08034 Barcelona, Spain.
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science, Utrecht University, 3584CH Utrecht, The Netherlands.
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
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24
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Barik A, Nithin C, Karampudi NBR, Mukherjee S, Bahadur RP. Probing binding hot spots at protein-RNA recognition sites. Nucleic Acids Res 2015; 44:e9. [PMID: 26365245 PMCID: PMC4737170 DOI: 10.1093/nar/gkv876] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2015] [Accepted: 08/23/2015] [Indexed: 01/30/2023] Open
Abstract
We use evolutionary conservation derived from structure alignment of polypeptide sequences along with structural and physicochemical attributes of protein–RNA interfaces to probe the binding hot spots at protein–RNA recognition sites. We find that the degree of conservation varies across the RNA binding proteins; some evolve rapidly compared to others. Additionally, irrespective of the structural class of the complexes, residues at the RNA binding sites are evolutionary better conserved than those at the solvent exposed surfaces. For recognitions involving duplex RNA, residues interacting with the major groove are better conserved than those interacting with the minor groove. We identify multi-interface residues participating simultaneously in protein–protein and protein–RNA interfaces in complexes where more than one polypeptide is involved in RNA recognition, and show that they are better conserved compared to any other RNA binding residues. We find that the residues at water preservation site are better conserved than those at hydrated or at dehydrated sites. Finally, we develop a Random Forests model using structural and physicochemical attributes for predicting binding hot spots. The model accurately predicts 80% of the instances of experimental ΔΔG values in a particular class, and provides a stepping-stone towards the engineering of protein–RNA recognition sites with desired affinity.
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Affiliation(s)
- Amita Barik
- Computational Structural Biology Laboratory, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur-721302, India
| | - Chandran Nithin
- Computational Structural Biology Laboratory, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur-721302, India
| | | | - Sunandan Mukherjee
- Computational Structural Biology Laboratory, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur-721302, India
| | - Ranjit Prasad Bahadur
- Computational Structural Biology Laboratory, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur-721302, India Advanced Technology Development Centre, Indian Institute of Technology Kharagpur, Kharagpur-721302, India
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25
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Wassenaar TA, Pluhackova K, Moussatova A, Sengupta D, Marrink SJ, Tieleman DP, Böckmann RA. High-Throughput Simulations of Dimer and Trimer Assembly of Membrane Proteins. The DAFT Approach. J Chem Theory Comput 2015; 11:2278-91. [PMID: 26574426 DOI: 10.1021/ct5010092] [Citation(s) in RCA: 75] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Interactions between membrane proteins are of great biological significance and are consequently an important target for pharmacological intervention. Unfortunately, it is still difficult to obtain detailed views on such interactions, both experimentally, where the environment hampers atomic resolution investigation, and computationally, where the time and length scales are problematic. Coarse grain simulations have alleviated the later issue, but the slow movement through the bilayer, coupled to the long life times of nonoptimal dimers, still stands in the way of characterizing binding distributions. In this work, we present DAFT, a Docking Assay For Transmembrane components, developed to identify preferred binding orientations. The method builds on a program developed recently for generating custom membranes, called insane (INSert membrANE). The key feature of DAFT is the setup of starting structures, for which optimal periodic boundary conditions are devised. The purpose of DAFT is to perform a large number of simulations with different components, starting from unbiased noninteracting initial states, such that the simulations evolve collectively, in a manner reflecting the underlying energy landscape of interaction. The implementation and characteristic features of DAFT are explained, and the efficacy and relaxation properties of the method are explored for oligomerization of glycophorin A dimers, polyleucine dimers and trimers, MS1 trimers, and rhodopsin dimers. The results suggest that, for simple helices, such as GpA and polyleucine, in POPC/DOPC membranes series of 500 simulations of 500 ns each allow characterization of the helix dimer orientations and allow comparing associating and nonassociating components. However, the results also demonstrate that short simulations may suffer significantly from nonconvergence of the ensemble and that using too few simulations may obscure or distort features of the interaction distribution. For trimers, simulation times exceeding several microseconds appear needed, due to the increased complexity. Similarly, characterization of larger proteins, such as rhodopsin, takes longer time scales due to the slower diffusion and the increased complexity of binding interfaces. DAFT and its auxiliary programs have been made available from http://cgmartini.nl/ , together with a working example.
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Affiliation(s)
- Tsjerk A Wassenaar
- Computational Biology, Department of Biology, Friedrich-Alexander University of Erlangen-Nürnberg , Staudtstrasse 5, 91058 Erlangen, Germany
| | - Kristyna Pluhackova
- Computational Biology, Department of Biology, Friedrich-Alexander University of Erlangen-Nürnberg , Staudtstrasse 5, 91058 Erlangen, Germany
| | - Anastassiia Moussatova
- Department of Biological Sciences and Institute for Biocomplexity and Informatics, University of Calgary , 2500 University Drive NW, Calgary, Alberta, Canada T2N 1N4
| | - Durba Sengupta
- National Chemical Laboratory , Dr. Homi Bhabha Road, Pune 411008, India
| | - Siewert J Marrink
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen , Nijenborgh 7, 9747 AG Groningen, The Netherlands
| | - D Peter Tieleman
- Department of Biological Sciences and Institute for Biocomplexity and Informatics, University of Calgary , 2500 University Drive NW, Calgary, Alberta, Canada T2N 1N4
| | - Rainer A Böckmann
- Computational Biology, Department of Biology, Friedrich-Alexander University of Erlangen-Nürnberg , Staudtstrasse 5, 91058 Erlangen, Germany
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26
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Moal IH, Dapkūnas J, Fernández-Recio J. Inferring the microscopic surface energy of protein-protein interfaces from mutation data. Proteins 2015; 83:640-50. [PMID: 25586563 DOI: 10.1002/prot.24761] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 12/04/2014] [Accepted: 12/21/2014] [Indexed: 11/11/2022]
Abstract
Mutations at protein-protein recognition sites alter binding strength by altering the chemical nature of the interacting surfaces. We present a simple surface energy model, parameterized with empirical ΔΔG values, yielding mean energies of -48 cal mol(-1) Å(-2) for interactions between hydrophobic surfaces, -51 to -80 cal mol(-1) Å(-2) for surfaces of complementary charge, and 66-83 cal mol(-1) Å(-2) for electrostatically repelling surfaces, relative to the aqueous phase. This places the mean energy of hydrophobic surface burial at -24 cal mol(-1) Å(-2) . Despite neglecting configurational entropy and intramolecular changes, the model correlates with empirical binding free energies of a functionally diverse set of rigid-body interactions (r = 0.66). When used to rerank docking poses, it can place near-native solutions in the top 10 for 37% of the complexes evaluated, and 82% in the top 100. The method shows that hydrophobic burial is the driving force for protein association, accounting for 50-95% of the cohesive energy. The model is available open-source from http://life.bsc.es/pid/web/surface_energy/ and via the CCharpPPI web server http://life.bsc.es/pid/ccharppi/.
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Affiliation(s)
- Iain H Moal
- Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center, Barcelona, 08034, Spain
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