1
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Biswas G, Mukherjee D, Basu S. Combining Complementarity and Binding Energetics in the Assessment of Protein Interactions: EnCPdock-A Practical Manual. J Comput Biol 2024. [PMID: 38885081 DOI: 10.1089/cmb.2024.0554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024] Open
Abstract
The combined effect of shape and electrostatic complementarities (Sc, EC) at the interface of the interacting protein partners (PPI) serves as the physical basis for such associations and is a strong determinant of their binding energetics. EnCPdock (https://www.scinetmol.in/EnCPdock/) presents a comprehensive web platform for the direct conjoint comparative analyses of complementarity and binding energetics in PPIs. It elegantly interlinks the dual nature of local (Sc) and nonlocal complementarity (EC) in PPIs using the complementarity plot. It further derives an AI-based ΔGbinding with a prediction accuracy comparable to the state of the art. This book chapter presents a practical manual to conceptualize and implement EnCPdock with its various features and functionalities, collectively having the potential to serve as a valuable protein engineering tool in the design of novel protein interfaces.
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Affiliation(s)
- Gargi Biswas
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | | | - Sankar Basu
- Department of Microbiology, Asutosh College, University of Calcutta, Kolkata, India
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2
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Raisinghani N, Alshahrani M, Gupta G, Verkhivker G. Ensemble-Based Mutational Profiling and Network Analysis of the SARS-CoV-2 Spike Omicron XBB Lineages for Interactions with the ACE2 Receptor and Antibodies: Cooperation of Binding Hotspots in Mediating Epistatic Couplings Underlies Binding Mechanism and Immune Escape. Int J Mol Sci 2024; 25:4281. [PMID: 38673865 PMCID: PMC11049863 DOI: 10.3390/ijms25084281] [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: 03/13/2024] [Revised: 04/09/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
In this study, we performed a computational study of binding mechanisms for the SARS-CoV-2 spike Omicron XBB lineages with the host cell receptor ACE2 and a panel of diverse class one antibodies. The central objective of this investigation was to examine the molecular factors underlying epistatic couplings among convergent evolution hotspots that enable optimal balancing of ACE2 binding and antibody evasion for Omicron variants BA.1, BA2, BA.3, BA.4/BA.5, BQ.1.1, XBB.1, XBB.1.5, and XBB.1.5 + L455F/F456L. By combining evolutionary analysis, molecular dynamics simulations, and ensemble-based mutational scanning of spike protein residues in complexes with ACE2, we identified structural stability and binding affinity hotspots that are consistent with the results of biochemical studies. In agreement with the results of deep mutational scanning experiments, our quantitative analysis correctly reproduced strong and variant-specific epistatic effects in the XBB.1.5 and BA.2 variants. It was shown that Y453W and F456L mutations can enhance ACE2 binding when coupled with Q493 in XBB.1.5, while these mutations become destabilized when coupled with the R493 position in the BA.2 variant. The results provided a molecular rationale of the epistatic mechanism in Omicron variants, showing a central role of the Q493/R493 hotspot in modulating epistatic couplings between convergent mutational sites L455F and F456L in XBB lineages. The results of mutational scanning and binding analysis of the Omicron XBB spike variants with ACE2 receptors and a panel of class one antibodies provide a quantitative rationale for the experimental evidence that epistatic interactions of the physically proximal binding hotspots Y501, R498, Q493, L455F, and F456L can determine strong ACE2 binding, while convergent mutational sites F456L and F486P are instrumental in mediating broad antibody resistance. The study supports a mechanism in which the impact on ACE2 binding affinity is mediated through a small group of universal binding hotspots, while the effect of immune evasion could be more variant-dependent and modulated by convergent mutational sites in the conformationally adaptable spike regions.
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Affiliation(s)
- Nishank Raisinghani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (N.R.); (M.A.); (G.G.)
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (N.R.); (M.A.); (G.G.)
| | - Grace Gupta
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (N.R.); (M.A.); (G.G.)
| | - Gennady Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (N.R.); (M.A.); (G.G.)
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA
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3
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Raisinghani N, Alshahrani M, Gupta G, Xiao S, Tao P, Verkhivker G. AlphaFold2-Enabled Atomistic Modeling of Structure, Conformational Ensembles, and Binding Energetics of the SARS-CoV-2 Omicron BA.2.86 Spike Protein with ACE2 Host Receptor and Antibodies: Compensatory Functional Effects of Binding Hotspots in Modulating Mechanisms of Receptor Binding and Immune Escape. J Chem Inf Model 2024; 64:1657-1681. [PMID: 38373700 DOI: 10.1021/acs.jcim.3c01857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
The latest wave of SARS-CoV-2 Omicron variants displayed a growth advantage and increased viral fitness through convergent evolution of functional hotspots that work synchronously to balance fitness requirements for productive receptor binding and efficient immune evasion. In this study, we combined AlphaFold2-based structural modeling approaches with atomistic simulations and mutational profiling of binding energetics and stability for prediction and comprehensive analysis of the structure, dynamics, and binding of the SARS-CoV-2 Omicron BA.2.86 spike variant with ACE2 host receptor and distinct classes of antibodies. We adapted several AlphaFold2 approaches to predict both the structure and conformational ensembles of the Omicron BA.2.86 spike protein in the complex with the host receptor. The results showed that the AlphaFold2-predicted structural ensemble of the BA.2.86 spike protein complex with ACE2 can accurately capture the main conformational states of the Omicron variant. Complementary to AlphaFold2 structural predictions, microsecond molecular dynamics simulations reveal the details of the conformational landscape and produced equilibrium ensembles of the BA.2.86 structures that are used to perform mutational scanning of spike residues and characterize structural stability and binding energy hotspots. The ensemble-based mutational profiling of the receptor binding domain residues in the BA.2 and BA.2.86 spike complexes with ACE2 revealed a group of conserved hydrophobic hotspots and critical variant-specific contributions of the BA.2.86 convergent mutational hotspots R403K, F486P, and R493Q. To examine the immune evasion properties of BA.2.86 in atomistic detail, we performed structure-based mutational profiling of the spike protein binding interfaces with distinct classes of antibodies that displayed significantly reduced neutralization against the BA.2.86 variant. The results revealed the molecular basis of compensatory functional effects of the binding hotspots, showing that BA.2.86 lineage may have evolved to outcompete other Omicron subvariants by improving immune evasion while preserving binding affinity with ACE2 via through a compensatory effect of R493Q and F486P convergent mutational hotspots. This study demonstrated that an integrative approach combining AlphaFold2 predictions with complementary atomistic molecular dynamics simulations and robust ensemble-based mutational profiling of spike residues can enable accurate and comprehensive characterization of structure, dynamics, and binding mechanisms of newly emerging Omicron variants.
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Affiliation(s)
- Nishank Raisinghani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States of America
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States of America
| | - Grace Gupta
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States of America
| | - Sian Xiao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75275, United States of America
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75275, United States of America
| | - Gennady Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States of America
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, California 92618, United States of America
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4
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Yi C, Taylor ML, Ziebarth J, Wang Y. Predictive Models and Impact of Interfacial Contacts and Amino Acids on Protein-Protein Binding Affinity. ACS OMEGA 2024; 9:3454-3468. [PMID: 38284090 PMCID: PMC10809705 DOI: 10.1021/acsomega.3c06996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 12/11/2023] [Accepted: 12/14/2023] [Indexed: 01/30/2024]
Abstract
Protein-protein interactions (PPIs) play a central role in nearly all cellular processes. The strength of the binding in a PPI is characterized by the binding affinity (BA) and is a key factor in controlling protein-protein complex formation and defining the structure-function relationship. Despite advancements in understanding protein-protein binding, much remains unknown about the interfacial region and its association with BA. New models are needed to predict BA with improved accuracy for therapeutic design. Here, we use machine learning approaches to examine how well different types of interfacial contacts can be used to predict experimentally determined BA and to reveal the impact of the specific amino acids at the binding interface on BA. We create a series of multivariate linear regression models incorporating different contact features at both residue and atomic levels and examine how different methods of identifying and characterizing these properties impact the performance of these models. Particularly, we introduce a new and simple approach to predict BA based on the quantities of specific amino acids at the protein-protein interface. We found that the numbers of specific amino acids at the protein-protein interface were correlated with BA. We show that the interfacial numbers of amino acids can be used to produce models with consistently good performance across different data sets, indicating the importance of the identities of interfacial amino acids in underlying BA. When trained on a diverse set of complexes from two benchmark data sets, the best performing BA model was generated with an explicit linear equation involving six amino acids. Tyrosine, in particular, was identified as the key amino acid in controlling BA, as it had the strongest correlation with BA and was consistently identified as the most important amino acid in feature importance studies. Glycine and serine were identified as the next two most important amino acids in predicting BA. The results from this study further our understanding of PPIs and can be used to make improved predictions of BA, giving them implications for drug design and screening in the pharmaceutical industry.
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Affiliation(s)
- Carey
Huang Yi
- Department of Chemistry, The University of Memphis, Memphis, Tennessee 38152, United States
| | - Mitchell Lee Taylor
- Department of Chemistry, The University of Memphis, Memphis, Tennessee 38152, United States
| | - Jesse Ziebarth
- Department of Chemistry, The University of Memphis, Memphis, Tennessee 38152, United States
| | - Yongmei Wang
- Department of Chemistry, The University of Memphis, Memphis, Tennessee 38152, United States
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5
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Tsishyn M, Pucci F, Rooman M. Quantification of biases in predictions of protein-protein binding affinity changes upon mutations. Brief Bioinform 2023; 25:bbad491. [PMID: 38197311 PMCID: PMC10777193 DOI: 10.1093/bib/bbad491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 10/02/2023] [Accepted: 12/05/2023] [Indexed: 01/11/2024] Open
Abstract
Understanding the impact of mutations on protein-protein binding affinity is a key objective for a wide range of biotechnological applications and for shedding light on disease-causing mutations, which are often located at protein-protein interfaces. Over the past decade, many computational methods using physics-based and/or machine learning approaches have been developed to predict how protein binding affinity changes upon mutations. They all claim to achieve astonishing accuracy on both training and test sets, with performances on standard benchmarks such as SKEMPI 2.0 that seem overly optimistic. Here we benchmarked eight well-known and well-used predictors and identified their biases and dataset dependencies, using not only SKEMPI 2.0 as a test set but also deep mutagenesis data on the severe acute respiratory syndrome coronavirus 2 spike protein in complex with the human angiotensin-converting enzyme 2. We showed that, even though most of the tested methods reach a significant degree of robustness and accuracy, they suffer from limited generalizability properties and struggle to predict unseen mutations. Interestingly, the generalizability problems are more severe for pure machine learning approaches, while physics-based methods are less affected by this issue. Moreover, undesirable prediction biases toward specific mutation properties, the most marked being toward destabilizing mutations, are also observed and should be carefully considered by method developers. We conclude from our analyses that there is room for improvement in the prediction models and suggest ways to check, assess and improve their generalizability and robustness.
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Affiliation(s)
- Matsvei Tsishyn
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Roosevelt Ave, 1050, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Brussels, Belgium
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Roosevelt Ave, 1050, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Brussels, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Roosevelt Ave, 1050, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Brussels, Belgium
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6
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Nikam R, Yugandhar K, Gromiha MM. Deep learning-based method for predicting and classifying the binding affinity of protein-protein complexes. BIOCHIMICA ET BIOPHYSICA ACTA. PROTEINS AND PROTEOMICS 2023; 1871:140948. [PMID: 37567456 DOI: 10.1016/j.bbapap.2023.140948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 08/05/2023] [Accepted: 08/08/2023] [Indexed: 08/13/2023]
Abstract
Protein-protein interactions (PPIs) play a critical role in various biological processes. Accurately estimating the binding affinity of PPIs is essential for understanding the underlying molecular recognition mechanisms. In this study, we employed a deep learning approach to predict the binding affinity (ΔG) of protein-protein complexes. To this end, we compiled a dataset of 903 protein-protein complexes, each with its corresponding experimental binding affinity, which belong to six functional classes. We extracted 8 to 20 non-redundant features from the sequence information as well as the predicted three-dimensional structures using feature selection methods for each protein functional class. Our method showed an overall mean absolute error of 1.05 kcal/mol and a correlation of 0.79 between experimental and predicted ΔG values. Additionally, we evaluated our model for discriminating high and low affinity protein-protein complexes and it achieved an accuracy of 87% with an F1 score of 0.86 using 10-fold cross-validation on the selected features. Our approach presents an efficient tool for studying PPIs and provides crucial insights into the underlying mechanisms of the molecular recognition process. The web server can be freely accessed at https://web.iitm.ac.in/bioinfo2/DeepPPAPred/index.html.
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Affiliation(s)
- Rahul Nikam
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
| | - Kumar Yugandhar
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India; Department of Computational Biology, Cornell University, New York, USA
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India; Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan; Department of Computer Science, National University of Singapore, Singapore.
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7
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Ismail M, Martin SR, George R, Houghton F, Kelly G, Chaleil RAG, Anastasiou P, Wang X, O'Reilly N, Federico S, Joshi D, Nagaraj H, Cooley R, Hui NS, Molina-Arcas M, Hancock DC, Tavassoli A, Downward J. Characterisation of a cyclic peptide that binds to the RAS binding domain of phosphoinositide 3-kinase p110α. Sci Rep 2023; 13:1889. [PMID: 36732563 PMCID: PMC9894841 DOI: 10.1038/s41598-023-28756-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 01/24/2023] [Indexed: 02/04/2023] Open
Abstract
P110α is a member of the phosphoinositide 3-kinase (PI3K) enzyme family that functions downstream of RAS. RAS proteins contribute to the activation of p110α by interacting directly with its RAS binding domain (RBD), resulting in the promotion of many cellular functions such as cell growth, proliferation and survival. Previous work from our lab has highlighted the importance of the p110α/RAS interaction in tumour initiation and growth. Here we report the discovery and characterisation of a cyclic peptide inhibitor (cyclo-CRVLIR) that interacts with the p110α-RBD and blocks its interaction with KRAS. cyclo-CRVLIR was discovered by screening a "split-intein cyclisation of peptides and proteins" (SICLOPPS) cyclic peptide library. The primary cyclic peptide hit from the screen initially showed a weak affinity for the p110α-RBD (Kd about 360 µM). However, two rounds of amino acid substitution led to cyclo-CRVLIR, with an improved affinity for p110α-RBD in the low µM (Kd 3 µM). We show that cyclo-CRVLIR binds selectively to the p110α-RBD but not to KRAS or the structurally-related RAF-RBD. Further, using biophysical, biochemical and cellular assays, we show that cyclo-CRVLIR effectively blocks the p110α/KRAS interaction in a dose dependent manner and reduces phospho-AKT levels in several oncogenic KRAS cell lines.
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Affiliation(s)
- Mohamed Ismail
- Oncogene Biology Laboratory, Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Stephen R Martin
- Structural Biology, Science Technology Platforms, Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Roger George
- Structural Biology, Science Technology Platforms, Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Francesca Houghton
- Oncogene Biology Laboratory, Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Geoff Kelly
- Structural Biology, Science Technology Platforms, Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Raphaël A G Chaleil
- Biomolecular Modelling Lab, Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Panayiotis Anastasiou
- Oncogene Biology Laboratory, Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Xinyue Wang
- Oncogene Biology Laboratory, Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Nicola O'Reilly
- Peptide Chemistry, Science Technology Platforms, Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Stefania Federico
- Peptide Chemistry, Science Technology Platforms, Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Dhira Joshi
- Peptide Chemistry, Science Technology Platforms, Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Hemavathi Nagaraj
- Peptide Chemistry, Science Technology Platforms, Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Rachel Cooley
- Oncogene Biology Laboratory, Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Ning Sze Hui
- Oncogene Biology Laboratory, Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Miriam Molina-Arcas
- Oncogene Biology Laboratory, Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - David C Hancock
- Oncogene Biology Laboratory, Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Ali Tavassoli
- School of Chemistry, University of Southampton, Southampton, SO17 1BJ, UK
| | - Julian Downward
- Oncogene Biology Laboratory, Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK.
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Chitosan and HPMCAS double-coating as protective systems for alginate microparticles loaded with Ctx(Ile 21)-Ha antimicrobial peptide to prevent intestinal infections. Biomaterials 2023; 293:121978. [PMID: 36580719 DOI: 10.1016/j.biomaterials.2022.121978] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 11/03/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022]
Abstract
The incorrect use of conventional drugs for both prevention and control of intestinal infections has contributed to a significant spread of bacterial resistance. In this way, studies that promote their replacement are a priority. In the last decade, the use of antimicrobial peptides (AMP), especially Ctx(Ile21)-Ha AMP, has gained strength, demonstrating efficient antimicrobial activity (AA) against pathogens, including multidrug-resistant bacteria. However, gastrointestinal degradation does not allow its direct oral application. In this research, double-coating systems using alginate microparticles loaded with Ctx(Ile21)-Ha peptide were designed, and in vitro release assays simulating the gastrointestinal tract were evaluated. Also, the AA against Salmonella spp. and Escherichia coli was examined. The results showed the physicochemical stability of Ctx(Ile21)-Ha peptide in the system and its potent antimicrobial activity. In addition, the combination of HPMCAS and chitosan as a gastric protection system can be promising for peptide carriers or other low pH-sensitive molecules, adequately released in the intestine. In conclusion, the coated systems employed in this study can improve the formulation of new foods or biopharmaceutical products for specific application against intestinal pathogens in animal production or, possibly, in the near future, in human health.
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9
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Liu J, Xia KL, Wu J, Yau SST, Wei GW. Biomolecular Topology: Modelling and Analysis. ACTA MATHEMATICA SINICA, ENGLISH SERIES 2022; 38:1901-1938. [PMID: 36407804 PMCID: PMC9640850 DOI: 10.1007/s10114-022-2326-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 07/12/2022] [Indexed: 05/25/2023]
Abstract
With the great advancement of experimental tools, a tremendous amount of biomolecular data has been generated and accumulated in various databases. The high dimensionality, structural complexity, the nonlinearity, and entanglements of biomolecular data, ranging from DNA knots, RNA secondary structures, protein folding configurations, chromosomes, DNA origami, molecular assembly, to others at the macromolecular level, pose a severe challenge in their analysis and characterization. In the past few decades, mathematical concepts, models, algorithms, and tools from algebraic topology, combinatorial topology, computational topology, and topological data analysis, have demonstrated great power and begun to play an essential role in tackling the biomolecular data challenge. In this work, we introduce biomolecular topology, which concerns the topological problems and models originated from the biomolecular systems. More specifically, the biomolecular topology encompasses topological structures, properties and relations that are emerged from biomolecular structures, dynamics, interactions, and functions. We discuss the various types of biomolecular topology from structures (of proteins, DNAs, and RNAs), protein folding, and protein assembly. A brief discussion of databanks (and databases), theoretical models, and computational algorithms, is presented. Further, we systematically review related topological models, including graphs, simplicial complexes, persistent homology, persistent Laplacians, de Rham-Hodge theory, Yau-Hausdorff distance, and the topology-based machine learning models.
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Affiliation(s)
- Jian Liu
- School of Mathematical Sciences, Hebei Normal University, Shijiazhuang, 050024 P. R. China
- Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing, 101408 P. R. China
| | - Ke-Lin Xia
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, 639798 Singapore
| | - Jie Wu
- Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing, 101408 P. R. China
- Department of Mathematical Sciences, Tsinghua University, Beijing, 100084 P. R. China
| | - Stephen Shing-Toung Yau
- Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing, 101408 P. R. China
- Department of Mathematical Sciences, Tsinghua University, Beijing, 100084 P. R. China
| | - Guo-Wei Wei
- Department of Mathematics & Department of Biochemistry and Molecular Biology & Department of Electrical and Computer Engineering, Michigan State University, Wells Hall 619 Red Cedar Road, East Lansing, MI 48824-1027 USA
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10
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Liu X, Feng H, Wu J, Xia K. Hom-Complex-Based Machine Learning (HCML) for the Prediction of Protein-Protein Binding Affinity Changes upon Mutation. J Chem Inf Model 2022; 62:3961-3969. [PMID: 36040839 DOI: 10.1021/acs.jcim.2c00580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Protein-protein interactions (PPIs) are involved in almost all biological processes in the cell. Understanding protein-protein interactions holds the key for the understanding of biological functions, diseases and the development of therapeutics. Recently, artificial intelligence (AI) models have demonstrated great power in PPIs. However, a key issue for all AI-based PPI models is efficient molecular representations and featurization. Here, we propose Hom-complex-based PPI representation, and Hom-complex-based machine learning models for the prediction of PPI binding affinity changes upon mutation, for the first time. In our model, various Hom complexes Hom(G1, G) can be generated for the graph representation G of protein-protein complex by using different graphs G1, which reveal G1-related inner connections within the graph representation G of protein-protein complex. Further, for a specific graph G1, a series of nested Hom complexes are generated to give a multiscale characterization of the PPIs. Its persistent homology and persistent Euler characteristic are used as molecular descriptors and further combined with the machine learning model, in particular, gradient boosting tree (GBT). We systematically test our model on the two most-commonly used data sets, that is, SKEMPI and AB-Bind. It has been found that our model outperforms all the existing models as far as we know, which demonstrates the great potential of our model for the analysis of PPIs. Our model can be used for the analysis and design of efficient antibodies for SARS-CoV-2.
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Affiliation(s)
- Xiang Liu
- Chern Institute of Mathematics and LPMC, Nankai University, Tianjin, China, 300071.,Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University, Singapore 637371
| | - Huitao Feng
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University, Singapore 637371.,Mathematical Science Research Center, Chongqing University of Technology, Chongqing, China, 400054
| | - Jie Wu
- Yanqi Lake Beijing Institute of Mathematical Sciences and Applications (BIMSA), Beijing, China,101408
| | - Kelin Xia
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University, Singapore 637371
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11
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Romero-Molina S, Ruiz-Blanco YB, Mieres-Perez J, Harms M, Münch J, Ehrmann M, Sanchez-Garcia E. PPI-Affinity: A Web Tool for the Prediction and Optimization of Protein-Peptide and Protein-Protein Binding Affinity. J Proteome Res 2022; 21:1829-1841. [PMID: 35654412 PMCID: PMC9361347 DOI: 10.1021/acs.jproteome.2c00020] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
Virtual screening
of protein–protein and protein–peptide
interactions is a challenging task that directly impacts the processes
of hit identification and hit-to-lead optimization in drug design
projects involving peptide-based pharmaceuticals. Although several
screening tools designed to predict the binding affinity of protein–protein
complexes have been proposed, methods specifically developed to predict
protein–peptide binding affinity are comparatively scarce.
Frequently, predictors trained to score the affinity of small molecules
are used for peptides indistinctively, despite the larger complexity
and heterogeneity of interactions rendered by peptide binders. To
address this issue, we introduce PPI-Affinity, a tool that leverages
support vector machine (SVM) predictors of binding affinity to screen
datasets of protein–protein and protein–peptide complexes,
as well as to generate and rank mutants of a given structure. The
performance of the SVM models was assessed on four benchmark datasets,
which include protein–protein and protein–peptide binding
affinity data. In addition, we evaluated our model on a set of mutants
of EPI-X4, an endogenous peptide inhibitor of the chemokine receptor
CXCR4, and on complexes of the serine proteases HTRA1 and HTRA3 with
peptides. PPI-Affinity is freely accessible at https://protdcal.zmb.uni-due.de/PPIAffinity.
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Affiliation(s)
- Sandra Romero-Molina
- Computational Biochemistry, Center of Medical Biotechnology, University of Duisburg-Essen, Essen 45141, Germany
| | - Yasser B Ruiz-Blanco
- Computational Biochemistry, Center of Medical Biotechnology, University of Duisburg-Essen, Essen 45141, Germany
| | - Joel Mieres-Perez
- Computational Biochemistry, Center of Medical Biotechnology, University of Duisburg-Essen, Essen 45141, Germany
| | - Mirja Harms
- Institute of Molecular Virology, Ulm University Medical Center, Ulm 89081, Germany
| | - Jan Münch
- Institute of Molecular Virology, Ulm University Medical Center, Ulm 89081, Germany.,Core Facility Functional Peptidomics, Ulm University Medical Center, Ulm 89081, Germany
| | - Michael Ehrmann
- Faculty of Biology, Center of Medical Biotechnology, University of Duisburg-Essen, Essen 45141, Germany
| | - Elsa Sanchez-Garcia
- Computational Biochemistry, Center of Medical Biotechnology, University of Duisburg-Essen, Essen 45141, Germany
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12
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Zhou P, Wen L, Lin J, Mei L, Liu Q, Shang S, Li J, Shu J. Integrated unsupervised-supervised modeling and prediction of protein-peptide affinities at structural level. Brief Bioinform 2022; 23:6555404. [PMID: 35352094 DOI: 10.1093/bib/bbac097] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 02/15/2022] [Accepted: 02/23/2022] [Indexed: 12/24/2022] Open
Abstract
Cell signal networks are orchestrated directly or indirectly by various peptide-mediated protein-protein interactions, which are normally weak and transient and thus ideal for biological regulation and medicinal intervention. Here, we develop a general-purpose method for modeling and predicting the binding affinities of protein-peptide interactions (PpIs) at the structural level. The method is a hybrid strategy that employs an unsupervised approach to derive a layered PpI atom-residue interaction (ulPpI[a-r]) potential between different protein atom types and peptide residue types from thousands of solved PpI complex structures and then statistically correlates the potential descriptors with experimental affinities (KD values) over hundreds of known PpI samples in a supervised manner to create an integrated unsupervised-supervised PpI affinity (usPpIA) predictor. Although both the ulPpI[a-r] potential and usPpIA predictor can be used to calculate PpI affinities from their complex structures, the latter seems to perform much better than the former, suggesting that the unsupervised potential can be improved substantially with a further correction by supervised statistical learning. We examine the robustness and fault-tolerance of usPpIA predictor when applied to treat the coarse-grained PpI complex structures modeled computationally by sophisticated peptide docking and dynamics simulation. It is revealed that, despite developed solely based on solved structures, the integrated unsupervised-supervised method is also applicable for locally docked structures to reach a quantitative prediction but can only give a qualitative prediction on globally docked structures. The dynamics refinement seems not to change (or improve) the predictive results essentially, although it is computationally expensive and time-consuming relative to peptide docking. We also perform extrapolation of usPpIA predictor to the indirect affinity quantities of HLA-A*0201 binding epitope peptides and NHERF PDZ binding scaffold peptides, consequently resulting in a good and moderate correlation of the predicted KD with experimental IC50 and BLU on the two peptide sets, with Pearson's correlation coefficients Rp = 0.635 and 0.406, respectively.
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Affiliation(s)
- Peng Zhou
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
| | - Li Wen
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
| | - Jing Lin
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
| | - Li Mei
- Institute of Culinary, Sichuan Tourism University, Chengdu 610100, China
| | - Qian Liu
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
| | - Shuyong Shang
- of Ecological Environment Protection, Chengdu Normal University, Chengdu 611130, China
| | - Juelin Li
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
| | - Jianping Shu
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
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13
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Wee J, Xia K. Persistent spectral based ensemble learning (PerSpect-EL) for protein-protein binding affinity prediction. Brief Bioinform 2022; 23:6533501. [PMID: 35189639 DOI: 10.1093/bib/bbac024] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/30/2021] [Accepted: 01/17/2022] [Indexed: 12/14/2022] Open
Abstract
Protein-protein interactions (PPIs) play a significant role in nearly all cellular and biological activities. Data-driven machine learning models have demonstrated great power in PPIs. However, the design of efficient molecular featurization poses a great challenge for all learning models for PPIs. Here, we propose persistent spectral (PerSpect) based PPI representation and featurization, and PerSpect-based ensemble learning (PerSpect-EL) models for PPI binding affinity prediction, for the first time. In our model, a sequence of Hodge (or combinatorial) Laplacian (HL) matrices at various different scales are generated from a specially designed filtration process. PerSpect attributes, which are statistical and combinatorial properties of spectrum information from these HL matrices, are used as features for PPI characterization. Each PerSpect attribute is input into a 1D convolutional neural network (CNN), and these CNN networks are stacked together in our PerSpect-based ensemble learning models. We systematically test our model on the two most commonly used datasets, i.e. SKEMPI and AB-Bind. It has been found that our model can achieve state-of-the-art results and outperform all existing models to the best of our knowledge.
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Affiliation(s)
- JunJie Wee
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371
| | - Kelin Xia
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371
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14
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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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15
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Verkhivker GM, Agajanian S, Oztas DY, Gupta G. Atomistic Simulations and In Silico Mutational Profiling of Protein Stability and Binding in the SARS-CoV-2 Spike Protein Complexes with Nanobodies: Molecular Determinants of Mutational Escape Mechanisms. ACS OMEGA 2021; 6:26354-26371. [PMID: 34660995 PMCID: PMC8515575 DOI: 10.1021/acsomega.1c03558] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 09/10/2021] [Indexed: 05/11/2023]
Abstract
Structure-functional studies have recently revealed a spectrum of diverse high-affinity nanobodies with efficient neutralizing capacity against SARS-CoV-2 virus and resilience against mutational escape. In this study, we combine atomistic simulations with the ensemble-based mutational profiling of binding for the SARS-CoV-2 S-RBD complexes with a wide range of nanobodies to identify dynamic and binding affinity fingerprints and characterize the energetic determinants of nanobody-escaping mutations. Using an in silico mutational profiling approach for probing the protein stability and binding, we examine dynamics and energetics of the SARS-CoV-2 complexes with single nanobodies Nb6 and Nb20, VHH E, a pair combination VHH E + U, a biparatopic nanobody VHH VE, and a combination of the CC12.3 antibody and VHH V/W nanobodies. This study characterizes the binding energy hotspots in the SARS-CoV-2 protein and complexes with nanobodies providing a quantitative analysis of the effects of circulating variants and escaping mutations on binding that is consistent with a broad range of biochemical experiments. The results suggest that mutational escape may be controlled through structurally adaptable binding hotspots in the receptor-accessible binding epitope that are dynamically coupled to the stability centers in the distant binding epitope targeted by VHH U/V/W nanobodies. This study offers a plausible mechanism in which through cooperative dynamic changes, nanobody combinations and biparatopic nanobodies can elicit the increased binding affinity response and yield resilience to common escape mutants.
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Affiliation(s)
- Gennady M. Verkhivker
- Keck
Center for Science and Engineering, Schmid College of Science and
Technology, Chapman University, One University Drive, Orange, California 92866, United States
- Department
of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, California 92618, United States
| | - Steve Agajanian
- Keck
Center for Science and Engineering, Schmid College of Science and
Technology, Chapman University, One University Drive, Orange, California 92866, United States
| | - Deniz Yasar Oztas
- Keck
Center for Science and Engineering, Schmid College of Science and
Technology, Chapman University, One University Drive, Orange, California 92866, United States
| | - Grace Gupta
- Keck
Center for Science and Engineering, Schmid College of Science and
Technology, Chapman University, One University Drive, Orange, California 92866, United States
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16
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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.
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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)
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17
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Sunny S, Jayaraj PB. FPDock: Protein-protein docking using flower pollination algorithm. Comput Biol Chem 2021; 93:107518. [PMID: 34048986 DOI: 10.1016/j.compbiolchem.2021.107518] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 05/11/2021] [Accepted: 05/16/2021] [Indexed: 11/25/2022]
Abstract
Proteins play their vital role in biological systems through interaction and complex formation with other biological molecules. Indeed, abnormalities in the interaction patterns affect the proteins' structure and have detrimental effects on living organisms. Research in structure prediction gains its gravity as the functions of proteins depend on their structures. Protein-protein docking is one of the computational methods devised to understand the interaction between proteins. Metaheuristic algorithms are promising to use owing to the hardness of the structure prediction problem. In this paper, a variant of the Flower Pollination Algorithm (FPA) is applied to get an accurate protein-protein complex structure. The algorithm begins execution from a randomly generated initial population, which gets flourished in different isolated islands, trying to find their local optimum. The abiotic and biotic pollination applied in different generations brings diversity and intensity to the solutions. Each round of pollination applies an energy-based scoring function whose value influences the choice to accept a new solution. Analysis of final predictions based on CAPRI quality criteria shows that the proposed method has a success rate of 58% in top10 ranks, which in comparison with other methods like SwarmDock, pyDock, ZDOCK is better. Source code of the work is available at: https://github.com/Sharon1989Sunny/_FPDock_.
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Affiliation(s)
- Sharon Sunny
- Department of Computer Science and Engineering, National Institute of Technology Calicut, India.
| | - P B Jayaraj
- Department of Computer Science and Engineering, National Institute of Technology Calicut, India
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18
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Wang B, Su Z, Wu Y. Computational Assessment of Protein-Protein Binding Affinity by Reverse Engineering the Energetics in Protein Complexes. GENOMICS PROTEOMICS & BIOINFORMATICS 2021; 19:1012-1022. [PMID: 33838354 PMCID: PMC9403033 DOI: 10.1016/j.gpb.2021.03.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 03/07/2019] [Accepted: 05/17/2019] [Indexed: 11/29/2022]
Abstract
The cellular functions of proteins are maintained by forming diverse complexes. The stability of these complexes is quantified by the measurement of binding affinity, and mutations that alter the binding affinity can cause various diseases such as cancer and diabetes. As a result, accurate estimation of the binding stability and the effects of mutations on changes of binding affinity is a crucial step to understanding the biological functions of proteins and their dysfunctional consequences. It has been hypothesized that the stability of a protein complex is dependent not only on the residues at its binding interface by pairwise interactions but also on all other remaining residues that do not appear at the binding interface. Here, we computationally reconstruct the binding affinity by decomposing it into the contributions of interfacial residues and other non-interfacial residues in a protein complex. We further assume that the contributions of both interfacial and non-interfacial residues to the binding affinity depend on their local structural environments such as solvent-accessible surfaces and secondary structural types. The weights of all corresponding parameters are optimized by Monte-Carlo simulations. After cross-validation against a large-scale dataset, we show that the model not only shows a strong correlation between the absolute values of the experimental and calculated binding affinities, but can also be an effective approach to predict the relative changes of binding affinity from mutations. Moreover, we have found that the optimized weights of many parameters can capture the first-principle chemical and physical features of molecular recognition, therefore reversely engineering the energetics of protein complexes. These results suggest that our method can serve as a useful addition to current computational approaches for predicting binding affinity and understanding the molecular mechanism of protein–protein interactions.
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Affiliation(s)
- Bo Wang
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| | - Zhaoqian Su
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.
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19
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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: 47] [Impact Index Per Article: 15.7] [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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20
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Gonzalez TR, Martin KP, Barnes JE, Patel JS, Ytreberg FM. Assessment of software methods for estimating protein-protein relative binding affinities. PLoS One 2020; 15:e0240573. [PMID: 33347442 PMCID: PMC7751979 DOI: 10.1371/journal.pone.0240573] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Accepted: 12/07/2020] [Indexed: 11/19/2022] Open
Abstract
A growing number of computational tools have been developed to accurately and rapidly predict the impact of amino acid mutations on protein-protein relative binding affinities. Such tools have many applications, for example, designing new drugs and studying evolutionary mechanisms. In the search for accuracy, many of these methods employ expensive yet rigorous molecular dynamics simulations. By contrast, non-rigorous methods use less exhaustive statistical mechanics, allowing for more efficient calculations. However, it is unclear if such methods retain enough accuracy to replace rigorous methods in binding affinity calculations. This trade-off between accuracy and computational expense makes it difficult to determine the best method for a particular system or study. Here, eight non-rigorous computational methods were assessed using eight antibody-antigen and eight non-antibody-antigen complexes for their ability to accurately predict relative binding affinities (ΔΔG) for 654 single mutations. In addition to assessing accuracy, we analyzed the CPU cost and performance for each method using a variety of physico-chemical structural features. This allowed us to posit scenarios in which each method may be best utilized. Most methods performed worse when applied to antibody-antigen complexes compared to non-antibody-antigen complexes. Rosetta-based JayZ and EasyE methods classified mutations as destabilizing (ΔΔG < -0.5 kcal/mol) with high (83-98%) accuracy and a relatively low computational cost for non-antibody-antigen complexes. Some of the most accurate results for antibody-antigen systems came from combining molecular dynamics with FoldX with a correlation coefficient (r) of 0.46, but this was also the most computationally expensive method. Overall, our results suggest these methods can be used to quickly and accurately predict stabilizing versus destabilizing mutations but are less accurate at predicting actual binding affinities. This study highlights the need for continued development of reliable, accessible, and reproducible methods for predicting binding affinities in antibody-antigen proteins and provides a recipe for using current methods.
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Affiliation(s)
- Tawny R. Gonzalez
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
| | - Kyle P. Martin
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
- Department of Physics, University of Idaho, Moscow, Idaho, United States of America
| | - Jonathan E. Barnes
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
- Department of Physics, University of Idaho, Moscow, Idaho, United States of America
| | - Jagdish Suresh Patel
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America
| | - F. Marty Ytreberg
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
- Department of Physics, University of Idaho, Moscow, Idaho, United States of America
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21
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Ranade SS, Ramalingam R. In silico study on pH-based alanine scanning of Phylloseptin-2 helps determine potential mutant sites for futuristic therapeutic analogues. MOLECULAR SIMULATION 2020. [DOI: 10.1080/08927022.2020.1804563] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Shruti Sunil Ranade
- Department of Biotechnology, School of Bioscience and Technology, Vellore Institute of Technology (Deemed to be University), Vellore, India
| | - Rajasekaran Ramalingam
- Department of Biotechnology, School of Bioscience and Technology, Vellore Institute of Technology (Deemed to be University), Vellore, India
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22
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Torchala M, Gerguri T, Chaleil RAG, Gordon P, Russell F, Keshani M, Bates PA. Enhanced sampling of protein conformational states for dynamic cross-docking within the protein-protein docking server SwarmDock. Proteins 2020; 88:962-972. [PMID: 31697436 PMCID: PMC7496321 DOI: 10.1002/prot.25851] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 10/02/2019] [Accepted: 11/03/2019] [Indexed: 12/12/2022]
Abstract
The formation of specific protein-protein interactions is often a key to a protein's function. During complex formation, each protein component will undergo a change in the conformational state, for some these changes are relatively small and reside primarily at the sidechain level; however, others may display notable backbone adjustments. One of the classic problems in the protein-docking field is to be able to a priori predict the extent of such conformational changes. In this work, we investigated three protocols to find the most suitable input structure conformations for cross-docking, including a robust sampling approach in normal mode space. Counterintuitively, knowledge of the theoretically best combination of normal modes for unbound-bound transitions does not always lead to the best results. We used a novel spatial partitioning library, Aether Engine (see Supplementary Materials), to efficiently search the conformational states of 56 receptor/ligand pairs, including a recent CAPRI target, in a systematic manner and selected diverse conformations as input to our automated docking server, SwarmDock, a server that allows moderate conformational adjustments during the docking process. In essence, here we present a dynamic cross-docking protocol, which when benchmarked against the simpler approach of just docking the unbound components shows a 10% uplift in the quality of the top docking pose.
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Affiliation(s)
- Mieczyslaw Torchala
- Biomolecular Modelling LaboratoryThe Francis Crick InstituteLondonUK
- Hadean Supercomputing LtdLondonUK
| | - Tereza Gerguri
- Biomolecular Modelling LaboratoryThe Francis Crick InstituteLondonUK
| | | | | | | | | | - Paul A. Bates
- Biomolecular Modelling LaboratoryThe Francis Crick InstituteLondonUK
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23
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Ranade SS, Ramalingam R. Hydrogen bonds in anoplin peptides aid in identification of a structurally stable therapeutic drug scaffold. J Mol Model 2020; 26:155. [PMID: 32451705 DOI: 10.1007/s00894-020-04380-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 04/07/2020] [Indexed: 12/30/2022]
Abstract
Multi-drug resistance is a major issue faced by the global pharmaceutical industry. Short antimicrobial peptides such as anoplins can be used to replace antibiotics, thus mitigating this issue. Antimicrobial activity, non-toxicity, and structural stability are essential features of a therapeutic drug. Antimicrobial activity and toxicity to human erythrocytes have been previously reported for anoplin and anoplin R5K T8W. This study attempts to identify a therapeutic peptide drug scaffold between these peptides by examining their structural stability, mainly based on the hydrogen bonds (H-bond) found in their structures. The static structure of anoplin R5K T8W displayed lower H-bond distances than anoplin, thereby exhibiting enhanced structural stability. Dynamic stability studies revealed that conformers of anoplin R5K T8W exhibited lower hydrogen bond distances (HBDs), higher H-bond occupancies, and higher radial distribution function (RDF) of H-bonds in comparison with conformers of anoplin. Furthermore, conformers of anoplin R5K T8W generated using 50-ns molecular dynamics simulation displayed lower conformational free energy than anoplin, thus establishing its higher structural stability. Overall, anoplin R5K T8W can be claimed as a promising scaffold that may be used for therapeutic purposes. In conclusion, H-bonds play a major role in structural stability and may aid in identification of a therapeutic peptide scaffold. Graphical abstract.
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Affiliation(s)
- Shruti Sunil Ranade
- Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology (Deemed to be University), Vellore, Tamil Nadu, 632014, India
| | - Rajasekaran Ramalingam
- Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology (Deemed to be University), Vellore, Tamil Nadu, 632014, India.
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24
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Zhou G, Chen M, Ju CJT, Wang Z, Jiang JY, Wang W. Mutation effect estimation on protein-protein interactions using deep contextualized representation learning. NAR Genom Bioinform 2020; 2:lqaa015. [PMID: 32166223 PMCID: PMC7059401 DOI: 10.1093/nargab/lqaa015] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 01/20/2020] [Accepted: 02/24/2020] [Indexed: 12/14/2022] Open
Abstract
The functional impact of protein mutations is reflected on the alteration of conformation and thermodynamics of protein–protein interactions (PPIs). Quantifying the changes of two interacting proteins upon mutations is commonly carried out by computational approaches. Hence, extensive research efforts have been put to the extraction of energetic or structural features on proteins, followed by statistical learning methods to estimate the effects of mutations on PPI properties. Nonetheless, such features require extensive human labors and expert knowledge to obtain, and have limited abilities to reflect point mutations. We present an end-to-end deep learning framework, MuPIPR (Mutation Effects in Protein–protein Interaction PRediction Using Contextualized Representations), to estimate the effects of mutations on PPIs. MuPIPR incorporates a contextualized representation mechanism of amino acids to propagate the effects of a point mutation to surrounding amino acid representations, therefore amplifying the subtle change in a long protein sequence. On top of that, MuPIPR leverages a Siamese residual recurrent convolutional neural encoder to encode a wild-type protein pair and its mutation pair. Multi-layer perceptron regressors are applied to the protein pair representations to predict the quantifiable changes of PPI properties upon mutations. Experimental evaluations show that, with only sequence information, MuPIPR outperforms various state-of-the-art systems on estimating the changes of binding affinity for SKEMPI v1, and offers comparable performance on SKEMPI v2. Meanwhile, MuPIPR also demonstrates state-of-the-art performance on estimating the changes of buried surface areas. The software implementation is available at https://github.com/guangyu-zhou/MuPIPR.
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Affiliation(s)
- Guangyu Zhou
- Department of Computer Science, University of California, Los Angeles, CA 90095, USA
| | - Muhao Chen
- Department of Computer Science, University of California, Los Angeles, CA 90095, USA.,Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Chelsea J T Ju
- Department of Computer Science, University of California, Los Angeles, CA 90095, USA
| | - Zheng Wang
- Department of Computer Science, University of California, Los Angeles, CA 90095, USA
| | - Jyun-Yu Jiang
- Department of Computer Science, University of California, Los Angeles, CA 90095, USA
| | - Wei Wang
- Department of Computer Science, University of California, Los Angeles, CA 90095, USA
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25
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A topology-based network tree for the prediction of protein-protein binding affinity changes following mutation. NAT MACH INTELL 2020; 2:116-123. [PMID: 34170981 PMCID: PMC7223817 DOI: 10.1038/s42256-020-0149-6] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 01/10/2020] [Indexed: 12/14/2022]
Abstract
The ability to predict protein-protein interactions is crucial to our understanding of a wide range of biological activities and functions in the human body, and for guiding drug discovery. Despite considerable efforts to develop suitable computational methods, predicting protein-protein interaction binding affinity changes following mutation (ΔΔG) remains a severe challenge. Algebraic topology, a champion in recent worldwide competitions for protein-ligand binding affinity predictions, is a promising approach to simplifying the complexity of biological structures. Here we introduce element- and site-specific persistent homology (a new branch of algebraic topology) to simplify the structural complexity of protein-protein complexes and embed crucial biological information into topological invariants. We also propose a new deep learning algorithm called NetTree to take advantage of convolutional neural networks and gradient-boosting trees. A topology-based network tree is constructed by integrating the topological representation and NetTree for predicting protein-protein interaction ΔΔG. Tests on major benchmark datasets indicate that the proposed topology-based network tree is an important improvement over the current state of the art in predicting ΔΔG.
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26
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Abstract
Many of the biological functions of the cell are driven by protein-protein interactions. However, determining which proteins interact and exactly how they do so to enable their functions, remain major research questions. Functional interactions are dependent on a number of complicated factors; therefore, modeling the three-dimensional structure of protein-protein complexes is still considered a complex endeavor. Nevertheless, the rewards for modeling protein interactions to atomic level detail are substantial, and there are numerous examples of how models can provide useful information for drug design, protein engineering, systems biology, and understanding of the immune system. Here, we provide practical guidelines for docking proteins using the web-server, SwarmDock, a flexible protein-protein docking method. Moreover, we provide an overview of the factors that need to be considered when deciding whether docking is likely to be successful.
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Affiliation(s)
- Iain H Moal
- European Bioinformatics Institute, Hinxton, UK
| | | | | | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK.
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27
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Rosell M, Rodríguez‐Lumbreras LA, Romero‐Durana M, Jiménez‐García B, Díaz L, Fernández‐Recio J. Integrative modeling of protein‐protein interactions with pyDock for the new docking challenges. Proteins 2019; 88:999-1008. [DOI: 10.1002/prot.25858] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 10/30/2019] [Accepted: 11/15/2019] [Indexed: 01/12/2023]
Affiliation(s)
- Mireia Rosell
- Barcelona Supercomputing Center (BSC) Barcelona Spain
- Instituto de Ciencias de la Vid y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja) Logroño Spain
| | - Luis A. Rodríguez‐Lumbreras
- Barcelona Supercomputing Center (BSC) Barcelona Spain
- Instituto de Ciencias de la Vid y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja) Logroño Spain
| | - Miguel Romero‐Durana
- Barcelona Supercomputing Center (BSC) Barcelona Spain
- Instituto de Ciencias de la Vid y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja) Logroño Spain
- Structural Biology Unit, Instituto de Biología Molecular de Barcelona (IBMB‐CSIC) Barcelona Spain
| | | | - Lucía Díaz
- Barcelona Supercomputing Center (BSC) Barcelona Spain
| | - Juan Fernández‐Recio
- Barcelona Supercomputing Center (BSC) Barcelona Spain
- Instituto de Ciencias de la Vid y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja) Logroño Spain
- Structural Biology Unit, Instituto de Biología Molecular de Barcelona (IBMB‐CSIC) Barcelona Spain
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28
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Su Z, Wu Y. Multiscale simulation unravel the kinetic mechanisms of inflammasome assembly. BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR CELL RESEARCH 2019; 1867:118612. [PMID: 31758956 DOI: 10.1016/j.bbamcr.2019.118612] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 11/11/2019] [Accepted: 11/18/2019] [Indexed: 01/16/2023]
Abstract
In the innate immune system, the host defense from the invasion of external pathogens triggers the inflammatory responses. Proteins involved in the inflammatory pathways were often found to aggregate into supramolecular oligomers, called 'inflammasome', mostly through the homotypic interaction between their domains that belong to the death domain superfamily. Although much has been known about the formation of these helical molecular machineries, the detailed correlation between the dynamics of their assembly and the structure of each domain is still not well understood. Using the filament formed by the PYD domains of adaptor molecule ASC as a test system, we constructed a new multiscale simulation framework to study the kinetics of inflammasome assembly. We found that the filament assembly is a multi-step, but highly cooperative process. Moreover, there are three types of binding interfaces between domain subunits in the ASCPYD filament. The multiscale simulation results suggest that dynamics of domain assembly are rooted in the primary protein sequence which defines the energetics of molecular recognition through three binding interfaces. Interface I plays a more regulatory role than the other two in mediating both the kinetics and the thermodynamics of assembly. Finally, the efficiency of our computational framework allows us to design mutants on a systematic scale and predict their impacts on filament assembly. In summary, this is, to the best of our knowledge, the first simulation method to model the spatial-temporal process of inflammasome assembly. Our work is a useful addition to a suite of existing experimental techniques to study the functions of inflammasome in innate immune system.
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Affiliation(s)
- Zhaoqian Su
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, United States of America
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, United States of America.
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29
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Siebenmorgen T, Zacharias M. Computational prediction of protein–protein binding affinities. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2019. [DOI: 10.1002/wcms.1448] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Till Siebenmorgen
- Physics Department T38 Technical University of Munich Garching Germany
| | - Martin Zacharias
- Physics Department T38 Technical University of Munich Garching Germany
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30
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Shruti SR, Rajasekaran R. Identification of therapeutic peptide scaffold from tritrpticin family for urinary tract infections using in silico techniques. J Biomol Struct Dyn 2019; 38:4407-4417. [DOI: 10.1080/07391102.2019.1680437] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- S. R. Shruti
- Department of Biotechnology, School of Biosciences and Technology, VIT (Deemed to Be University), Vellore, India
| | - R. Rajasekaran
- Department of Biotechnology, School of Biosciences and Technology, VIT (Deemed to Be University), Vellore, India
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31
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Célerse F, Lagardère L, Derat E, Piquemal JP. Massively Parallel Implementation of Steered Molecular Dynamics in Tinker-HP: Comparisons of Polarizable and Non-Polarizable Simulations of Realistic Systems. J Chem Theory Comput 2019; 15:3694-3709. [DOI: 10.1021/acs.jctc.9b00199] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Frédéric Célerse
- Laboratoire de Chimie Théorique, UMR 7616 CNRS, Sorbonne Université, 75005 Paris, France
- Institut Parisien de Chimie Moléculaire, UMR 8232 CNRS, Sorbonne Université, 75005 Paris, France
| | - Louis Lagardère
- Institut des Sciences du Calcul et des Données, Sorbonne Université, 75005 Paris, France
- Institut Parisien de Chimie Physique et Théorique, FR 2622 CNRS, Sorbonne Université, 75005 Paris, France
- Laboratoire de Chimie théorique, UMR 7616 CNRS, Sorbonne Université, 75005 Paris, France
| | - Etienne Derat
- Institut Parisien de Chimie Moléculaire, UMR 8232 CNRS, Sorbonne Université, 75005 Paris, France
| | - Jean-Philip Piquemal
- Laboratoire de Chimie Théorique, UMR 7616 CNRS, Sorbonne Université, 75005 Paris, France
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Institut Universitaire de France, 75005 Paris, France
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32
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Conti S, Karplus M. Estimation of the breadth of CD4bs targeting HIV antibodies by molecular modeling and machine learning. PLoS Comput Biol 2019; 15:e1006954. [PMID: 30970017 PMCID: PMC6457539 DOI: 10.1371/journal.pcbi.1006954] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Accepted: 03/18/2019] [Indexed: 11/21/2022] Open
Abstract
HIV is a highly mutable virus for which all attempts to develop a vaccine have been unsuccessful. Nevertheless, few long-infected patients develop antibodies, called broadly neutralizing antibodies (bnAbs), that have a high breadth and can neutralize multiple variants of the virus. This suggests that a universal HIV vaccine should be possible. A measure of the efficacy of a HIV vaccine is the neutralization breadth of the antibodies it generates. The breadth is defined as the fraction of viruses in the Seaman panel that are neutralized by the antibody. Experimentally the neutralization ability is measured as the half maximal inhibitory concentration of the antibody (IC50). To avoid such time-consuming experimental measurements, we developed a computational approach to estimate the IC50 and use it to determine the antibody breadth. Given that no direct method exists for calculating IC50 values, we resort to a combination of atomistic modeling and machine learning. For each antibody/virus complex, an all-atoms model is built using the amino acid sequence and a known structure of a related complex. Then a series of descriptors are derived from the atomistic models, and these are used to train a Multi-Layer Perceptron (an Artificial Neural Network) to predict the value of the IC50 (by regression), or if the antibody binds or not to the virus (by classification). The neural networks are trained by use of experimental IC50 values collected in the CATNAP database. The computed breadths obtained by regression and classification are reported and the importance of having some related information in the data set for obtaining accurate predictions is analyzed. This approach is expected to prove useful for the design of HIV bnAbs, where the computation of the potency must be accompanied by a computation of the breadth, and for evaluating the efficiency of potential vaccination schemes developed through modeling and simulation.
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Affiliation(s)
- Simone Conti
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Martin Karplus
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America
- Laboratoire de Chimie Biophysique, ISIS, Université de Strasbourg, Strasbourg, France
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33
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Litfin T, Yang Y, Zhou Y. SPOT-Peptide: Template-Based Prediction of Peptide-Binding Proteins and Peptide-Binding Sites. J Chem Inf Model 2019; 59:924-930. [DOI: 10.1021/acs.jcim.8b00777] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Thomas Litfin
- School of Information and Communication Technology, Griffith University, Southport, QLD 4222, Australia
| | - Yuedong Yang
- School of Data and Computer Science, Sun-Yat Sen University, Guangzhou, Guangdong 510006, China
| | - Yaoqi Zhou
- School of Information and Communication Technology, Griffith University, Southport, QLD 4222, Australia
- Institute for Glycomics, Griffith University, Southport, QLD 4222, Australia
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34
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Lu B, Li C, Chen Q, Song J. ProBAPred: Inferring protein–protein binding affinity by incorporating protein sequence and structural features. J Bioinform Comput Biol 2018; 16:1850011. [PMID: 29954286 DOI: 10.1142/s0219720018500117] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Protein-protein binding interaction is the most prevalent biological activity that mediates a great variety of biological processes. The increasing availability of experimental data of protein–protein interaction allows a systematic construction of protein–protein interaction networks, significantly contributing to a better understanding of protein functions and their roles in cellular pathways and human diseases. Compared to well-established classification for protein–protein interactions (PPIs), limited work has been conducted for estimating protein–protein binding free energy, which can provide informative real-value regression models for characterizing the protein–protein binding affinity. In this study, we propose a novel ensemble computational framework, termed ProBAPred (Protein–protein Binding Affinity Predictor), for quantitative estimation of protein–protein binding affinity. A large number of sequence and structural features, including physical–chemical properties, binding energy and conformation annotations, were collected and calculated from currently available protein binding complex datasets and the literature. Feature selection based on the WEKA package was performed to identify and characterize the most informative and contributing feature subsets. Experiments on the independent test showed that our ensemble method achieved the lowest Mean Absolute Error (MAE; 1.657[Formula: see text]kcal/mol) and the second highest correlation coefficient ([Formula: see text]), compared with the existing methods. The datasets and source codes of ProBAPred, and the supplementary materials in this study can be downloaded at http://lightning.med.monash.edu/probapred/ for academic use. We anticipate that the developed ProBAPred regression models can facilitate computational characterization and experimental studies of protein–protein binding affinity.
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Affiliation(s)
- Bangli Lu
- School of Computer, Electronic and Information, and State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, 100 Daxue Road, 530004 Nanning, P. R. China
| | - Chen Li
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, VIC 3800, Australia
| | - Qingfeng Chen
- School of Computer, Electronic and Information, and State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, 100 Daxue Road, 530004 Nanning, P. R. China
| | - Jiangning Song
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, VIC 3800, Australia
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, VIC 3800, Australia
- ARC Centre of Excellence for Advanced Molecular Imaging, Monash University, VIC 3800, Australia
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35
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Ganesan P, Ramalingam R. Investigation of structural stability and functionality of homodimeric gramicidin towards peptide‐based drug: a molecular simulation approach. J Cell Biochem 2018; 120:4903-4911. [DOI: 10.1002/jcb.27765] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Accepted: 09/06/2018] [Indexed: 12/17/2022]
Affiliation(s)
- Pavithrra Ganesan
- Bioinformatics Lab, Department of Biotechnology, School of Biosciences and Technology Vellore Institute of Technology (VIT) Vellore India
| | - Rajasekaran Ramalingam
- Bioinformatics Lab, Department of Biotechnology, School of Biosciences and Technology Vellore Institute of Technology (VIT) Vellore India
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36
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Identification of Effective Dimeric Gramicidin-D Peptide as Antimicrobial Therapeutics over Drug Resistance: In-Silico Approach. Interdiscip Sci 2018; 11:575-583. [PMID: 30182355 DOI: 10.1007/s12539-018-0304-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 07/25/2018] [Accepted: 08/28/2018] [Indexed: 10/28/2022]
Abstract
Discovering and developing the antimicrobial peptides are recently focused on pharmaceutical firm, since they serve as complementary to antibiotics in prevailing over drug resistance by eliciting the disruption of microbial membrane. Still, there are lots of challenges to bring up the structurally stable and functionally efficient antimicrobial peptides. It is well known that gramicidin D is the prominent antimicrobial peptide that exists as g-AB, g-BC, and g-AC. This study analyzes the structural stability and the functional activity of hetero-dimeric double-stranded gramicidin-D peptides, thereby demonstrating its potent antimicrobial activity against antibiotic-resistant micro-organisms. To investigate the structural stability and functionality of gramicidin D, we performed static and dynamic analysis. Initially, we observed a maximum number of intermolecular interactions and membrane penetration in g-AB as compared to g-BC and g-AC. To substantiate further, the geometrical and thermodynamic parameters revealed the retention of maximum stability in g-AB than g-AC and g-BC. Thus, the conformational free energy and the binding free energy showed the variation among gramicidin-D peptides for the prediction of increased stability and functionality. In conclusion, g-AB peptide has definitely demonstrated adequate structural stability and functionality and this work will need to be considered in peptide-based drug discovery.
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37
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Pfeiffenberger E, Bates PA. Predicting improved protein conformations with a temporal deep recurrent neural network. PLoS One 2018; 13:e0202652. [PMID: 30180164 PMCID: PMC6122789 DOI: 10.1371/journal.pone.0202652] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Accepted: 08/07/2018] [Indexed: 02/03/2023] Open
Abstract
Accurate protein structure prediction from amino acid sequence is still an unsolved problem. The most reliable methods centre on template based modelling. However, the accuracy of these models entirely depends on the availability of experimentally resolved homologous template structures. In order to generate more accurate models, extensive physics based molecular dynamics (MD) refinement simulations are performed to sample many different conformations to find improved conformational states. In this study, we propose a deep recurrent network model, called DeepTrajectory, that is able to identify these improved conformational states, with high precision, from a variety of different MD based sampling protocols. The proposed model learns the temporal patterns of features computed from MD trajectory data in order to classify whether each recorded simulation snapshot is an improved quality conformational state, decreased quality conformational state or whether there is no perceivable change in state with respect to the starting conformation. The model was trained and tested on 904 trajectories from 42 different protein systems with a cumulative number of more than 1.7 million snapshots. We show that our model outperforms other state of the art machine-learning algorithms that do not consider temporal dependencies. To our knowledge, DeepTrajectory is the first implementation of a time-dependent deep-learning protocol that is re-trainable and able to adapt to any new MD based sampling procedure, thereby demonstrating how a neural network can be used to learn the latter part of the protein folding funnel.
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Affiliation(s)
- Erik Pfeiffenberger
- Biomolecular Modelling Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, United Kingdom
| | - Paul A. Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, United Kingdom
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38
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Wang B, Xie ZR, Chen J, Wu Y. Integrating Structural Information to Study the Dynamics of Protein-Protein Interactions in Cells. Structure 2018; 26:1414-1424.e3. [PMID: 30174150 DOI: 10.1016/j.str.2018.07.010] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 06/12/2018] [Accepted: 07/24/2018] [Indexed: 02/07/2023]
Abstract
The information of how two proteins interact is embedded in the atomic details of their binding interfaces. These interactions, spatial-temporally coordinating each other as a network in a variable cytoplasmic environment, dominate almost all biological functions. A feasible and reliable computational model is highly demanded to realistically simulate these cellular processes and unravel the complexities beneath them. We therefore present a multiscale framework that integrates simulations on two different scales. The higher-resolution model incorporates structural information of proteins and energetics of their binding, while the lower-resolution model uses a highly simplified representation of proteins to capture the long-time-scale dynamics of a system with multiple proteins. Through a systematic benchmark test and two practical applications of biomolecular systems with specific cellular functions, we demonstrated that this method could be a powerful approach to understand molecular mechanisms of dynamic interactions between biomolecules and their functional impacts with high computational efficiency.
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Affiliation(s)
- Bo Wang
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, New York, NY 10461, USA
| | - Zhong-Ru Xie
- College of Engineering, University of Georgia, Athens, GA 30602, USA
| | - Jiawen Chen
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, New York, NY 10461, USA
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, New York, NY 10461, USA.
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39
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Raucci R, Laine E, Carbone A. Local Interaction Signal Analysis Predicts Protein-Protein Binding Affinity. Structure 2018; 26:905-915.e4. [PMID: 29779789 DOI: 10.1016/j.str.2018.04.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 02/06/2018] [Accepted: 04/10/2018] [Indexed: 12/27/2022]
Abstract
Several models estimating the strength of the interaction between proteins in a complex have been proposed. By exploring the geometry of contact distribution at protein-protein interfaces, we provide an improved model of binding energy. Local interaction signal analysis (LISA) is a radial function based on terms describing favorable and non-favorable contacts obtained by density functional theory, the support-core-rim interface residue distribution, non-interacting charged residues and secondary structures contribution. The three-dimensional organization of the contacts and their contribution on localized hot-sites over the entire interaction surface were numerically evaluated. LISA achieves a correlation of 0.81 (and a root-mean-square error of 2.35 ± 0.38 kcal/mol) when tested on 125 complexes for which experimental measurements were realized. LISA's performance is stable for subsets defined by functional composition and extent of conformational changes upon complex formation. A large-scale comparison with 17 other functions demonstrated the power of the geometrical model in the understanding of complex binding.
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Affiliation(s)
- Raffaele Raucci
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 4 Place Jussieu, 75005 Paris, France; Sorbonne Université, Institut des Sciences du Calcul et des Données (ISCD), 75005 Paris, France
| | - Elodie Laine
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 4 Place Jussieu, 75005 Paris, France
| | - Alessandra Carbone
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 4 Place Jussieu, 75005 Paris, France; Institut Universitaire de France, 75005 Paris, France.
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40
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Ahmad I, Jagtap DD, Selvaa Kumar C, Balasinor NH, Babitha Rani AM, Agarwal D, Saharan N. Molecular characterization of inhibin-A: Structure and expression analysis in Clarias batrachus. Gen Comp Endocrinol 2018; 261:104-114. [PMID: 29438674 DOI: 10.1016/j.ygcen.2018.02.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2017] [Revised: 11/30/2017] [Accepted: 02/09/2018] [Indexed: 12/27/2022]
Abstract
The inhibins are disulphide-linked heterodimeric glycoproteins that belong to the TGFβ superfamily. Inhibins have been well studied in mammals but the information about their structure and function is very limited in lower vertebrates. The aim of the present study was to characterize inhibin-A and to understand its receptor binding interaction, and to evaluate its biological function in Clarias batrachus. Structure prediction of inhibin-A revealed two glycosylation sites on inhibin-α (Asp262 and Asn334). Docking of inhibin-A with its receptor; betaglycan and Act RIIA showed that residues Ser321, Gly324 and Leu325 of inhibin-α are involved in high affinity binding with betaglycan while inhibin-βA bound to Act RIIA by forming hydrogen bonds. The mRNA transcript analysis of various tissues indicated the presence of higher to moderate expression of inhibin-α and inhibin-βA in the gonads and the extra-gonadal tissues. Further, stage specific expression showed decreased levels of inhibin-α in the gonads during the annual reproductive cycles. Inhibin-βA, activin-βB and Act RIIA increased in the brain during spawning while FSHr increased in the gonads during the preparatory phase. Our study provides molecular, structural and functional insights of inhibin-A for the first time in C. batrachus.
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Affiliation(s)
- Irshad Ahmad
- ICAR-Central Institute of Fisheries Education, Panch Marg, Yari Road, Versova, Andheri West, Mumbai 400061, India
| | - Dhanashree D Jagtap
- National Institute for Research in Reproductive Health (Indian Council of Medical Research), Jehangir Merwanji Street, Parel, Mumbai 400012, India
| | - C Selvaa Kumar
- Bioinformatics Department, School of Biotechnology and Bioinformatics, D.Y. Patil University, CBD Belapur, Navi Mumbai 400614, India
| | - Nafisa H Balasinor
- National Institute for Research in Reproductive Health (Indian Council of Medical Research), Jehangir Merwanji Street, Parel, Mumbai 400012, India
| | - A M Babitha Rani
- ICAR-Central Institute of Fisheries Education, Panch Marg, Yari Road, Versova, Andheri West, Mumbai 400061, India
| | - Deepak Agarwal
- ICAR-Central Institute of Fisheries Education, Panch Marg, Yari Road, Versova, Andheri West, Mumbai 400061, India
| | - Neelam Saharan
- ICAR-Central Institute of Fisheries Education, Panch Marg, Yari Road, Versova, Andheri West, Mumbai 400061, India.
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41
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Chu H, Liu H. TetraBASE: A Side Chain-Independent Statistical Energy for Designing Realistically Packed Protein Backbones. J Chem Inf Model 2018; 58:430-442. [PMID: 29314837 DOI: 10.1021/acs.jcim.7b00677] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
To construct backbone structures of high designability is a primary aspect of computational protein design. We report here a side chain-independent statistical energy that aims at realistic modeling of through-space packing of polypeptide backbones. To mitigate the lack of explicit amino acid side chains, the model treats the interbackbone site packing as being dependent on peptide local conformation. In addition, new variables suitable for statistical analysis, one for relative orientation and another for distance, have been introduced to represent the intersite geometry based on the asymmetrical tetrahedron organization of distinct chemical groups surrounding the Cα-carbon atoms. The resulting tetrahedron-based backbone statistical energy (tetraBASE) model has been used to optimize the tertiary organizations of secondary structure elements (SSEs) of designated types with Monte Caro simulated annealing, starting from artificial initial configurations. The tetraBASE minimum energy structures can reproduce SSE packing frequently observed in native proteins with atomic root-mean-square deviations of 1-2 Å. The model has also been tested by examining the stability of native SSE arrangements under tetraBASE. The results suggest that tetraBASE model can be used to effectively represent interbackbone packing when designing backbone structures without explicitly knowing side chain types.
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Affiliation(s)
- Huanyu Chu
- School of Life Sciences, University of Science and Technology of China , 230027 Hefei, Anhui China.,Hefei National Laboratory for Physical Sciences at the Microscales , 230027 Hefei, Anhui China
| | - Haiyan Liu
- School of Life Sciences, University of Science and Technology of China , 230027 Hefei, Anhui China.,Hefei National Laboratory for Physical Sciences at the Microscales , 230027 Hefei, Anhui China.,Collaborative Innovation Center of Chemistry for Life Sciences , 230027 Hefei, Anhui China
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42
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Abstract
The atomic structures of protein complexes can provide useful information for drug design, protein engineering, systems biology, and understanding pathology. Obtaining this information experimentally can be challenging. However, if the structures of the subunits are known, then it is often possible to model the complex computationally. This chapter provide practical guidelines for docking proteins using the SwarmDock flexible protein-protein docking method, providing an overview of the factors that need to be considered when deciding whether docking is likely to be successful, the preparation of structural input, generation of docked poses, analysis and ranking of docked poses, and the validation of models using external data.
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Affiliation(s)
- Iain H Moal
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.
| | | | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
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43
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Kumar N, Shariq M, Kumar A, Kumari R, Subbarao N, Tyagi RK, Mukhopadhyay G. Analyzing the role of CagV, a VirB8 homolog of the type IV secretion system of Helicobacter pylori. FEBS Open Bio 2017; 7:915-933. [PMID: 28680806 PMCID: PMC5494299 DOI: 10.1002/2211-5463.12225] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 02/22/2017] [Accepted: 02/24/2017] [Indexed: 12/13/2022] Open
Abstract
The type IV secretion system of Helicobacter pylori (Cag‐T4SS) is composed of ~ 27 components including a VirB8 homolog, CagV. We have characterized CagV and reported that it is an inner membrane protein and, like VirB8, forms a homodimer. Its stability is not dependent on the other Cag components and the absence of cagV affects the stability of only CagI, a protein involved in pilus formation. CagV is not required for the stability and localization of outer membrane subcomplex proteins, but interacts with them through CagX. It also interacts with the inner membrane‐associated components, CagF and CagZ, and is required for the surface localization of CagA. The results of this study might help in deciphering the mechanistic contributions of CagV in the Cag‐T4SS biogenesis and function.
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Affiliation(s)
- Navin Kumar
- Special Centre for Molecular Medicine Jawaharlal Nehru University New Delhi India.,Present address: School of Biotechnology Gautam Buddha University Yamuna Expressway Greater Noida Gautam Budh Nagar Uttar Pradesh India
| | - Mohd Shariq
- Special Centre for Molecular Medicine Jawaharlal Nehru University New Delhi India.,Present address: School of Life Sciences Jawaharlal Nehru University New Delhi India
| | - Amarjeet Kumar
- School of Computational and Integrative Sciences Jawaharlal Nehru University New Delhi India
| | - Rajesh Kumari
- Special Centre for Molecular Medicine Jawaharlal Nehru University New Delhi India
| | - Naidu Subbarao
- School of Computational and Integrative Sciences Jawaharlal Nehru University New Delhi India
| | - Rakesh K Tyagi
- Special Centre for Molecular Medicine Jawaharlal Nehru University New Delhi India
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44
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Barradas-Bautista D, Moal IH, Fernández-Recio J. A systematic analysis of scoring functions in rigid-body protein docking: The delicate balance between the predictive rate improvement and the risk of overtraining. Proteins 2017; 85:1287-1297. [DOI: 10.1002/prot.25289] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 03/08/2017] [Accepted: 03/20/2017] [Indexed: 12/24/2022]
Affiliation(s)
- Didier Barradas-Bautista
- Life Sciences Department, Barcelona Supercomputing Center (BSC), Joint BSC-CRG-IRB Research Program in Computational Biology; Barcelona 08034 Spain
| | - Iain H. Moal
- Life Sciences Department, Barcelona Supercomputing Center (BSC), Joint BSC-CRG-IRB Research Program in Computational Biology; Barcelona 08034 Spain
- European Molecular Biology Laboratory; European Bioinformatics Institute, Wellcome Trust Genome Campus; Hinxton Cambridge CB10 1SD United Kingdom
| | - Juan Fernández-Recio
- Life Sciences Department, Barcelona Supercomputing Center (BSC), Joint BSC-CRG-IRB Research Program in Computational Biology; Barcelona 08034 Spain
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45
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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.
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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
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46
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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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47
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Xiong P, Zhang C, Zheng W, Zhang Y. BindProfX: Assessing Mutation-Induced Binding Affinity Change by Protein Interface Profiles with Pseudo-Counts. J Mol Biol 2016; 429:426-434. [PMID: 27899282 DOI: 10.1016/j.jmb.2016.11.022] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Revised: 11/22/2016] [Accepted: 11/23/2016] [Indexed: 11/27/2022]
Abstract
Understanding how gene-level mutations affect the binding affinity of protein-protein interactions is a key issue of protein engineering. Due to the complexity of the problem, using physical force field to predict the mutation-induced binding free-energy change remains challenging. In this work, we present a renewed approach to calculate the impact of gene mutations on the binding affinity through the structure-based profiling of protein-protein interfaces, where the binding free-energy change (ΔΔG) is counted as the logarithm of relative probability of mutant amino acids over wild-type ones in the interface alignment matrix; three pseudo-counts are introduced to alleviate the limit of the current interface library. Compared with a previous profile score that was based on the log-odds likelihood calculation, the correlation between predicted and experimental ΔΔG of single-site mutations is increased in this approach from 0.33 to 0.68. The structure-based profile score is found complementary to the physical potentials, where a linear combination of the profile score with the FoldX potential could increase the ΔΔG correlation from 0.46 to 0.74. It is also shown that the profile score is robust for counting the coupling effect of multiple individual mutations. For the mutations involving more than two mutation sites where the correlation between FoldX and experimental data vanishes, the profile-based calculation retains a strong correlation with the experimental measurements.
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Affiliation(s)
- Peng Xiong
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Wei Zheng
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
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48
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Palmieri G, Balestrieri M, Proroga YT, Falcigno L, Facchiano A, Riccio A, Capuano F, Marrone R, Neglia G, Anastasio A. New antimicrobial peptides against foodborne pathogens: From in silico design to experimental evidence. Food Chem 2016; 211:546-54. [DOI: 10.1016/j.foodchem.2016.05.100] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Revised: 04/19/2016] [Accepted: 05/16/2016] [Indexed: 10/21/2022]
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49
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Pallara C, Jiménez-García B, Romero M, Moal IH, Fernández-Recio J. pyDock scoring for the new modeling challenges in docking: Protein-peptide, homo-multimers, and domain-domain interactions. Proteins 2016; 85:487-496. [PMID: 27701776 DOI: 10.1002/prot.25184] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Revised: 09/22/2016] [Accepted: 10/02/2016] [Indexed: 12/18/2022]
Abstract
The sixth CAPRI edition included new modeling challenges, such as the prediction of protein-peptide complexes, and the modeling of homo-oligomers and domain-domain interactions as part of the first joint CASP-CAPRI experiment. Other non-standard targets included the prediction of interfacial water positions and the modeling of the interactions between proteins and nucleic acids. We have participated in all proposed targets of this CAPRI edition both as predictors and as scorers, with new protocols to efficiently use our docking and scoring scheme pyDock in a large variety of scenarios. In addition, we have participated for the first time in the servers section, with our recently developed webserver, pyDockWeb. Excluding the CASP-CAPRI cases, we submitted acceptable models (or better) for 7 out of the 18 evaluated targets as predictors, 4 out of the 11 targets as scorers, and 6 out of the 18 targets as servers. The overall success rates were below those in past CAPRI editions. This shows the challenging nature of this last edition, with many difficult targets for which no participant submitted a single acceptable model. Interestingly, we submitted acceptable models for 83% of the evaluated protein-peptide targets. As for the 25 cases of the CASP-CAPRI experiment, in which we used a larger variety of modeling techniques (template-based, symmetry restraints, literature information, etc.), we submitted acceptable models for 56% of the targets. In summary, this CAPRI edition showed that pyDock scheme can be efficiently adapted to the increasing variety of problems that the protein interactions field is currently facing. Proteins 2017; 85:487-496. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Chiara Pallara
- Life Sciences Department, Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain
| | - Brian Jiménez-García
- Life Sciences Department, Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain
| | - Miguel Romero
- Life Sciences Department, Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain
| | - Iain H Moal
- Life Sciences Department, Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain.,European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Juan Fernández-Recio
- Life Sciences Department, Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain
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50
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Spyrakis F, Cozzini P, Eugene Kellogg G. Applying Computational Scoring Functions to Assess Biomolecular Interactions in Food Science: Applications to the Estrogen Receptors. NUCLEAR RECEPTOR RESEARCH 2016. [DOI: 10.11131/2016/101202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Francesca Spyrakis
- University of Parma, Department of Food Science, Molecular Modelling Laboratory, Parma, Italy
| | - Pietro Cozzini
- University of Parma, Department of Food Science, Molecular Modelling Laboratory, Parma, Italy
| | - Glen Eugene Kellogg
- Virginia Commonwealth University, Department of Medicinal Chemistry & Institute for Structural Biology, Drug Discovery and Development Richmond, Virginia, USA
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