1
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Ding X, Chen X, Sullivan EE, Shay TF, Gradinaru V. Fast, accurate ranking of engineered proteins by target-binding propensity using structure modeling. Mol Ther 2024; 32:1687-1700. [PMID: 38582966 DOI: 10.1016/j.ymthe.2024.04.003] [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: 10/17/2023] [Revised: 02/08/2024] [Accepted: 04/03/2024] [Indexed: 04/08/2024] Open
Abstract
Deep-learning-based methods for protein structure prediction have achieved unprecedented accuracy, yet their utility in the engineering of protein-based binders remains constrained due to a gap between the ability to predict the structures of candidate proteins and the ability toprioritize proteins by their potential to bind to a target. To bridge this gap, we introduce Automated Pairwise Peptide-Receptor Analysis for Screening Engineered proteins (APPRAISE), a method for predicting the target-binding propensity of engineered proteins. After generating structural models of engineered proteins competing for binding to a target using an established structure prediction tool such as AlphaFold-Multimer or ESMFold, APPRAISE performs a rapid (under 1 CPU second per model) scoring analysis that takes into account biophysical and geometrical constraints. As proof-of-concept cases, we demonstrate that APPRAISE can accurately classify receptor-dependent vs. receptor-independent adeno-associated viral vectors and diverse classes of engineered proteins such as miniproteins targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike, nanobodies targeting a G-protein-coupled receptor, and peptides that specifically bind to transferrin receptor or programmed death-ligand 1 (PD-L1). APPRAISE is accessible through a web-based notebook interface using Google Colaboratory (https://tiny.cc/APPRAISE). With its accuracy, interpretability, and generalizability, APPRAISE promises to expand the utility of protein structure prediction and accelerate protein engineering for biomedical applications.
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Affiliation(s)
- Xiaozhe Ding
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 E California, Boulevard, Pasadena, CA 91125, USA.
| | - Xinhong Chen
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 E California, Boulevard, Pasadena, CA 91125, USA
| | - Erin E Sullivan
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 E California, Boulevard, Pasadena, CA 91125, USA
| | - Timothy F Shay
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 E California, Boulevard, Pasadena, CA 91125, USA
| | - Viviana Gradinaru
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 E California, Boulevard, Pasadena, CA 91125, USA.
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2
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Harihar B, Saravanan KM, Gromiha MM, Selvaraj S. Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design. Mol Biotechnol 2024:10.1007/s12033-024-01119-4. [PMID: 38498284 DOI: 10.1007/s12033-024-01119-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 02/10/2024] [Indexed: 03/20/2024]
Abstract
Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.
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Affiliation(s)
- Balasubramanian Harihar
- Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu, 620024, India
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India
| | - Konda Mani Saravanan
- Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu, 620024, India
- Department of Biotechnology, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, 600073, India
| | - Michael M Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India
| | - Samuel Selvaraj
- Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu, 620024, India.
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3
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Xie B, Xu S, Schecterson L, Gumbiner BM, Sivasankar S. Strengthening E-cadherin adhesion via antibody-mediated binding stabilization. Structure 2024; 32:217-227.e3. [PMID: 38052206 PMCID: PMC10872345 DOI: 10.1016/j.str.2023.11.002] [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: 07/05/2023] [Revised: 09/10/2023] [Accepted: 11/08/2023] [Indexed: 12/07/2023]
Abstract
E-cadherins (Ecads) are a crucial cell-cell adhesion protein with tumor suppression properties. Ecad adhesion can be enhanced by the monoclonal antibody 66E8, which has potential applications in inhibiting cancer metastasis. However, the biophysical mechanisms underlying 66E8-mediated adhesion strengthening are unknown. Here, we use molecular dynamics simulations, site-directed mutagenesis, and single-molecule atomic force microscopy experiments to demonstrate that 66E8 strengthens Ecad binding by stabilizing the primary Ecad adhesive conformation: the strand-swap dimer. By forming electrostatic interactions with Ecad, 66E8 stabilizes the swapped β-strand and its hydrophobic pocket and impedes Ecad conformational changes, which are necessary for rupture of the strand-swap dimer. Our findings identify fundamental mechanistic principles for strengthening of Ecad binding using monoclonal antibodies.
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Affiliation(s)
- Bin Xie
- Biophysics Graduate Group, University of California, Davis, Davis, CA, USA
| | - Shipeng Xu
- Department of Biomedical Engineering, University of California, Davis, Davis, CA, USA
| | - Leslayann Schecterson
- Seattle Children's Research Institute, Center for Developmental Biology and Regenerative Medicine, Seattle, WA, USA
| | - Barry M Gumbiner
- Seattle Children's Research Institute, Center for Developmental Biology and Regenerative Medicine, Seattle, WA, USA
| | - Sanjeevi Sivasankar
- Biophysics Graduate Group, University of California, Davis, Davis, CA, USA; Department of Biomedical Engineering, University of California, Davis, Davis, CA, USA.
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4
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Skrdla PJ, Coscia BJ, Gavartin J, Browning A, Shelley J. Drug Aggregation of Sparingly-Soluble Ionizable Drugs: Molecular Dynamics Simulations of Papaverine and Prostaglandin F2α. Mol Pharm 2023; 20:5135-5147. [PMID: 37671526 DOI: 10.1021/acs.molpharmaceut.3c00429] [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: 09/07/2023]
Abstract
Aggregation in aqueous solution can have important implications on both the in vivo exposure of a drug and its pharmaceutical manufacturability. However, the drug aggregates formed can be very small and, thus, difficult to interrogate experimentally. On the other hand, at higher supersaturations where larger aggregates are supported, the chemical system is inherently metastable and therefore likewise challenging to study from an experimental standpoint. Understanding aggregation behavior is further complicated in the case of ionizable drugs where, unlike neutral compounds, there can be uncertainty in the kinds of drug molecules (i.e., charged, neutral, or both) that become incorporated into various clusters, particularly at pH values near the pKa. In this paper, we apply physics-based all-atom molecular dynamics (MD) simulations to study aggregation in the weakly basic drug papaverine and in the weakly acidic drug prostaglandin F2α. We employ in silico tools to construct simulation workflows and comprehensive cluster analysis protocols to elucidate the size distributions and dynamics of the drug aggregates formed at both an experimentally relevant concentration and at high supersaturation. We build on a previously published treatment [Solubility of sparingly soluble ionizable drugs. Adv. Drug Deliv. Rev. 2007, 59, 568-590, DOI: 10.1016/j.addr.2007.05.008] to translate the predicted aggregate distributions of each ionized drug into corresponding pH-solubility curves that can be compared directly to experiment. Our findings show that the assumption of a single predominant (charged) aggregate can be misleading in interpreting experimental pH-solubility curves, as it does not adequately reflect the rich diversity revealed in our simulations. Beyond not accounting for the distribution of ionized drug-containing clusters actually observed in solution, for both drugs we find evidence that neutral drug molecules can also participate in the aggregation phenomena. Notably, we observe that many drug molecules remain as free monomers in solution even under simulated conditions designed to mimic those where there is significant deviation of the experimental pH-solubility curve from the Henderson-Hasselbalch (H-H) equation, often taken to be a clear signpost of drug aggregation.
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5
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Jiang J, Boughter CT, Ahmad J, Natarajan K, Boyd LF, Meier-Schellersheim M, Margulies DH. SARS-CoV-2 antibodies recognize 23 distinct epitopic sites on the receptor binding domain. Commun Biol 2023; 6:953. [PMID: 37726484 PMCID: PMC10509263 DOI: 10.1038/s42003-023-05332-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 09/07/2023] [Indexed: 09/21/2023] Open
Abstract
The COVID-19 pandemic and SARS-CoV-2 variants have dramatically illustrated the need for a better understanding of antigen (epitope)-antibody (paratope) interactions. To gain insight into the immunogenic characteristics of epitopic sites (ES), we systematically investigated the structures of 340 Abs and 83 nanobodies (Nbs) complexed with the Receptor Binding Domain (RBD) of the SARS-CoV-2 spike protein. We identified 23 distinct ES on the RBD surface and determined the frequencies of amino acid usage in the corresponding CDR paratopes. We describe a clustering method for analysis of ES similarities that reveals binding motifs of the paratopes and that provides insights for vaccine design and therapies for SARS-CoV-2, as well as a broader understanding of the structural basis of Ab-protein antigen (Ag) interactions.
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Affiliation(s)
- Jiansheng Jiang
- Molecular Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892, USA.
| | - Christopher T Boughter
- Computational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892, USA
| | - Javeed Ahmad
- Molecular Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892, USA
| | - Kannan Natarajan
- Molecular Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892, USA
| | - Lisa F Boyd
- Molecular Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892, USA
| | - Martin Meier-Schellersheim
- Computational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892, USA
| | - David H Margulies
- Molecular Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892, USA.
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6
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Gheeraert A, Lesieur C, Batista VS, Vuillon L, Rivalta I. Connected Component Analysis of Dynamical Perturbation Contact Networks. J Phys Chem B 2023; 127:7571-7580. [PMID: 37641933 PMCID: PMC10493978 DOI: 10.1021/acs.jpcb.3c04592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 08/02/2023] [Indexed: 08/31/2023]
Abstract
Describing protein dynamical networks through amino acid contacts is a powerful way to analyze complex biomolecular systems. However, due to the size of the systems, identifying the relevant features of protein-weighted graphs can be a difficult task. To address this issue, we present the connected component analysis (CCA) approach that allows for fast, robust, and unbiased analysis of dynamical perturbation contact networks (DPCNs). We first illustrate the CCA method as applied to a prototypical allosteric enzyme, the imidazoleglycerol phosphate synthase (IGPS) enzyme from Thermotoga maritima bacteria. This approach was shown to outperform the clustering methods applied to DPCNs, which could not capture the propagation of the allosteric signal within the protein graph. On the other hand, CCA reduced the DPCN size, providing connected components that nicely describe the allosteric propagation of the signal from the effector to the active sites of the protein. By applying the CCA to the IGPS enzyme in different conditions, i.e., at high temperature and from another organism (yeast IGPS), and to a different enzyme, i.e., a protein kinase, we demonstrated how CCA of DPCNs is an effective and transferable tool that facilitates the analysis of protein-weighted networks.
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Affiliation(s)
- Aria Gheeraert
- Laboratoire
de Mathématiques (LAMA), Université
Savoie Mont Blanc, CNRS, 73376 Le Bourget du Lac, France
- Dipartimento
di Chimica Industriale “Toso Montanari”, Alma Mater
Studiorum, Università di Bologna, Viale del Risorgimento 4, 40136 Bologna, Italy
| | - Claire Lesieur
- Univ.
Lyon, CNRS, INSA Lyon, Université Claude Bernard Lyon 1, Ecole
Centrale de Lyon, Ampère UMR5005, Villeurbanne 69622, France
- Institut
Rhônalpin des Systèmes Complexes, IXXI-ENS-Lyon, Lyon 69007, France
| | - Victor S. Batista
- Department
of Chemistry, Yale University, New Haven, Connecticut 06520, United States
| | - Laurent Vuillon
- Laboratoire
de Mathématiques (LAMA), Université
Savoie Mont Blanc, CNRS, 73376 Le Bourget du Lac, France
- Institut
Rhônalpin des Systèmes Complexes, IXXI-ENS-Lyon, Lyon 69007, France
| | - Ivan Rivalta
- Dipartimento
di Chimica Industriale “Toso Montanari”, Alma Mater
Studiorum, Università di Bologna, Viale del Risorgimento 4, 40136 Bologna, Italy
- ENS
de Lyon,
CNRS, Laboratoire de Chimie UMR 5182, 69364 Lyon, France
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7
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Sora V, Tiberti M, Beltrame L, Dogan D, Robbani SM, Rubin J, Papaleo E. PyInteraph2 and PyInKnife2 to Analyze Networks in Protein Structural Ensembles. J Chem Inf Model 2023; 63:4237-4245. [PMID: 37437128 DOI: 10.1021/acs.jcim.3c00574] [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: 07/14/2023]
Abstract
Due to the complex nature of noncovalent interactions and their long-range effects, analyzing protein conformations using network theory can be enlightening. Protein Structure Networks (PSNs) provide a convenient formalism to study protein structures in relation to essential properties such as key residues for structural stability, allosteric communication, and the effects of modifications of the protein. PSNs can be defined according to very different principles, and the available tools have limitations in input formats, supported models, and version control. Other outstanding problems are related to the definition of network cutoffs and the assessment of the stability of the network properties. The protein science community could benefit from a common framework to carry out these analyses and make them easier to reproduce, reuse, and evaluate. We here provide two open-source software packages, PyInteraph2 and PyInKnife2, to implement and analyze PSNs in a reproducible and documented manner. PyInteraph2 interfaces with multiple formats for protein ensembles and incorporates different network models with the possibility of integrating them into a macronetwork and performing various downstream analyses, including hubs, connected components, and several other centrality measures, and visualizes the networks or further analyzes them thanks to compatibility with Cytoscape.PyInKnife2 that supports the network models implemented in PyInteraph2. It employs a jackknife resampling approach to estimate the convergence of network properties and streamline the selection of distance cutoffs. We foresee that the modular structure of the code and the supported version control system will promote the transition to a community-driven effort, boost reproducibility, and establish common protocols in the PSN field. As developers, we will guarantee the introduction of new functionalities and maintenance, assistance, and training of new contributors.
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Affiliation(s)
- Valentina Sora
- Cancer Structural Biology, Danish Cancer Institute, Strandboulevarden 49, 2100 Copenhagen, Denmark
- Cancer Systems Biology, Section of Bioinformatics, Department of Health and Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Matteo Tiberti
- Cancer Structural Biology, Danish Cancer Institute, Strandboulevarden 49, 2100 Copenhagen, Denmark
| | - Ludovica Beltrame
- Cancer Systems Biology, Section of Bioinformatics, Department of Health and Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Deniz Dogan
- Cancer Structural Biology, Danish Cancer Institute, Strandboulevarden 49, 2100 Copenhagen, Denmark
| | - Shahriyar Mahdi Robbani
- Cancer Structural Biology, Danish Cancer Institute, Strandboulevarden 49, 2100 Copenhagen, Denmark
| | - Joshua Rubin
- Cancer Structural Biology, Danish Cancer Institute, Strandboulevarden 49, 2100 Copenhagen, Denmark
| | - Elena Papaleo
- Cancer Structural Biology, Danish Cancer Institute, Strandboulevarden 49, 2100 Copenhagen, Denmark
- Cancer Systems Biology, Section of Bioinformatics, Department of Health and Technology, Technical University of Denmark, 2800 Lyngby, Denmark
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8
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Xie B, Xu S, Schecterson L, Gumbiner BM, Sivasankar S. Strengthening E-cadherin adhesion via antibody mediated binding stabilization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.04.547716. [PMID: 37461464 PMCID: PMC10350017 DOI: 10.1101/2023.07.04.547716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
E-cadherins (Ecads) are a crucial cell-cell adhesion protein with tumor suppression properties. Ecad adhesion can be enhanced by the monoclonal antibody 66E8, which has potential applications in inhibiting cancer metastasis. However, the biophysical mechanisms underlying 66E8 mediated adhesion strengthening are unknown. Here, we use molecular dynamics simulations, site directed mutagenesis and single molecule atomic force microscopy experiments to demonstrate that 66E8 strengthens Ecad binding by stabilizing the primary Ecad adhesive conformation: the strand-swap dimer. By forming electrostatic interactions with Ecad, 66E8 stabilizes the swapped β-strand and its hydrophobic pocket and impedes Ecad conformational changes, which are necessary for rupture of the strand-swap dimer. Our findings identify fundamental mechanistic principles for strengthening of Ecad binding using monoclonal antibodies.
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9
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Desta IT, Kotelnikov S, Jones G, Ghani U, Abyzov M, Kholodov Y, Standley DM, Beglov D, Vajda S, Kozakov D. The ClusPro AbEMap web server for the prediction of antibody epitopes. Nat Protoc 2023; 18:1814-1840. [PMID: 37188806 PMCID: PMC10898366 DOI: 10.1038/s41596-023-00826-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 01/19/2023] [Indexed: 05/17/2023]
Abstract
Antibodies play an important role in the immune system by binding to molecules called antigens at their respective epitopes. These interfaces or epitopes are structural entities determined by the interactions between an antibody and an antigen, making them ideal systems to analyze by using docking programs. Since the advent of high-throughput antibody sequencing, the ability to perform epitope mapping using only the sequence of the antibody has become a high priority. ClusPro, a leading protein-protein docking server, together with its template-based modeling version, ClusPro-TBM, have been re-purposed to map epitopes for specific antibody-antigen interactions by using the Antibody Epitope Mapping server (AbEMap). ClusPro-AbEMap offers three different modes for users depending on the information available on the antibody as follows: (i) X-ray structure, (ii) computational/predicted model of the structure or (iii) only the amino acid sequence. The AbEMap server presents a likelihood score for each antigen residue of being part of the epitope. We provide detailed information on the server's capabilities for the three options and discuss how to obtain the best results. In light of the recent introduction of AlphaFold2 (AF2), we also show how one of the modes allows users to use their AF2-generated antibody models as input. The protocol describes the relative advantages of the server compared to other epitope-mapping tools, its limitations and potential areas of improvement. The server may take 45-90 min depending on the size of the proteins.
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Affiliation(s)
- Israel T Desta
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - George Jones
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Usman Ghani
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | | | | | - Daron M Standley
- Department of Genome Informatics, Osaka University, Osaka, Japan
- Center for Infectious Disease Education and Research, Osaka University, Osaka, Japan
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.
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10
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Pan Y, Xie N, Zhang X, Yang S, Lv S. Computational Insights into the Dynamic Structural Features and Binding Characteristics of Recombinase UvsX Compared with RecA. Molecules 2023; 28:molecules28083363. [PMID: 37110596 PMCID: PMC10144138 DOI: 10.3390/molecules28083363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/06/2023] [Accepted: 04/07/2023] [Indexed: 04/29/2023] Open
Abstract
RecA family recombinases are the core enzymes in the process of homologous recombination, and their normal operation ensures the stability of the genome and the healthy development of organisms. The UvsX protein from bacteriophage T4 is a member of the RecA family recombinases and plays a central role in T4 phage DNA repair and replication, which provides an important model for the biochemistry and genetics of DNA metabolism. UvsX shares a high degree of structural similarity and function with RecA, which is the most deeply studied member of the RecA family. However, the detailed molecular mechanism of UvsX has not been resolved. In this study, a comprehensive all-atom molecular dynamics simulation of the UvsX protein dimer complex was carried out in order to investigate the conformational and binding properties of UvsX in combination with ATP and DNA, and the simulation of RecA was synchronized with the property comparison learning for UvsX. This study confirmed the highly conserved molecular structure characteristics and catalytic centers of RecA and UvsX, and also discovered differences in regional conformation, volatility and the ability to bind DNA between the two proteins at different temperatures, which would be helpful for the subsequent understanding and application of related recombinases.
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Affiliation(s)
- Yue Pan
- Key Laboratory for Molecular Enzymology and Engineering of the Ministry of Education, School of Life Science, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Ningkang Xie
- Key Laboratory for Molecular Enzymology and Engineering of the Ministry of Education, School of Life Science, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Xin Zhang
- Key Laboratory for Molecular Enzymology and Engineering of the Ministry of Education, School of Life Science, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Shuo Yang
- Key Laboratory for Molecular Enzymology and Engineering of the Ministry of Education, School of Life Science, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Shaowu Lv
- Key Laboratory for Molecular Enzymology and Engineering of the Ministry of Education, School of Life Science, Jilin University, 2699 Qianjin Street, Changchun 130012, China
- Bioarchaeology Laboratory, Jilin University, 2699 Qianjin Street, Changchun 130012, China
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11
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Desta IT, Kotelnikov S, Jones G, Ghani U, Abyzov M, Kholodov Y, Standley DM, Sabitova M, Beglov D, Vajda S, Kozakov D. Mapping of antibody epitopes based on docking and homology modeling. Proteins 2023; 91:171-182. [PMID: 36088633 PMCID: PMC9822860 DOI: 10.1002/prot.26420] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 08/25/2022] [Accepted: 09/06/2022] [Indexed: 01/11/2023]
Abstract
Antibodies are key proteins produced by the immune system to target pathogen proteins termed antigens via specific binding to surface regions called epitopes. Given an antigen and the sequence of an antibody the knowledge of the epitope is critical for the discovery and development of antibody based therapeutics. In this work, we present a computational protocol that uses template-based modeling and docking to predict epitope residues. This protocol is implemented in three major steps. First, a template-based modeling approach is used to build the antibody structures. We tested several options, including generation of models using AlphaFold2. Second, each antibody model is docked to the antigen using the fast Fourier transform (FFT) based docking program PIPER. Attention is given to optimally selecting the docking energy parameters depending on the input data. In particular, the van der Waals energy terms are reduced for modeled antibodies relative to x-ray structures. Finally, ranking of antigen surface residues is produced. The ranking relies on the docking results, that is, how often the residue appears in the docking poses' interface, and also on the energy favorability of the docking pose in question. The method, called PIPER-Map, has been tested on a widely used antibody-antigen docking benchmark. The results show that PIPER-Map improves upon the existing epitope prediction methods. An interesting observation is that epitope prediction accuracy starting from antibody sequence alone does not significantly differ from that of starting from unbound (i.e., separately crystallized) antibody structure.
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Affiliation(s)
- Israel T. Desta
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
| | - George Jones
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Usman Ghani
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | | | | | - Daron M. Standley
- Department of Genome Informatics, Osaka University, Osaka, 565-0871, Japan
- Center for Infectious Disease Education and Research, Osaka University, Osaka, 565-0871, Japan
| | - Maria Sabitova
- Department of Mathematics, CUNY Queens College, Flushing, NY 11367, USA
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
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12
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Felline A, Gentile S, Fanelli F. psnGPCRdb: The Structure-network Database of G Protein Coupled Receptors. J Mol Biol 2023:167950. [PMID: 36646374 DOI: 10.1016/j.jmb.2023.167950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/02/2023] [Accepted: 01/03/2023] [Indexed: 01/15/2023]
Abstract
G protein coupled receptors (GPCRs) are critical eukaryotic signal transduction gatekeepers and represent the largest protein superfamily in the human proteome, with more than 800 members. They share seven transmembrane helices organized in an up-down bundle architecture. GPCR-mediated signaling pathways have been linked to numerous human diseases, and GPCRs are the targets of approximately 35% of all drugs currently on the market. Structure network analysis, a graph theory-based approach, represents a cutting-edge tool to deeply understand GPCR function, which strongly relies on communication between the extracellular and intracellular poles of their structure. psnGPCRdb stores the structure networks (i.e., linked nodes, hubs, communities and communication pathways) computed on all updated GPCR structures in the Protein Data Bank, in their isolated states or in complex with extracellular and/or intracellular molecules. The structure communication signatures of a sub-family or family of GPCRs as well as of their small-molecule activators or inhibitors are stored as consensus networks. The database stores also all meaningful structure network-based comparisons (i.e., difference networks) of functionally different states (i.e., inactive or active) of a given receptor sub-type, or of consensus networks representative of a receptor sub-type, type, sub-family or family. Single or consensus GPCR networks hold also information on amino acid conservation. The database allows to graphically analyze 3D structure networks together with interactive data-tables. Ligand-centric networks can be analyzed as well. psnGPCRdb is unique and represents a powerful resource to unravel GPCR function with important implications in cell signaling and drug design. psnGPCRdb is freely available at: http://webpsn.hpc.unimo.it/psngpcr.php.
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Affiliation(s)
- Angelo Felline
- Department of Life Sciences, University of Modena and Reggio Emilia, via Campy 103, 41125 Modena, Italy
| | - Sara Gentile
- Department of Life Sciences, University of Modena and Reggio Emilia, via Campy 103, 41125 Modena, Italy
| | - Francesca Fanelli
- Department of Life Sciences, University of Modena and Reggio Emilia, via Campy 103, 41125 Modena, Italy; Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, via Campi 287, 41125 Modena, Italy.
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13
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Ibrahim AH, Karabulut OC, Karpuzcu BA, Türk E, Süzek BE. A correlation coefficient-based feature selection approach for virus-host protein-protein interaction prediction. PLoS One 2023; 18:e0285168. [PMID: 37130110 PMCID: PMC10153705 DOI: 10.1371/journal.pone.0285168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 04/17/2023] [Indexed: 05/03/2023] Open
Abstract
Prediction of virus-host protein-protein interactions (PPI) is a broad research area where various machine-learning-based classifiers are developed. Transforming biological data into machine-usable features is a preliminary step in constructing these virus-host PPI prediction tools. In this study, we have adopted a virus-host PPI dataset and a reduced amino acids alphabet to create tripeptide features and introduced a correlation coefficient-based feature selection. We applied feature selection across several correlation coefficient metrics and statistically tested their relevance in a structural context. We compared the performance of feature-selection models against that of the baseline virus-host PPI prediction models created using different classification algorithms without the feature selection. We also tested the performance of these baseline models against the previously available tools to ensure their predictive power is acceptable. Here, the Pearson coefficient provides the best performance with respect to the baseline model as measured by AUPR; a drop of 0.003 in AUPR while achieving a 73.3% (from 686 to 183) reduction in the number of tripeptides features for random forest. The results suggest our correlation coefficient-based feature selection approach, while decreasing the computation time and space complexity, has a limited impact on the prediction performance of virus-host PPI prediction tools.
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Affiliation(s)
- Ahmed Hassan Ibrahim
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Onur Can Karabulut
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Betül Asiye Karpuzcu
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Erdem Türk
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
- Department of Computer Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Barış Ethem Süzek
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
- Department of Computer Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University, Muğla, Turkey
- Georgetown University Medical Center, Biochemistry and Molecular & Cellular Biology, Washington DC, United States of America
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14
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The Importance of Charge Transfer and Solvent Screening in the Interactions of Backbones and Functional Groups in Amino Acid Residues and Nucleotides. Int J Mol Sci 2022; 23:ijms232113514. [PMID: 36362296 PMCID: PMC9654426 DOI: 10.3390/ijms232113514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 10/26/2022] [Accepted: 10/28/2022] [Indexed: 11/06/2022] Open
Abstract
Quantum mechanical (QM) calculations at the level of density-functional tight-binding are applied to a protein–DNA complex (PDB: 2o8b) consisting of 3763 atoms, averaging 100 snapshots from molecular dynamics simulations. A detailed comparison of QM and force field (Amber) results is presented. It is shown that, when solvent screening is taken into account, the contributions of the backbones are small, and the binding of nucleotides in the double helix is governed by the base–base interactions. On the other hand, the backbones can make a substantial contribution to the binding of amino acid residues to nucleotides and other residues. The effect of charge transfer on the interactions is also analyzed, revealing that the actual charge of nucleotides and amino acid residues can differ by as much as 6 and 8% from the formal integer charge, respectively. The effect of interactions on topological models (protein -residue networks) is elucidated.
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15
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Petrizzelli F, Biagini T, Bianco SD, Liorni N, Napoli A, Castellana S, Mazza T. Connecting the dots: A practical evaluation of web-tools for describing protein dynamics as networks. FRONTIERS IN BIOINFORMATICS 2022; 2:1045368. [PMID: 36438625 PMCID: PMC9689706 DOI: 10.3389/fbinf.2022.1045368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 10/05/2022] [Indexed: 01/25/2023] Open
Abstract
Protein Structure Networks (PSNs) are a well-known mathematical model for estimation and analysis of the three-dimensional protein structure. Investigating the topological architecture of PSNs may help identify the crucial amino acid residues for protein stability and protein-protein interactions, as well as deduce any possible mutational effects. But because proteins go through conformational changes to give rise to essential biological functions, this has to be done dynamically over time. The most effective method to describe protein dynamics is molecular dynamics simulation, with the most popular software programs for manipulating simulations to infer interaction networks being RING, MD-TASK, and NAPS. Here, we compare the computational approaches used by these three tools-all of which are accessible as web servers-to understand the pathogenicity of missense mutations and talk about their potential applications as well as their advantages and disadvantages.
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Affiliation(s)
- Francesco Petrizzelli
- Bioinformatics Laboratory, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Tommaso Biagini
- Bioinformatics Laboratory, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Salvatore Daniele Bianco
- Bioinformatics Laboratory, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy,Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Niccolò Liorni
- Bioinformatics Laboratory, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy,Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Alessandro Napoli
- Bioinformatics Laboratory, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Stefano Castellana
- Bioinformatics Laboratory, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Tommaso Mazza
- Bioinformatics Laboratory, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy,*Correspondence: Tommaso Mazza,
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16
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Otaki H, Taguchi Y, Nishida N. Conformation-Dependent Influences of Hydrophobic Amino Acids in Two In-Register Parallel β-Sheet Amyloids, an α-Synuclein Amyloid and a Local Structural Model of PrP Sc. ACS OMEGA 2022; 7:31271-31288. [PMID: 36092583 PMCID: PMC9453792 DOI: 10.1021/acsomega.2c03523] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/09/2022] [Indexed: 06/15/2023]
Abstract
Prions are unconventional pathogens that encode the pathogenic information in conformations of the constituent abnormal isoform of prion protein (PrPSc), independently of the nucleotide genome. Therefore, conformational diversity of PrPSc underlies the existence of many prion strains and species barriers of prions, although the conformational information is extremely limited. Interestingly, differences between polymorphic or species-specific residues responsible for the species/strain barriers are often caused by conservative replacements between hydrophobic amino acids. This implies that subtle differences among hydrophobic amino acids are significant for PrPSc structures. Here we analyzed the influence of different hydrophobic residues on the structures of an in-register parallel β-sheet amyloid of α-synuclein (αSyn) using molecular dynamics (MD) simulation and applied the knowledge from the αSyn amyloid to modeling a local structure of human PrPSc encompassing residues 107-143. We found that mutations equivalent to polymorphisms that cause transmission barriers substantially affect the stabilities of the local structures; for example, the G127V mutation, which makes the host resistant to various human prion diseases, greatly destabilized the local structure of the model amyloid. Our study indicates that subtle differences among hydrophobic side chains can considerably affect the interaction network, including hydrogen bonds, and demonstrates specifically how and in what structures hydrophobic residues can exert unique effects on in-register parallel β-sheet amyloids.
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Affiliation(s)
- Hiroki Otaki
- Center
for Bioinformatics and Molecular Medicine, Graduate School of Biomedical
Sciences, Nagasaki University, 1-14 Bunkyo-machi, Nagasaki 852-8521, Japan
| | - Yuzuru Taguchi
- Department
of Molecular Microbiology and Immunology, Graduate School of Biomedical
Sciences, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan
| | - Noriyuki Nishida
- Department
of Molecular Microbiology and Immunology, Graduate School of Biomedical
Sciences, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan
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17
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Dynamic stability of salt stable cowpea chlorotic mottle virus capsid protein dimers and pentamers of dimers. Sci Rep 2022; 12:14251. [PMID: 35995818 PMCID: PMC9395436 DOI: 10.1038/s41598-022-18019-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 08/03/2022] [Indexed: 12/03/2022] Open
Abstract
Intermediates of the self-assembly process of the salt stable cowpea chlorotic mottle virus (ss-CCMV) capsid can be modelled atomistically on realistic computational timescales either by studying oligomers in equilibrium or by focusing on their dissociation instead of their association. Our previous studies showed that among the three possible dimer interfaces in the icosahedral capsid, two are thermodynamically relevant for capsid formation. The aim of the current study is to evaluate the relative structural stabilities of the three different ss-CCMV dimers and to find and understand the conditions that lead to their dissociation. Long timescale molecular dynamics simulations at 300 K of the various dimers and of the pentamer of dimers underscore the importance of large contact surfaces on stabilizing the capsid subunits within an oligomer. Simulations in implicit solvent show that at higher temperature (350 K), the N-terminal tails of the protein units act as tethers, delaying dissociation for all but the most stable interface. The pentamer of dimers is also found to be stable on long timescales at 300 K, with an inherent flexibility of the outer protein chains.
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18
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Schaefer‐Ramadan S, Aleksic J, Al‐Thani NM, Malek JA. Novel protein contact points among TP53 and minichromosome maintenance complex proteins 2, 3, and 5. Cancer Med 2022; 11:4989-5000. [PMID: 35567389 PMCID: PMC9761056 DOI: 10.1002/cam4.4805] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 04/11/2022] [Accepted: 04/26/2022] [Indexed: 02/03/2023] Open
Abstract
OBJECTIVE Identify protein contact points between TP53 and minichromosome maintenance (MCM) complex proteins 2, 3, and 5 with high resolution allowing for potential novel Cancer drug design. METHODS A next-generation sequencing-based protein-protein interaction method developed in our laboratory called AVA-Seq was applied to a gold-standard human protein interaction set. Proteins including TP53, MCM2, MCM3, MCM5, HSP90AA1, PCNA, NOD1, and others were sheared and ligated into the AVA-Seq system. Protein-protein interactions were then identified in both mild and stringent selective conditions. RESULTS Known interactions among MCM2, MCM3, and MCM5 were identified with the AVA-Seq system. The interacting regions detected between these three proteins overlap with the structural data of the MCM complex, and novel domains were identified with high resolution determined by multiple overlapping fragments. Fragments of wild type TP53 were shown to interact with MCM2, MCM3, and MCM5, and details on the location of the interactions were provided. Finally, a mini-network of known and novel cancer protein interactions was provided, which could have implications for fundamental changes in multiple cancers. CONCLUSION We provide a high-resolution mini-interactome that could direct novel drug targets and implicate possible effects of specific cancer mutations.
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Affiliation(s)
| | - Jovana Aleksic
- Department of Genetic MedicineWeill Cornell Medicine in QatarDohaQatar
| | - Nayra M. Al‐Thani
- Department of Genetic MedicineWeill Cornell Medicine in QatarDohaQatar
| | - Joel A. Malek
- Department of Genetic MedicineWeill Cornell Medicine in QatarDohaQatar
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19
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Felline A, Seeber M, Fanelli F. PSNtools for standalone and web-based structure network analyses of conformational ensembles. Comput Struct Biotechnol J 2022; 20:640-649. [PMID: 35140884 PMCID: PMC8801349 DOI: 10.1016/j.csbj.2021.12.044] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 12/22/2021] [Accepted: 12/30/2021] [Indexed: 12/03/2022] Open
Abstract
Structure graphs, in which interacting amino acids/nucleotides correspond to linked nodes, represent cutting-edge tools to investigate macromolecular function. The graph-based approach defined as Protein Structure Network (PSN) was initially implemented in the Wordom software and subsequently in the webPSN server. PSNs are computed either on a molecular dynamics (MD) trajectory (PSN-MD) or on a single structure. In the latter case, information on atomic fluctuations is inferred from the Elastic Network Model-Normal Mode Analysis (ENM-NMA) (PSN-ENM). While Wordom performs both PSN-ENM and PSN-MD analyses but without output post-processing, the webPSN server performs only single-structure PSN-EMN but assisting the user in input setup and output analysis. Here we release for the first time the standalone software PSNtools, which allows calculation and post-processing of PSN analyses carried out either on single structures or on conformational ensembles. Relevant unique and novel features of PSNtools are either comparisons of two networks or computations of consensus networks on sets of homologous/analogous macromolecular structures or conformational ensembles. Network comparisons and consensus serve to infer differences in functionally different states of the same system or network-based signatures in groups of bio-macromolecules sharing either the same functionality or the same fold. In addition to the new software, here we release also an updated version of the webPSN server, which allows performing an interactive graphical analysis of PSN-MD, following the upload of the PSNtools output. PSNtools, the auxiliary binary version of Wordom software, and the WebPSN server are freely available at http://webpsn.hpc.unimo.it/wpsn3.php.
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20
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Ahmad S, Strunk CH, Schott-Verdugo SN, Jaeger KE, Kovacic F, Gohlke H. Substrate Access Mechanism in a Novel Membrane-Bound Phospholipase A of Pseudomonas aeruginosa Concordant with Specificity and Regioselectivity. J Chem Inf Model 2021; 61:5626-5643. [PMID: 34748335 DOI: 10.1021/acs.jcim.1c00973] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
PlaF is a cytoplasmic membrane-bound phospholipase A1 from Pseudomonas aeruginosa that alters the membrane glycerophospholipid (GPL) composition and fosters the virulence of this human pathogen. PlaF activity is regulated by a dimer-to-monomer transition followed by tilting of the monomer in the membrane. However, how substrates reach the active site and how the characteristics of the active site tunnels determine the activity, specificity, and regioselectivity of PlaF for natural GPL substrates have remained elusive. Here, we combined unbiased and biased all-atom molecular dynamics (MD) simulations and configurational free-energy computations to identify access pathways of GPL substrates to the catalytic center of PlaF. Our results map out a distinct tunnel through which substrates access the catalytic center. PlaF variants with bulky tryptophan residues in this tunnel revealed decreased catalysis rates due to tunnel blockage. The MD simulations suggest that GPLs preferably enter the active site with the sn-1 acyl chain first, which agrees with the experimentally demonstrated PLA1 activity of PlaF. We propose that the acyl chain-length specificity of PlaF is determined by the structural features of the access tunnel, which results in favorable free energy of binding of medium-chain GPLs. The suggested egress route conveys fatty acid (FA) products to the dimerization interface and, thus, contributes to understanding the product feedback regulation of PlaF by FA-triggered dimerization. These findings open up opportunities for developing potential PlaF inhibitors, which may act as antibiotics against P. aeruginosa.
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Affiliation(s)
- Sabahuddin Ahmad
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Christoph Heinrich Strunk
- Institute of Molecular Enzyme Technology, Heinrich Heine University Düsseldorf, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Stephan N Schott-Verdugo
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Centro de Bioinformática y Simulación Molecular (CBSM), Faculty of Engineering, University of Talca, 3460000 Talca, Chile.,John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC), Institute of Biological Information Processing (IBI-7: Structural Biochemistry) & Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Karl-Erich Jaeger
- Institute of Molecular Enzyme Technology, Heinrich Heine University Düsseldorf, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.,Institute of Bio- and Geosciences (IBG-1: Biotechnology), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Filip Kovacic
- Institute of Molecular Enzyme Technology, Heinrich Heine University Düsseldorf, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Holger Gohlke
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany.,John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC), Institute of Biological Information Processing (IBI-7: Structural Biochemistry) & Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
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21
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Pacini L, Dorantes-Gilardi R, Vuillon L, Lesieur C. Mapping Function from Dynamics: Future Challenges for Network-Based Models of Protein Structures. Front Mol Biosci 2021; 8:744646. [PMID: 34708077 PMCID: PMC8543124 DOI: 10.3389/fmolb.2021.744646] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 08/19/2021] [Indexed: 11/25/2022] Open
Abstract
Proteins fulfill complex and diverse biological functions through the controlled atomic motions of their structures (functional dynamics). The protein composition is given by its amino-acid sequence, which was assumed to encode the function. However, the discovery of functional sequence variants proved that the functional encoding does not come down to the sequence, otherwise a change in the sequence would mean a change of function. Likewise, the discovery that function is fulfilled by a set of structures and not by a unique structure showed that the functional encoding does not come down to the structure either. That leaves us with the possibility that a set of atomic motions, achievable by different sequences and different structures, encodes a specific function. Thanks to the exponential growth in annual depositions in the Protein Data Bank of protein tridimensional structures at atomic resolutions, network models using the Cartesian coordinates of atoms of a protein structure as input have been used over 20 years to investigate protein features. Combining networks with experimental measures or with Molecular Dynamics (MD) simulations and using typical or ad-hoc network measures is well suited to decipher the link between protein dynamics and function. One perspective is to consider static structures alone as alternatives to address the question and find network measures relevant to dynamics that can be subsequently used for mining and classification of dynamic sequence changes functionally robust, adaptable or faulty. This way the set of dynamics that fulfill a function over a diversity of sequences and structures will be determined.
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Affiliation(s)
- Lorenza Pacini
- Ecole Centrale de Lyon, Ampère, UMR5005, Univ. Lyon, CNRS, INSA Lyon, Université Claude Bernard Lyon 1, Villeurbanne, France
- Institut Rhônalpin des Systèmes Complexes, IXXI-ENS-Lyon, Lyon, France
| | - Rodrigo Dorantes-Gilardi
- Institut Rhônalpin des Systèmes Complexes, IXXI-ENS-Lyon, Lyon, France
- USMB, CNRS, LAMA UMR5127, Le Bourget du Lac, France
| | | | - Claire Lesieur
- Ecole Centrale de Lyon, Ampère, UMR5005, Univ. Lyon, CNRS, INSA Lyon, Université Claude Bernard Lyon 1, Villeurbanne, France
- Institut Rhônalpin des Systèmes Complexes, IXXI-ENS-Lyon, Lyon, France
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22
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Ghadie M, Xia Y. Mutation Edgotype Drives Fitness Effect in Human. FRONTIERS IN BIOINFORMATICS 2021; 1:690769. [PMID: 36303776 PMCID: PMC9581054 DOI: 10.3389/fbinf.2021.690769] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 08/18/2021] [Indexed: 11/24/2022] Open
Abstract
Missense mutations are known to perturb protein-protein interaction networks (known as interactome networks) in different ways. However, it remains unknown how different interactome perturbation patterns (“edgotypes”) impact organismal fitness. Here, we estimate the fitness effect of missense mutations with different interactome perturbation patterns in human, by calculating the fractions of neutral and deleterious mutations that do not disrupt PPIs (“quasi-wild-type”), or disrupt PPIs either by disrupting the binding interface (“edgetic”) or by disrupting overall protein stability (“quasi-null”). We first map pathogenic mutations and common non-pathogenic mutations onto homology-based three-dimensional structural models of proteins and protein-protein interactions in human. Next, we perform structure-based calculations to classify each mutation as either quasi-wild-type, edgetic, or quasi-null. Using our predicted as well as experimentally determined interactome perturbation patterns, we estimate that >∼40% of quasi-wild-type mutations are effectively neutral and the remaining are mostly mildly deleterious, that >∼75% of edgetic mutations are only mildly deleterious, and that up to ∼75% of quasi-null mutations may be strongly detrimental. These estimates are the first such estimates of fitness effect for different network perturbation patterns in any interactome. Our results suggest that while mutations that do not disrupt the interactome tend to be effectively neutral, the majority of human PPIs are under strong purifying selection and the stability of most human proteins is essential to human life.
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23
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Sladek V, Yamamoto Y, Harada R, Shoji M, Shigeta Y, Sladek V. pyProGA-A PyMOL plugin for protein residue network analysis. PLoS One 2021; 16:e0255167. [PMID: 34329304 PMCID: PMC8323899 DOI: 10.1371/journal.pone.0255167] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 07/11/2021] [Indexed: 11/18/2022] Open
Abstract
The field of protein residue network (PRN) research has brought several useful methods and techniques for structural analysis of proteins and protein complexes. Many of these are ripe and ready to be used by the proteomics community outside of the PRN specialists. In this paper we present software which collects an ensemble of (network) methods tailored towards the analysis of protein-protein interactions (PPI) and/or interactions of proteins with ligands of other type, e.g. nucleic acids, oligosaccharides etc. In parallel, we propose the use of the network differential analysis as a method to identify residues mediating key interactions between proteins. We use a model system, to show that in combination with other, already published methods, also included in pyProGA, it can be used to make such predictions. Such extended repertoire of methods allows to cross-check predictions with other methods as well, as we show here. In addition, the possibility to construct PRN models from various kinds of input is so far a unique asset of our code. One can use structural data as defined in PDB files and/or from data on residue pair interaction energies, either from force-field parameters or fragment molecular orbital (FMO) calculations. pyProGA is a free open-source software available from https://gitlab.com/Vlado_S/pyproga.
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Affiliation(s)
- Vladimir Sladek
- Institute of Chemistry, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Yuta Yamamoto
- Department of Chemistry, Rikkyo University, Nishi-Ikebukuro, Tokyo, Japan
| | - Ryuhei Harada
- Center for Computational Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Mitsuo Shoji
- Center for Computational Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Yasuteru Shigeta
- Center for Computational Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Vladimir Sladek
- Institute of Construction and Architecture, Slovak Academy of Sciences, Bratislava, Slovakia
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24
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Di Venere A, Nicolai E, Minicozzi V, Caccuri AM, Di Paola L, Mei G. The Odd Faces of Oligomers: The Case of TRAF2-C, A Trimeric C-Terminal Domain of TNF Receptor-Associated Factor. Int J Mol Sci 2021; 22:ijms22115871. [PMID: 34070875 PMCID: PMC8198530 DOI: 10.3390/ijms22115871] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/20/2021] [Accepted: 05/26/2021] [Indexed: 12/31/2022] Open
Abstract
TNF Receptor Associated Factor 2 (TRAF2) is a trimeric protein that belongs to the TNF receptor associated factor family (TRAFs). The TRAF2 oligomeric state is crucial for receptor binding and for its interaction with other proteins involved in the TNFR signaling. The monomer-trimer equilibrium of a C- terminal domain truncated form of TRAF2 (TRAF2-C), plays also a relevant role in binding the membrane, causing inward vesiculation. In this study, we have investigated the conformational dynamics of TRAF2-C through circular dichroism, fluorescence, and dynamic light scattering, performing temperature-dependent measurements. The data indicate that the protein retains its oligomeric state and most of its secondary structure, while displaying a significative increase in the heterogeneity of the tyrosines signal, increasing the temperature from ≈15 to ≈35 °C. The peculiar crowding of tyrosine residues (12 out of 18) at the three subunit interfaces and the strong dependence on the trimer concentration indicate that such conformational changes mainly involve the contact areas between each pair of monomers, affecting the oligomeric state. Molecular dynamic simulations in this temperature range suggest that the interfaces heterogeneity is an intrinsic property of the trimer that arises from the continuous, asymmetric approaching and distancing of its subunits. Such dynamics affect the results of molecular docking on the external protein surface using receptor peptides, indicating that the TRAF2-receptor interaction in the solution might not involve three subunits at the same time, as suggested by the static analysis obtainable from the crystal structure. These findings shed new light on the role that the TRAF2 oligomeric state might have in regulating the protein binding activity in vivo.
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Affiliation(s)
- Almerinda Di Venere
- Department of Experimental Medicine, Tor Vergata University of Rome, Via Montpellier 1, 00133 Rome, Italy; (A.D.V.); (E.N.)
| | - Eleonora Nicolai
- Department of Experimental Medicine, Tor Vergata University of Rome, Via Montpellier 1, 00133 Rome, Italy; (A.D.V.); (E.N.)
| | - Velia Minicozzi
- Department of Physics, Tor Vergata University of Rome, Via Della Ricerca Scientifica 1, 00133 Rome, Italy;
| | - Anna Maria Caccuri
- Department of Chemistry, University of Rome Tor Vergata, Via Della Ricerca Scientifica 1, 00133 Rome, Italy;
| | - Luisa Di Paola
- Unit of Chemical-Physics Fundamentals in Chemical Engineering, Department of Engineering, University Campus Bio-Medico of Rome, Via Álvaro del Portillo 21, 00128 Rome, Italy
- Correspondence: (L.D.P.); (G.M.)
| | - Giampiero Mei
- Department of Experimental Medicine, Tor Vergata University of Rome, Via Montpellier 1, 00133 Rome, Italy; (A.D.V.); (E.N.)
- Correspondence: (L.D.P.); (G.M.)
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25
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Kumari A, Mittal L, Srivastava M, Asthana S. Binding mode characterization of 13b in the monomeric and dimeric states of SARS-CoV-2 main protease using molecular dynamics simulations. J Biomol Struct Dyn 2021; 40:9287-9305. [PMID: 34029506 DOI: 10.1080/07391102.2021.1927844] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The main protease, Mpro/3CLpro, plays an essential role in processing polyproteins translated from viral RNA to produce functional viral proteins and therefore serve as an attractive target for discovering COVID-19 therapeutics. The availability of both monomer and dimer crystal bound with a common ligand, '13b' (α-ketoamide inhibitor), opened up opportunities to understand the Mpro mechanism of action. A comparative analysis of both forms of Mpro was carried out to elucidate the binding site architectural differences in the presence and absence of '13b'. Molecular dynamics simulations suggest that the presence of '13b' enhances the stability of Mpro than the unbound APO form. The N- and C- terminals of both the protomers stabilize each other, and making it's interface essential for the active form of Mpro. In comparison to monomer, the relatively high affinity of '13b' is gained in dimer pocket due to the high stability of the pocket by the interaction of S1 residue of chain B with residues F140, E166 and H172 of chain A, which is absent in monomer. The comprehensive essential dynamics, protein structure network analysis and thermodynamic profiling highlight the hot-spots, pivotal in molecular recognition process at protein-ligand and protein-protein interaction levels, cross-validated through computational alanine scanning study. A comparative description of '13b' binding mechanism in both forms illustrates valuable insights into the inhibition mechanism and the selection of critical residues suitable for the structure-based approaches for the identification of more potent Mpro inhibitors.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Anita Kumari
- Translational Health Science and Technology Institute (THSTI), Faridabad, Haryana, India
| | - Lovika Mittal
- Translational Health Science and Technology Institute (THSTI), Faridabad, Haryana, India
| | - Mitul Srivastava
- Translational Health Science and Technology Institute (THSTI), Faridabad, Haryana, India
| | - Shailendra Asthana
- Translational Health Science and Technology Institute (THSTI), Faridabad, Haryana, India
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26
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Sora V, Sanchez D, Papaleo E. Bcl-xL Dynamics under the Lens of Protein Structure Networks. J Phys Chem B 2021; 125:4308-4320. [PMID: 33848145 DOI: 10.1021/acs.jpcb.0c11562] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Understanding the finely orchestrated interactions leading to or preventing programmed cell death (apoptosis) is of utmost importance in cancer research because the failure of these systems could eventually lead to the onset of the disease. In this regard, the maintenance of a delicate balance between the promoters and inhibitors of mitochondrial apoptosis is crucial, as demonstrated by the interplay among the Bcl-2 family members. In particular, B-cell lymphoma extra-large (Bcl-xL) is a target of interest due to the forefront role of its dysfunctions in cancer development. Bcl-xL prevents apoptosis by binding both the pro-apoptotic BH3-only proteins, like PUMA, and the noncanonical partners, such as p53, at different sites. An allosteric communication between the BH3-only protein binding pocket and the p53 binding site, mediating the release of p53 from Bcl-xL upon PUMA binding, has been postulated and supported by nuclear magnetic resonance and other biophysical data. The molecular details of this mechanism, especially at the residue level, remain unclear. In this work, we investigated the distal communication between these two sites in Bcl-xL in its free state and when bound to PUMA. We also evaluated how missense mutations of Bcl-xL found in cancer samples might impair this communication and therefore the allosteric mechanism. We employed all-atom explicit solvent microsecond molecular dynamics simulations, analyzed through a Protein Structure Network approach and integrated with calculations of changes in free energies upon cancer-related mutations identified by genomics studies. We found a subset of candidate residues responsible for both maintaining protein stability and for conveying structural information between the two binding sites and hypothesized possible communication routes between specific residues at both sites.
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Affiliation(s)
- Valentina Sora
- Computational Biology Laboratory, Danish Cancer Society Research Center, 2100 Copenhagen, Denmark.,Cancer Systems Biology, Section for Bioinformatics, Department of Health and Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Dionisio Sanchez
- Computational Biology Laboratory, Danish Cancer Society Research Center, 2100 Copenhagen, Denmark
| | - Elena Papaleo
- Computational Biology Laboratory, Danish Cancer Society Research Center, 2100 Copenhagen, Denmark.,Cancer Systems Biology, Section for Bioinformatics, Department of Health and Technology, Technical University of Denmark, 2800 Lyngby, Denmark
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27
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Akbar R, Robert PA, Pavlović M, Jeliazkov JR, Snapkov I, Slabodkin A, Weber CR, Scheffer L, Miho E, Haff IH, Haug DTT, Lund-Johansen F, Safonova Y, Sandve GK, Greiff V. A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding. Cell Rep 2021; 34:108856. [PMID: 33730590 DOI: 10.1016/j.celrep.2021.108856] [Citation(s) in RCA: 75] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 11/29/2020] [Accepted: 02/22/2021] [Indexed: 12/16/2022] Open
Abstract
Antibody-antigen binding relies on the specific interaction of amino acids at the paratope-epitope interface. The predictability of antibody-antigen binding is a prerequisite for de novo antibody and (neo-)epitope design. A fundamental premise for the predictability of antibody-antigen binding is the existence of paratope-epitope interaction motifs that are universally shared among antibody-antigen structures. In a dataset of non-redundant antibody-antigen structures, we identify structural interaction motifs, which together compose a commonly shared structure-based vocabulary of paratope-epitope interactions. We show that this vocabulary enables the machine learnability of antibody-antigen binding on the paratope-epitope level using generative machine learning. The vocabulary (1) is compact, less than 104 motifs; (2) distinct from non-immune protein-protein interactions; and (3) mediates specific oligo- and polyreactive interactions between paratope-epitope pairs. Our work leverages combined structure- and sequence-based learning to demonstrate that machine-learning-driven predictive paratope and epitope engineering is feasible.
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Affiliation(s)
- Rahmad Akbar
- Department of Immunology, University of Oslo, Oslo, Norway.
| | | | - Milena Pavlović
- Department of Informatics, University of Oslo, Oslo, Norway; Centre for Bioinformatics, University of Oslo, Norway; K.G. Jebsen Centre for Coeliac Disease Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | - Igor Snapkov
- Department of Immunology, University of Oslo, Oslo, Norway
| | | | - Cédric R Weber
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Lonneke Scheffer
- Department of Informatics, University of Oslo, Oslo, Norway; Centre for Bioinformatics, University of Oslo, Norway
| | - Enkelejda Miho
- Institute of Medical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
| | | | | | | | - Yana Safonova
- Computer Science and Engineering Department, University of California, San Diego, La Jolla, CA, USA
| | - Geir K Sandve
- Department of Informatics, University of Oslo, Oslo, Norway; Centre for Bioinformatics, University of Oslo, Norway; K.G. Jebsen Centre for Coeliac Disease Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo, Oslo, Norway.
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28
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Dang TKL, Nguyen T, Habeck M, Gültas M, Waack S. A graph-based algorithm for detecting rigid domains in protein structures. BMC Bioinformatics 2021; 22:66. [PMID: 33579190 PMCID: PMC7881620 DOI: 10.1186/s12859-021-03966-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 01/08/2021] [Indexed: 11/17/2022] Open
Abstract
Background Conformational transitions are implicated in the biological function of many proteins. Structural changes in proteins can be described approximately as the relative movement of rigid domains against each other. Despite previous efforts, there is a need to develop new domain segmentation algorithms that are capable of analysing the entire structure database efficiently and do not require the choice of protein-dependent tuning parameters such as the number of rigid domains. Results We develop a graph-based method for detecting rigid domains in proteins. Structural information from multiple conformational states is represented by a graph whose nodes correspond to amino acids. Graph clustering algorithms allow us to reduce the graph and run the Viterbi algorithm on the associated line graph to obtain a segmentation of the input structures into rigid domains. In contrast to many alternative methods, our approach does not require knowledge about the number of rigid domains. Moreover, we identified default values for the algorithmic parameters that are suitable for a large number of conformational ensembles. We test our algorithm on examples from the DynDom database and illustrate our method on various challenging systems whose structural transitions have been studied extensively. Conclusions The results strongly suggest that our graph-based algorithm forms a novel framework to characterize structural transitions in proteins via detecting their rigid domains. The web server is available at http://azifi.tz.agrar.uni-goettingen.de/webservice/.
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Affiliation(s)
- Truong Khanh Linh Dang
- Institute of Computer Science, University of Göttingen, Goldschmidtstr 7, 37077, Göttingen, Germany.
| | - Thach Nguyen
- Felix Bernstein Institute for Mathematical Statistics in the Biosciences, University of Göttingen, Goldschmidtstr 7, 37077, Göttingen, Germany
| | - Michael Habeck
- Felix Bernstein Institute for Mathematical Statistics in the Biosciences, University of Göttingen, Goldschmidtstr 7, 37077, Göttingen, Germany.,Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077, Göttingen, Germany.,Microscopic Image Analysis Group, University Hospital Jena, Am Klinikum 1, 07747, Jena, Germany
| | - Mehmet Gültas
- Breeding Informatics Group, Department of Animal Sciences, Margarethe von Wrangell-Weg 7, 37075, Göttingen, Germany.,Center for Integrated Breeding Research (CiBreed), Albrecht-Thaer-Weg 3, 37075, Göttingen, Germany
| | - Stephan Waack
- Institute of Computer Science, University of Göttingen, Goldschmidtstr 7, 37077, Göttingen, Germany
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29
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Abstract
Proteins are located in the twilight zone between chemistry and biology, where a peculiar kind of complexity starts. Proteins are the smallest 'devices' showing a sensible adaptation to their environment by the production of appropriate behavior when facing a specific stimulus. This fact qualifies (from the 'effector' side) proteins as nanomachines working as catalysts, motors, or switches. However (from the sensor side), the need to single out the 'specific stimulus' out of thermal noise qualifies proteins as information processing devices. Allostery corresponds to the modification of the configuration (in a broad sense) of the protein molecule in response to a specific stimulus in a non-strictly local way, thereby connecting the sensor and effector sides of the nanomachine. This is why the 'disclosing' of allostery phenomenon is at the very heart of protein function; in this chapter, we will demonstrate how a network-based representation of protein structure in terms of nodes (aminoacid residues) and edges (effective contacts between residues) is the natural language for getting rid of allosteric phenomena and, more in general, of protein structure/function relationships.
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Affiliation(s)
- Luisa Di Paola
- Unit of Chemical-Physics Fundamentals in Chemical Engineering, Department of Engineering, Università Campus Bio-Medico di Rome, Rome, Italy.
| | - Giampiero Mei
- Department of Experimental Medicine and Surgery, University of Rome "Tor Vergata", Rome, Italy
| | - Almerinda Di Venere
- Department of Experimental Medicine and Surgery, University of Rome "Tor Vergata", Rome, Italy
| | - Alessandro Giuliani
- Environment and Health Department, Instituto Superiore di Sanità, Rome, Italy
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30
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Wright WE, Li C, Zheng CX, Tucker HO. FOXP1 Interacts with MyoD to Repress its Transcription and Myoblast Conversion. JOURNAL OF CELLULAR SIGNALING 2021; 2:9-26. [PMID: 33554216 PMCID: PMC7861563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
Forkhead transcription factors (TFs) often dimerize outside their extensive family, whereas bHLH transcription factors typically dimerize with E12/E47. Based on structural similarities, we predicted that a member of the former, Forkhead Box P1 (FOXP1), might heterodimerize with a member of the latter, MYOD1 (MyoD). Data shown here support this hypothesis and further demonstrate the specificity of this forkhead/myogenic interaction among other myogenic regulatory factors. We found that FOXP1-MyoD heterodimerization compromises the ability of MyoD to bind to E-boxes and to transactivate E box- containing promoters. We observed that FOXP1 is required for the full ability of MyoD to convert fibroblasts into myotubules. We provide a model in which FOXP1 displaces ID and E12/E47 to repress MyoD during the proliferative phase of myoblast differentiation. These data identify FOXP1 as a hitherto unsuspected transcriptional repressor of MyoD. We suggest that isolation of paired E-box and forkhead sites within 1 turn helical spacings provides potential for cooperative interactions among heretofore distinct classes of transcription factors.
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Affiliation(s)
- Woodring E. Wright
- Department of Cell Biology, UT Southwestern Medical School,
Dallas TX 75235, USA
| | - Chuan Li
- Department of Microbiology, University of Texas
Southwestern Medical Center, Dallas TX 75235, USA
| | - Chang-xue Zheng
- Department of Molecular Biosciences, the University of
Texas at Austin, Austin TX 78712, USA
| | - Haley O. Tucker
- Department of Molecular Biosciences, the University of
Texas at Austin, Austin TX 78712, USA,Correspondence should be addressed to Haley O.
Tucker;
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31
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Papaleo E. Investigating Conformational Dynamics and Allostery in the p53 DNA-Binding Domain Using Molecular Simulations. Methods Mol Biol 2021; 2253:221-244. [PMID: 33315226 DOI: 10.1007/978-1-0716-1154-8_13] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The p53 tumor suppressor is a multifaceted context-dependent protein, which is involved in multiple cellular pathways, with the ability to either keep the cells alive or to kill them through mechanisms such as apoptosis. To complicate this picture, cancer cells that express mutant p53 becomes addicted to the mutant activity, so that the mutant variant features a myriad of gain-of-function activities, opening different venues for therapy. This makes essential to think outside the box and apply new approaches to the study of p53 structure-(mis)function relationship to find new critical components of its pathway or to understand how known parts are interconnected, compete, or cooperate. In this context, I will here illustrate how to integrate different computational methods to the identification of possible allosteric effects transmitted from the DNA binding interface of p53 to regions for cofactor recruitment. The protocol can be extended to any other cases of study. Indeed, it does not necessarily apply only to the study of DNA-induced effects, but more broadly to the investigation of long-range effects induced by a biological partner that binds to a biomolecule of interest.
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Affiliation(s)
- Elena Papaleo
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark.
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32
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Halder A, Anto A, Subramanyan V, Bhattacharyya M, Vishveshwara S, Vishveshwara S. Surveying the Side-Chain Network Approach to Protein Structure and Dynamics: The SARS-CoV-2 Spike Protein as an Illustrative Case. Front Mol Biosci 2020; 7:596945. [PMID: 33392257 PMCID: PMC7775578 DOI: 10.3389/fmolb.2020.596945] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 11/04/2020] [Indexed: 02/04/2023] Open
Abstract
Network theory-based approaches provide valuable insights into the variations in global structural connectivity between different dynamical states of proteins. Our objective is to review network-based analyses to elucidate such variations, especially in the context of subtle conformational changes. We present technical details of the construction and analyses of protein structure networks, encompassing both the non-covalent connectivity and dynamics. We examine the selection of optimal criteria for connectivity based on the physical concept of percolation. We highlight the advantages of using side-chain-based network metrics in contrast to backbone measurements. As an illustrative example, we apply the described network approach to investigate the global conformational changes between the closed and partially open states of the SARS-CoV-2 spike protein. These conformational changes in the spike protein is crucial for coronavirus entry and fusion into human cells. Our analysis reveals global structural reorientations between the two states of the spike protein despite small changes between the two states at the backbone level. We also observe some differences at strategic locations in the structures, correlating with their functions, asserting the advantages of the side-chain network analysis. Finally, we present a view of allostery as a subtle synergistic-global change between the ligand and the receptor, the incorporation of which would enhance drug design strategies.
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Affiliation(s)
- Anushka Halder
- Department of Pharmacology, Yale University, New Haven, CT, United States
| | - Arinnia Anto
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Varsha Subramanyan
- Department of Physics, University of Illinois at Urbana-Champaign, Champaign, IL, United States
| | | | - Smitha Vishveshwara
- Department of Physics, University of Illinois at Urbana-Champaign, Champaign, IL, United States
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33
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Topology Results on Adjacent Amino Acid Networks of Oligomeric Proteins. Methods Mol Biol 2020. [PMID: 33315221 DOI: 10.1007/978-1-0716-1154-8_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
In this chapter, we focus on topology measurements of the adjacent amino acid networks for a data set of oligomeric proteins and some of its subnetworks. The aim is to present many mathematical tools in order to understand the structures of proteins implicitly coded in such networks and subnetworks. We mainly investigate four important networks by computing the number of connected components, the degree distribution, and assortativity measures. We compare each result in order to prove that the four networks have quite independent topologies.
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34
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Felline A, Seeber M, Fanelli F. webPSN v2.0: a webserver to infer fingerprints of structural communication in biomacromolecules. Nucleic Acids Res 2020; 48:W94-W103. [PMID: 32427333 PMCID: PMC7319592 DOI: 10.1093/nar/gkaa397] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 04/21/2020] [Accepted: 05/17/2020] [Indexed: 12/25/2022] Open
Abstract
A mixed Protein Structure Network (PSN) and Elastic Network Model-Normal Mode Analysis (ENM-NMA)-based strategy (i.e. PSN-ENM) was developed to investigate structural communication in bio-macromolecules. Protein Structure Graphs (PSGs) are computed on a single structure, whereas information on system dynamics is supplied by ENM-NMA. The approach was implemented in a webserver (webPSN), which was significantly updated herein. The webserver now handles both proteins and nucleic acids and relies on an internal upgradable database of network parameters for ions and small molecules in all PDB structures. Apart from the radical restyle of the server and some changes in the calculation setup, other major novelties concern the possibility to: a) compute the differences in nodes, links, and communication pathways between two structures (i.e. network difference) and b) infer links, hubs, communities, and metapaths from consensus networks computed on a number of structures. These new features are useful to identify commonalties and differences between two different functional states of the same system or structural-communication signatures in homologous or analogous systems. The output analysis relies on 3D-representations, interactive tables and graphs, also available for download. Speed and accuracy make this server suitable to comparatively investigate structural communication in large sets of bio-macromolecular systems. URL: http://webpsn.hpc.unimore.it.
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Affiliation(s)
- Angelo Felline
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena 41125, Italy
| | - Michele Seeber
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena 41125, Italy
| | - Francesca Fanelli
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena 41125, Italy.,Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena 41125, Italy
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35
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Rocha GV, Bastos LS, Costa MGS. Identification of potential allosteric binding sites in cathepsin K based on intramolecular communication. Proteins 2020; 88:1675-1687. [PMID: 32683717 DOI: 10.1002/prot.25985] [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: 01/21/2020] [Revised: 06/02/2020] [Accepted: 07/12/2020] [Indexed: 12/18/2022]
Abstract
Network theory methods and molecular dynamics (MD) simulations are accepted tools to study allosteric regulation. Indeed, dynamic networks built upon correlation analysis of MD trajectories provide detailed information about communication paths between distant sites. In this context, we aimed to understand whether the efficiency of intramolecular communication could be used to predict the allosteric potential of a given site. To this end, we performed MD simulations and network theory analyses in cathepsin K (catK), whose allosteric sites are well defined. To obtain a quantitative measure of the efficiency of communication, we designed a new protocol that enables the comparison between properties related to ensembles of communication paths obtained from different sites. Further, we applied our strategy to evaluate the allosteric potential of different catK cavities not yet considered for drug design. Our predictions of the allosteric potential based on intramolecular communication correlate well with previous catK experimental and theoretical data. We also discuss the possibility of applying our approach to other proteins from the same family.
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Affiliation(s)
- Gisele V Rocha
- Programa de Computação Científica, Vice-Presidência de Educação, Informação e Comunicação, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.,Laboratoire de Biologie et de Pharmacologie Appliquée, Ecole Normale Supérieure Paris Saclay, Centre National de la Recherche Scientifique, Cachan, France
| | - Leonardo S Bastos
- Programa de Computação Científica, Vice-Presidência de Educação, Informação e Comunicação, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.,Department of Infectious Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Mauricio G S Costa
- Programa de Computação Científica, Vice-Presidência de Educação, Informação e Comunicação, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.,Laboratoire de Biologie et de Pharmacologie Appliquée, Ecole Normale Supérieure Paris Saclay, Centre National de la Recherche Scientifique, Cachan, France
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36
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Ng JF, Fraternali F. Understanding the structural details of APOBEC3-DNA interactions using graph-based representations. Curr Res Struct Biol 2020; 2:130-143. [PMID: 34235473 PMCID: PMC8244423 DOI: 10.1016/j.crstbi.2020.07.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 07/17/2020] [Accepted: 07/21/2020] [Indexed: 12/22/2022] Open
Abstract
Human APOBEC3 (A3; apolipoprotein B mRNA editing catalytic polypeptide-like 3) is a family of seven enzymes involved in generating mutations in nascent reverse transcripts of many retroviruses, as well as the human genome in a range of cancer types. The structural details of the interaction between A3 proteins and DNA molecules are only available for a few family members. Here we use homology modelling techniques to address the difference in structural coverage of human A3 enzymes interacting with different DNA substrates. A3-DNA interfaces are represented as residue networks ("graphs"), based on which features at these interfaces are compared and quantified. We demonstrate that graph-based representations are effective in highlighting structural features of A3-DNA interfaces. By large-scale in silico mutagenesis of the bound DNA chain, we predicted the preference of substrate DNA sequence for multiple A3 domains. These data suggested that computational modelling approaches could contribute in the exploration of the structural basis for sequence specificity in A3 substrate selection, and demonstrated the utility of graph-based approaches in evaluating a large number of structural models generated in silico. APOBEC3(A3)-DNA structures have been resolved with modified deaminase domains. Structural modelling of interaction between wild-type A3 domains and DNA substrates. Graph-based representations reveal structural differences across A3-DNA interfaces. Using in silico mutagenesis we compared substrate preference of multiple A3 domains. Graph-based approaches can efficiently compare a large number of structural models.
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37
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Aydınkal RM, Serçinoğlu O, Ozbek P. ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism. Nucleic Acids Res 2020; 47:W471-W476. [PMID: 31114881 PMCID: PMC6602423 DOI: 10.1093/nar/gkz390] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 04/17/2019] [Accepted: 05/09/2019] [Indexed: 01/14/2023] Open
Abstract
ProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.
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Affiliation(s)
- Rasim Murat Aydınkal
- Department of Bioengineering, Faculty of Engineering, Marmara University, Kadikoy, Istanbul 34722, Turkey.,Ali Nihat Gokyigit Foundation, Etiler, Istanbul 34340, Turkey
| | - Onur Serçinoğlu
- Department of Bioengineering, Faculty of Engineering, Marmara University, Kadikoy, Istanbul 34722, Turkey.,Department of Bioengineering, Faculty of Engineering, Recep Tayyip Erdoğan University, Rize 53100, Turkey
| | - Pemra Ozbek
- Department of Bioengineering, Faculty of Engineering, Marmara University, Kadikoy, Istanbul 34722, Turkey
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38
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Bhattacharya S, Xu L, Thompson D. Long-range Regulation of Partially Folded Amyloidogenic Peptides. Sci Rep 2020; 10:7597. [PMID: 32371882 PMCID: PMC7200734 DOI: 10.1038/s41598-020-64303-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 04/15/2020] [Indexed: 01/20/2023] Open
Abstract
Neurodegeneration involves abnormal aggregation of intrinsically disordered amyloidogenic peptides (IDPs), usually mediated by hydrophobic protein-protein interactions. There is mounting evidence that formation of α-helical intermediates is an early event during self-assembly of amyloid-β42 (Aβ42) and α-synuclein (αS) IDPs in Alzheimer’s and Parkinson’s disease pathogenesis, respectively. However, the driving force behind on-pathway molecular assembly of partially folded helical monomers into helical oligomers assembly remains unknown. Here, we employ extensive molecular dynamics simulations to sample the helical conformational sub-spaces of monomeric peptides of both Aβ42 and αS. Our computed free energies, population shifts, and dynamic cross-correlation network analyses reveal a common feature of long-range intra-peptide modulation of partial helical folds of the amyloidogenic central hydrophobic domains via concerted coupling with their charged terminal tails (N-terminus of Aβ42 and C-terminus of αS). The absence of such inter-domain fluctuations in both fully helical and completely unfolded (disordered) states suggests that long-range coupling regulates the dynamicity of partially folded helices, in both Aβ42 and αS peptides. The inter-domain coupling suggests a form of intra-molecular allosteric regulation of the aggregation trigger in partially folded helical monomers. This approach could be applied to study the broad range of amyloidogenic peptides, which could provide a new path to curbing pathogenic aggregation of partially folded conformers into oligomers, by inhibition of sites far from the hydrophobic core.
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Affiliation(s)
- Shayon Bhattacharya
- Department of Physics, Bernal Institute, University of Limerick, Limerick, V94 T9PX, Ireland
| | - Liang Xu
- Department of Physics, Bernal Institute, University of Limerick, Limerick, V94 T9PX, Ireland
| | - Damien Thompson
- Department of Physics, Bernal Institute, University of Limerick, Limerick, V94 T9PX, Ireland.
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39
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Minicozzi V, Di Venere A, Nicolai E, Giuliani A, Caccuri AM, Di Paola L, Mei G. Non-symmetrical structural behavior of a symmetric protein: the case of homo-trimeric TRAF2 (tumor necrosis factor-receptor associated factor 2). J Biomol Struct Dyn 2020; 39:319-329. [PMID: 31980009 DOI: 10.1080/07391102.2020.1719202] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The oligomeric state of TRAF2 (tumor necrosis factor-receptor associated factor 2), a TNF (tumor necrosis factor) receptor-associated factor, is crucial for membrane binding and probably plays a fundamental role in regulating the protein function in vivo. In this study we have combined molecular dynamics with the protein contact network approach to characterize the interaction of the three identical subunits of TRAF2. The average structure obtained after a 225 ns simulation reveals that two clusters of different size are formed, one of which includes almost completely two subunits, while the third monomer appears to be more independent. This picture is also confirmed by the estimated average number of inter-subunit contacts and by the comparison of side chains mobility in each monomer. The analysis of equilibrium pressure-induced dissociation measurements supports such findings, indicating that the dimeric-monomeric (2 + 1) might be prevalent with respect to the trimeric configuration, especially in the case of more diluted samples. These findings suggest that the formation of monomeric species, which is crucial for the formation of intra-luminal vesicles, might depend on preferential asymmetric interactions among the three subunits.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Velia Minicozzi
- INFN and Department of Physics, University of Rome Tor Vergata, Rome, Italy
| | - Almerinda Di Venere
- Department of Experimental Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Eleonora Nicolai
- Department of Experimental Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Alessandro Giuliani
- Department of Environment and Health, Istituto Superiore di Sanità, Rome, Italy
| | | | - Luisa Di Paola
- Department of Engineering, Unit of Chemical-Physics Fundamentals in Chemical Engineering, Università Campus Bio-Medico of Rome, Rome, Italy
| | - Giampiero Mei
- Department of Experimental Medicine, University of Rome Tor Vergata, Rome, Italy
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40
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Heifetz A, Sladek V, Townsend-Nicholson A, Fedorov DG. Characterizing Protein-Protein Interactions with the Fragment Molecular Orbital Method. Methods Mol Biol 2020; 2114:187-205. [PMID: 32016895 DOI: 10.1007/978-1-0716-0282-9_13] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Proteins are vital components of living systems, serving as building blocks, molecular machines, enzymes, receptors, ion channels, sensors, and transporters. Protein-protein interactions (PPIs) are a key part of their function. There are more than 645,000 reported disease-relevant PPIs in the human interactome, but drugs have been developed for only 2% of these targets. The advances in PPI-focused drug discovery are highly dependent on the availability of structural data and accurate computational tools for analysis of this data. Quantum mechanical approaches are often too expensive computationally, but the fragment molecular orbital (FMO) method offers an excellent solution that combines accuracy, speed and the ability to reveal key interactions that would otherwise be hard to detect. FMO provides essential information for PPI drug discovery, namely, identification of key interactions formed between residues of two proteins, including their strength (in kcal/mol) and their chemical nature (electrostatic or hydrophobic). In this chapter, we have demonstrated how three different FMO-based approaches (pair interaction energy analysis (PIE analysis), subsystem analysis (SA) and analysis of protein residue networks (PRNs)) have been applied to study PPI in three protein-protein complexes.
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Affiliation(s)
| | - Vladimir Sladek
- Institute of Chemistry, Centre for Glycomics, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Andrea Townsend-Nicholson
- Research Department of Structural & Molecular Biology, Division of Biosciences, University College London, London, UK
| | - Dmitri G Fedorov
- Research Center for Computational Design of Advanced Functional Materials (CD-FMat), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan.
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41
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Kønig SM, Rissler V, Terkelsen T, Lambrughi M, Papaleo E. Alterations of the interactome of Bcl-2 proteins in breast cancer at the transcriptional, mutational and structural level. PLoS Comput Biol 2019; 15:e1007485. [PMID: 31825969 PMCID: PMC6927658 DOI: 10.1371/journal.pcbi.1007485] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 12/23/2019] [Accepted: 10/12/2019] [Indexed: 12/11/2022] Open
Abstract
Apoptosis is an essential defensive mechanism against tumorigenesis. Proteins of the B-cell lymphoma-2 (Bcl-2) family regulate programmed cell death by the mitochondrial apoptosis pathway. In response to intracellular stress, the apoptotic balance is governed by interactions of three distinct subgroups of proteins; the activator/sensitizer BH3 (Bcl-2 homology 3)-only proteins, the pro-survival, and the pro-apoptotic executioner proteins. Changes in expression levels, stability, and functional impairment of pro-survival proteins can lead to an imbalance in tissue homeostasis. Their overexpression or hyperactivation can result in oncogenic effects. Pro-survival Bcl-2 family members carry out their function by binding the BH3 short linear motif of pro-apoptotic proteins in a modular way, creating a complex network of protein-protein interactions. Their dysfunction enables cancer cells to evade cell death. The critical role of Bcl-2 proteins in homeostasis and tumorigenesis, coupled with mounting insight in their structural properties, make them therapeutic targets of interest. A better understanding of gene expression, mutational profile, and molecular mechanisms of pro-survival Bcl-2 proteins in different cancer types, could help to clarify their role in cancer development and may guide advancement in drug discovery. Here, we shed light on the pro-survival Bcl-2 proteins in breast cancer using different bioinformatic approaches, linking -omics with structural data. We analyzed the changes in the expression of the Bcl-2 proteins and their BH3-containing interactors in breast cancer samples. We then studied, at the structural level, a selection of interactions, accounting for effects induced by mutations found in the breast cancer samples. We find two complexes between the up-regulated Bcl2A1 and two down-regulated BH3-only candidates (i.e., Hrk and Nr4a1) as targets associated with reduced apoptosis in breast cancer samples for future experimental validation. Furthermore, we predict L99R, M75R as damaging mutations altering protein stability, and Y120C as a possible allosteric mutation from an exposed surface to the BH3-binding site.
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Affiliation(s)
- Simon Mathis Kønig
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Vendela Rissler
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Thilde Terkelsen
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Matteo Lambrughi
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Elena Papaleo
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
- Translational Disease Systems Biology, Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research University of Copenhagen, Copenhagen, Denmark
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42
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Gaiha GD, Rossin EJ, Urbach J, Landeros C, Collins DR, Nwonu C, Muzhingi I, Anahtar MN, Waring OM, Piechocka-Trocha A, Waring M, Worrall DP, Ghebremichael MS, Newman RM, Power KA, Allen TM, Chodosh J, Walker BD. Structural topology defines protective CD8 + T cell epitopes in the HIV proteome. Science 2019; 364:480-484. [PMID: 31048489 DOI: 10.1126/science.aav5095] [Citation(s) in RCA: 91] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2018] [Accepted: 03/25/2019] [Indexed: 12/26/2022]
Abstract
Mutationally constrained epitopes of variable pathogens represent promising targets for vaccine design but are not reliably identified by sequence conservation. In this study, we employed structure-based network analysis, which applies network theory to HIV protein structure data to quantitate the topological importance of individual amino acid residues. Mutation of residues at important network positions disproportionately impaired viral replication and occurred with high frequency in epitopes presented by protective human leukocyte antigen (HLA) class I alleles. Moreover, CD8+ T cell targeting of highly networked epitopes distinguished individuals who naturally control HIV, even in the absence of protective HLA alleles. This approach thereby provides a mechanistic basis for immune control and a means to identify CD8+ T cell epitopes of topological importance for rational immunogen design, including a T cell-based HIV vaccine.
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Affiliation(s)
- Gaurav D Gaiha
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA.,Gastrointestinal Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Elizabeth J Rossin
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA 02114, USA
| | - Jonathan Urbach
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | | | - David R Collins
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA.,Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Chioma Nwonu
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Itai Muzhingi
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Melis N Anahtar
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA.,Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Olivia M Waring
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA.,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Alicja Piechocka-Trocha
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA.,Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Michael Waring
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA.,Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Daniel P Worrall
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | | | - Ruchi M Newman
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Karen A Power
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Todd M Allen
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - James Chodosh
- Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA 02114, USA
| | - Bruce D Walker
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA. .,The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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43
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Yao XQ, Momin M, Hamelberg D. Establishing a Framework of Using Residue–Residue Interactions in Protein Difference Network Analysis. J Chem Inf Model 2019; 59:3222-3228. [DOI: 10.1021/acs.jcim.9b00320] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Xin-Qiu Yao
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30302-3965, United States
| | - Mohamed Momin
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30302-3965, United States
| | - Donald Hamelberg
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30302-3965, United States
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44
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Alvarez-Paggi D, Lorenzo JR, Camporeale G, Montero L, Sánchez IE, de Prat Gay G, Alonso LG. Topology Dictates Evolution of Regulatory Cysteines in a Family of Viral Oncoproteins. Mol Biol Evol 2019; 36:1521-1532. [DOI: 10.1093/molbev/msz085] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Affiliation(s)
| | - Juan Ramiro Lorenzo
- Universidad de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas, Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Facultad de Ciencias Exactas y Naturales, Laboratorio de Fisiología de Proteínas, Buenos Aires, Argentina
| | - Gabriela Camporeale
- Protein Structure-Function and Engineering Laboratory, Fundación Instituto Leloir and IIBBA-CONICET, Buenos Aires, Argentina
| | - Luciano Montero
- Protein Structure-Function and Engineering Laboratory, Fundación Instituto Leloir and IIBBA-CONICET, Buenos Aires, Argentina
| | - Ignacio E Sánchez
- Universidad de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas, Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Facultad de Ciencias Exactas y Naturales, Laboratorio de Fisiología de Proteínas, Buenos Aires, Argentina
| | - Gonzalo de Prat Gay
- Protein Structure-Function and Engineering Laboratory, Fundación Instituto Leloir and IIBBA-CONICET, Buenos Aires, Argentina
| | - Leonardo G Alonso
- Instituto de Nanobiotecnología (NANOBIOTEC), CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina
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45
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Neelamraju S, Wales DJ, Gosavi S. Go-Kit: A Tool To Enable Energy Landscape Exploration of Proteins. J Chem Inf Model 2019; 59:1703-1708. [PMID: 30977648 DOI: 10.1021/acs.jcim.9b00007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Coarse-grained Go̅-like models, based on the principle of minimal frustration, provide valuable insight into fundamental questions in the field of protein folding and dynamics. In conjunction with commonly used molecular dynamics (MD) simulations, energy landscape exploration methods like discrete path sampling (DPS) with Go̅-like models can provide quantitative details of the thermodynamics and kinetics of proteins. Here we present Go-kit, a software that facilitates the setup of MD and DPS simulations of several flavors of Go̅-like models. Go-kit is designed for use with MD (GROMACS) and DPS (PATHSAMPLE) simulation engines that are open source. The Go-kit code is written in python2.7 and is also open source. A case study for the ribosomal protein S6 is discussed to illustrate the utility of the software, which is available at https://github.com/gokit1/gokit .
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Affiliation(s)
- Sridhar Neelamraju
- Simons Centre for the Study of Living Machines , National Centre for Biological Sciences, Tata Institute of Fundamental Research , Bellary Road , Bangalore 560065 , India.,University Chemical Laboratories , University of Cambridge , Lensfield Road , Cambridge CB2 1EW , U.K
| | - David J Wales
- University Chemical Laboratories , University of Cambridge , Lensfield Road , Cambridge CB2 1EW , U.K
| | - Shachi Gosavi
- Simons Centre for the Study of Living Machines , National Centre for Biological Sciences, Tata Institute of Fundamental Research , Bellary Road , Bangalore 560065 , India
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46
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Medina E, Villalobos P, Coñuecar R, Ramírez-Sarmiento CA, Babul J. The protonation state of an evolutionarily conserved histidine modulates domainswapping stability of FoxP1. Sci Rep 2019; 9:5441. [PMID: 30931977 PMCID: PMC6443806 DOI: 10.1038/s41598-019-41819-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 03/13/2019] [Indexed: 11/12/2022] Open
Abstract
Forkhead box P (FoxP) proteins are members of the versatile Fox transcription factors, which control the timing and expression of multiple genes for eukaryotic cell homeostasis. Compared to other Fox proteins, they can form domain-swapped dimers through their DNA-binding –forkhead– domains, enabling spatial reorganization of distant chromosome elements by tethering two DNA molecules together. Yet, domain swapping stability and DNA binding affinity varies between different FoxP proteins. Experimental evidence suggests that the protonation state of a histidine residue conserved in all Fox proteins is responsible for pH-dependent modulation of these interactions. Here, we explore the consequences of the protonation state of another histidine (H59), only conserved within FoxM/O/P subfamilies, on folding and dimerization of the forkhead domain of human FoxP1. Dimer dissociation kinetics and equilibrium unfolding experiments demonstrate that protonation of H59 leads to destabilization of the domain-swapped dimer due to an increase in free energy difference between the monomeric and transition states. This pH–dependence is abolished when H59 is mutated to alanine. Furthermore, anisotropy measurements and molecular dynamics evidence that H59 has a direct impact in the local stability of helix H3. Altogether, our results highlight the relevance of H59 in domain swapping and folding stability of FoxP1.
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Affiliation(s)
- Exequiel Medina
- Departamento de Biología, Facultad de Ciencias, Universidad de Chile, Las Palmeras 3425, Casilla 653, Santiago, 7800003, Chile
| | - Pablo Villalobos
- Departamento de Biología, Facultad de Ciencias, Universidad de Chile, Las Palmeras 3425, Casilla 653, Santiago, 7800003, Chile
| | - Ricardo Coñuecar
- Departamento de Biología, Facultad de Ciencias, Universidad de Chile, Las Palmeras 3425, Casilla 653, Santiago, 7800003, Chile
| | - César A Ramírez-Sarmiento
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Santiago, 7820436, Chile.
| | - Jorge Babul
- Departamento de Biología, Facultad de Ciencias, Universidad de Chile, Las Palmeras 3425, Casilla 653, Santiago, 7800003, Chile.
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47
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Liang Z, Verkhivker GM, Hu G. Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications. Brief Bioinform 2019; 21:815-835. [DOI: 10.1093/bib/bbz029] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 02/04/2019] [Accepted: 02/21/2019] [Indexed: 12/24/2022] Open
Abstract
Abstract
Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. In parallel, bioinformatics and systems biology approaches including genomic analysis, coevolution and network-based modeling have provided an array of powerful tools that complemented and enriched biophysical insights by enabling high-throughput analysis of biological data and dissection of global molecular signatures underlying mechanisms of protein function and interactions in the cellular environment. These developments have provided a powerful interdisciplinary framework for quantifying the relationships between protein dynamics and allosteric regulation, allowing for high-throughput modeling and engineering of molecular mechanisms. Here, we review fundamental advances in protein dynamics, network theory and coevolutionary analysis that have provided foundation for rapidly growing computational tools for modeling of allosteric regulation. We discuss recent developments in these interdisciplinary areas bridging computational biophysics and network biology, focusing on promising applications in allosteric regulations, including the investigation of allosteric communication pathways, protein–DNA/RNA interactions and disease mutations in genomic medicine. We conclude by formulating and discussing future directions and potential challenges facing quantitative computational investigations of allosteric regulatory mechanisms in protein systems.
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Affiliation(s)
- Zhongjie Liang
- School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Gennady M Verkhivker
- Department of Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA, USA
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, USA
| | - Guang Hu
- School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
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48
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Rosa AC, Benetti E, Gallicchio M, Boscaro V, Cangemi L, Dianzani C, Miglio G. Analyzing Cysteine Site Neighbors in Proteins to Reveal Dimethyl Fumarate Targets. Proteomics 2019; 19:e1800301. [DOI: 10.1002/pmic.201800301] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 12/10/2018] [Indexed: 12/24/2022]
Affiliation(s)
- Arianna Carolina Rosa
- Dipartimento di Scienza e Tecnologia del Farmaco; Università degli Studi di Torino; Turin 10125 Italy
| | - Elisa Benetti
- Dipartimento di Scienza e Tecnologia del Farmaco; Università degli Studi di Torino; Turin 10125 Italy
| | - Margherita Gallicchio
- Dipartimento di Scienza e Tecnologia del Farmaco; Università degli Studi di Torino; Turin 10125 Italy
| | - Valentina Boscaro
- Dipartimento di Scienza e Tecnologia del Farmaco; Università degli Studi di Torino; Turin 10125 Italy
| | - Luigi Cangemi
- Dipartimento di Scienza e Tecnologia del Farmaco; Università degli Studi di Torino; Turin 10125 Italy
| | - Chiara Dianzani
- Dipartimento di Scienza e Tecnologia del Farmaco; Università degli Studi di Torino; Turin 10125 Italy
| | - Gianluca Miglio
- Dipartimento di Scienza e Tecnologia del Farmaco; Università degli Studi di Torino; Turin 10125 Italy
- Centro di Competenza sul Calcolo Scientifico C S; Università degli Studi di Torino; Turin 10125 Italy
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49
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Lambrughi M, Tiberti M, Allega MF, Sora V, Nygaard M, Toth A, Salamanca Viloria J, Bignon E, Papaleo E. Analyzing Biomolecular Ensembles. Methods Mol Biol 2019; 2022:415-451. [PMID: 31396914 DOI: 10.1007/978-1-4939-9608-7_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Several techniques are available to generate conformational ensembles of proteins and other biomolecules either experimentally or computationally. These methods produce a large amount of data that need to be analyzed to identify structure-dynamics-function relationship. In this chapter, we will cover different tools to unveil the information hidden in conformational ensemble data and to guide toward the rationalization of the data. We included routinely used approaches such as dimensionality reduction, as well as new methods inspired by high-order statistics and graph theory.
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Affiliation(s)
- Matteo Lambrughi
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Matteo Tiberti
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Maria Francesca Allega
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Valentina Sora
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Mads Nygaard
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Agota Toth
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Juan Salamanca Viloria
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Emmanuelle Bignon
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Elena Papaleo
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark.
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50
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Sladek V, Tokiwa H, Shimano H, Shigeta Y. Protein Residue Networks from Energetic and Geometric Data: Are They Identical? J Chem Theory Comput 2018; 14:6623-6631. [PMID: 30500196 DOI: 10.1021/acs.jctc.8b00733] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Protein residue networks (PRN) from energetic and geometric data are probably not identical. PRNs constructed from ab initio pair interaction energies are analyzed for the first time and compared to PRN based on center of mass separation. We use modern, previously unused algorithms such as global and local efficiencies to quantitatively confirm that both types of PRNs do exhibit small-world character. The main novelty finding is that interaction energy-based PRNs preserve small-world character even when clustered. A node hierarchy independent of the cutoff energy used for the edge creation is characteristic for them. Efficiency centrality identifies hubs responsible for such behavior. The interaction energy-based PRNs seem to comply with the scale-free network model with respect to efficiency centrality distribution as opposed to distance based PRNs. Community detection is introduced into protein network research as an extension beyond cluster analysis to study tertiary and quaternary structures.
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Affiliation(s)
- Vladimir Sladek
- Institute of Chemistry - Centre for Glycomics , Dubravska cesta 9 , 84538 Bratislava , Slovakia.,Agency for Medical Research and Development (AMED) , Chiyoda-ku , Japan
| | - Hiroaki Tokiwa
- Agency for Medical Research and Development (AMED) , Chiyoda-ku , Japan.,Department of Chemistry , Rikkyo University , Nishi-Ikebukuro , Toshima, Tokyo 171-8501 , Japan
| | - Hitoshi Shimano
- Agency for Medical Research and Development (AMED) , Chiyoda-ku , Japan.,Department of Internal Medicine, Faculty of Medicine , University of Tsukuba , 1-1-1 Tennodai , Tsukuba, Ibaraki 305-8575 , Japan
| | - Yasuteru Shigeta
- Center for Computational Sciences , University of Tsukuba , Tennodai 1-1-1 , Tsukuba, Ibaraki 305-8577 , Japan
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