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Ventouri IK, Loeber S, Somsen GW, Schoenmakers PJ, Astefanei A. Field-flow fractionation for molecular-interaction studies of labile and complex systems: A critical review. Anal Chim Acta 2022; 1193:339396. [DOI: 10.1016/j.aca.2021.339396] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 11/11/2021] [Accepted: 12/22/2021] [Indexed: 12/11/2022]
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2
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Meseguer A, Bota P, Fernández-Fuentes N, Oliva B. Prediction of Protein-Protein Binding Affinities from Unbound Protein Structures. Methods Mol Biol 2022; 2385:335-351. [PMID: 34888728 DOI: 10.1007/978-1-0716-1767-0_16] [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: 06/13/2023]
Abstract
Proteins are the workhorses of cells to carry out sophisticated and complex cellular processes. Such processes require a coordinated and regulated interactions between proteins that are both time and location specific. The strength, or binding affinity, of protein-protein interactions ranges between the micro- and the nanomolar association constant, often dictating the molecular mechanisms underlying the interaction and the longevity of the complex, i.e., transient or permanent. In consequence, there is a need to quantify the strength of protein-protein interactions for biological, biomedical, and biotechnological applications. While experimental methods are labor intensive and costly, computational ones are useful tools to predict the affinity of protein-protein interactions. In this chapter, we review the methods developed by us to address this question. We briefly present two methods to comprehend the structure of the protein complex derived by either comparative modeling or docking. Then we introduce BADOCK, a method to predict the binding energy without requiring the structure of the protein complex, thus overcoming one of the major limitations of structure-based methods for the prediction of binding affinity. BADOCK utilizes the structure of unbound proteins and the protein docking sampling space to predict protein-protein binding affinities. We present step-by-step protocols to utilize these methods, describing the inputs and potential pitfalls as well as their respective strengths and limitations.
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Affiliation(s)
- Alberto Meseguer
- Structural Bioinformatics Lab (GRIB-IMIM), Department of Experimental and Health Science, University Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Patricia Bota
- Structural Bioinformatics Lab (GRIB-IMIM), Department of Experimental and Health Science, University Pompeu Fabra, Barcelona, Catalonia, Spain
- Department of Biosciences, U Science Tech, Universitat de Vic-Universitat Central de Catalunya, Catalonia, Spain
| | - Narcis Fernández-Fuentes
- Department of Biosciences, U Science Tech, Universitat de Vic-Universitat Central de Catalunya, Catalonia, Spain
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - Baldo Oliva
- Structural Bioinformatics Lab (GRIB-IMIM), Department of Experimental and Health Science, University Pompeu Fabra, Barcelona, Catalonia, Spain.
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3
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Shringari SR, Giannakoulias S, Ferrie JJ, Petersson EJ. Rosetta custom score functions accurately predict ΔΔG of mutations at protein-protein interfaces using machine learning. Chem Commun (Camb) 2020; 56:6774-6777. [PMID: 32441721 DOI: 10.1039/d0cc01959c] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Protein-protein interfaces play essential roles in a variety of biological processes and many therapeutic molecules are targeted at these interfaces. However, accurate predictions of the effects of interfacial mutations to identify "hotspots" have remained elusive despite the myriad of modeling and machine learning methods tested. Here, for the first time, we demonstrate that nonlinear reweighting of energy terms from Rosetta, through the use of machine learning, exhibits improved predictability of ΔΔG values associated with interfacial mutations.
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Affiliation(s)
- Sumant R Shringari
- Department of Chemistry, University of Pennsylvania, 231 South 34th Street, Philadelphia, PA 19104, USA.
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4
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Kwon D, Lee D, Kim J, Lee J, Sim M, Kim J. Using INTERSPIA to Explore the Dynamics of Protein-Protein Interactions Among Multiple Species. ACTA ACUST UNITED AC 2019; 68:e88. [PMID: 31751498 DOI: 10.1002/cpbi.88] [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: 11/12/2022]
Abstract
INTER-Species Protein Interaction Analysis (INTERSPIA) is a web application for identifying diverse patterns of protein-protein interactions (PPIs) in different species. Given a set of proteins of interest to the user, INTERSPIA first discovers additional proteins that are functionally associated with the input proteins as well as different or common patterns of PPIs among the proteins in multiple species through a server-side pipeline. Second, it visualizes the dynamics of PPIs in multiple species via an easy-to-use web interface. This article contains a basic protocol describing how to visualize diverse patterns of PPIs of input proteins in multiple species, and how to use them for functional analysis in the web interface. INTERSPIA is freely available at http://bioinfo.konkuk.ac.kr/INTERSPIA/. © 2019 by John Wiley & Sons, Inc. Basic Protocol: Running INTERSPIA using a list of input proteins.
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Affiliation(s)
- Daehong Kwon
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, Republic of Korea
| | - Daehwan Lee
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, Republic of Korea
| | - Juyeon Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, Republic of Korea
| | - Jongin Lee
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, Republic of Korea
| | - Mikang Sim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, Republic of Korea
| | - Jaebum Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, Republic of Korea
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5
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Kwon D, Lee D, Kim J, Lee J, Sim M, Kim J. INTERSPIA: a web application for exploring the dynamics of protein-protein interactions among multiple species. Nucleic Acids Res 2019; 46:W89-W94. [PMID: 29746660 PMCID: PMC6031021 DOI: 10.1093/nar/gky378] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 04/27/2018] [Indexed: 02/06/2023] Open
Abstract
Proteins perform biological functions through cascading interactions with each other by forming protein complexes. As a result, interactions among proteins, called protein-protein interactions (PPIs) are not completely free from selection constraint during evolution. Therefore, the identification and analysis of PPI changes during evolution can give us new insight into the evolution of functions. Although many algorithms, databases and websites have been developed to help the study of PPIs, most of them are limited to visualize the structure and features of PPIs in a chosen single species with limited functions in the visualization perspective. This leads to difficulties in the identification of different patterns of PPIs in different species and their functional consequences. To resolve these issues, we developed a web application, called INTER-Species Protein Interaction Analysis (INTERSPIA). Given a set of proteins of user's interest, INTERSPIA first discovers additional proteins that are functionally associated with the input proteins and searches for different patterns of PPIs in multiple species through a server-side pipeline, and second visualizes the dynamics of PPIs in multiple species using an easy-to-use web interface. INTERSPIA is freely available at http://bioinfo.konkuk.ac.kr/INTERSPIA/.
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Affiliation(s)
- Daehong Kwon
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Daehwan Lee
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Juyeon Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Jongin Lee
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Mikang Sim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Jaebum Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
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6
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Marín-López MA, Planas-Iglesias J, Aguirre-Plans J, Bonet J, Garcia-Garcia J, Fernandez-Fuentes N, Oliva B. On the mechanisms of protein interactions: predicting their affinity from unbound tertiary structures. Bioinformatics 2018; 34:592-598. [PMID: 29028891 PMCID: PMC5860604 DOI: 10.1093/bioinformatics/btx616] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Accepted: 09/26/2017] [Indexed: 12/12/2022] Open
Abstract
Motivation The characterization of the protein–protein association mechanisms is crucial to understanding how biological processes occur. It has been previously shown that the early formation of non-specific encounters enhances the realization of the stereospecific (i.e. native) complex by reducing the dimensionality of the search process. The association rate for the formation of such complex plays a crucial role in the cell biology and depends on how the partners diffuse to be close to each other. Predicting the binding free energy of proteins provides new opportunities to modulate and control protein–protein interactions. However, existing methods require the 3D structure of the complex to predict its affinity, severely limiting their application to interactions with known structures. Results We present a new approach that relies on the unbound protein structures and protein docking to predict protein–protein binding affinities. Through the study of the docking space (i.e. decoys), the method predicts the binding affinity of the query proteins when the actual structure of the complex itself is unknown. We tested our approach on a set of globular and soluble proteins of the newest affinity benchmark, obtaining accuracy values comparable to other state-of-art methods: a 0.4 correlation coefficient between the experimental and predicted values of ΔG and an error < 3 Kcal/mol. Availability and implementation The binding affinity predictor is implemented and available at http://sbi.upf.edu/BADock and https://github.com/badocksbi/BADock. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Manuel Alejandro Marín-López
- Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, Barcelona 08003, Spain
| | - Joan Planas-Iglesias
- Division of Metabolic and Vascular Health, University of Warwick, Coventry CV4?7AL, UK
| | - Joaquim Aguirre-Plans
- Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, Barcelona 08003, Spain
| | - Jaume Bonet
- Laboratory of Protein Design and Immunoenginneering, School of Engineering, Ecole Polytechnique Federale de Lausanne, Lausanne 1015, Switzerland
| | - Javier Garcia-Garcia
- Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, Barcelona 08003, Spain
| | - Narcis Fernandez-Fuentes
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23?3DA, UK
| | - Baldo Oliva
- Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, Barcelona 08003, Spain
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7
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Rosetta FunFolDes - A general framework for the computational design of functional proteins. PLoS Comput Biol 2018; 14:e1006623. [PMID: 30452434 PMCID: PMC6277116 DOI: 10.1371/journal.pcbi.1006623] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 12/03/2018] [Accepted: 11/06/2018] [Indexed: 01/11/2023] Open
Abstract
The robust computational design of functional proteins has the potential to deeply impact translational research and broaden our understanding of the determinants of protein function and stability. The low success rates of computational design protocols and the extensive in vitro optimization often required, highlight the challenge of designing proteins that perform essential biochemical functions, such as binding or catalysis. One of the most simplistic approaches for the design of function is to adopt functional motifs in naturally occurring proteins and transplant them to computationally designed proteins. The structural complexity of the functional motif largely determines how readily one can find host protein structures that are "designable", meaning that are likely to present the functional motif in the desired conformation. One promising route to enhance the "designability" of protein structures is to allow backbone flexibility. Here, we present a computational approach that couples conformational folding with sequence design to embed functional motifs into heterologous proteins-Rosetta Functional Folding and Design (FunFolDes). We performed extensive computational benchmarks, where we observed that the enforcement of functional requirements resulted in designs distant from the global energetic minimum of the protein. An observation consistent with several experimental studies that have revealed function-stability tradeoffs. To test the design capabilities of FunFolDes we transplanted two viral epitopes into distant structural templates including one de novo "functionless" fold, which represent two typical challenges where the designability problem arises. The designed proteins were experimentally characterized showing high binding affinities to monoclonal antibodies, making them valuable candidates for vaccine design endeavors. Overall, we present an accessible strategy to repurpose old protein folds for new functions. This may lead to important improvements on the computational design of proteins, with structurally complex functional sites, that can perform elaborate biochemical functions related to binding and catalysis.
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8
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Modell AE, Blosser SL, Arora PS. Systematic Targeting of Protein-Protein Interactions. Trends Pharmacol Sci 2016; 37:702-713. [PMID: 27267699 DOI: 10.1016/j.tips.2016.05.008] [Citation(s) in RCA: 109] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Revised: 05/14/2016] [Accepted: 05/16/2016] [Indexed: 12/22/2022]
Abstract
Over the past decade, protein-protein interactions (PPIs) have gone from being neglected as 'undruggable' to being considered attractive targets for the development of therapeutics. Recent advances in computational analysis, fragment-based screening, and molecular design have revealed promising strategies to address the basic molecular recognition challenge: how to target large protein surfaces with specificity. Several systematic and complementary workflows have been developed to yield successful inhibitors of PPIs. Here we review the major contemporary approaches utilized for the discovery of inhibitors and focus on a structure-based workflow, from the selection of a biological target to design.
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Affiliation(s)
- Ashley E Modell
- Department of Chemistry, New York University, New York, NY 10003, USA
| | - Sarah L Blosser
- Department of Chemistry, New York University, New York, NY 10003, USA
| | - Paramjit S Arora
- Department of Chemistry, New York University, New York, NY 10003, USA
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9
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Rohr RP, Naisbit RE, Mazza C, Bersier LF. Matching-centrality decomposition and the forecasting of new links in networks. Proc Biol Sci 2016; 283:20152702. [PMID: 26842568 PMCID: PMC4760172 DOI: 10.1098/rspb.2015.2702] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Accepted: 01/08/2016] [Indexed: 01/27/2023] Open
Abstract
Networks play a prominent role in the study of complex systems of interacting entities in biology, sociology, and economics. Despite this diversity, we demonstrate here that a statistical model decomposing networks into matching and centrality components provides a comprehensive and unifying quantification of their architecture. The matching term quantifies the assortative structure in which node makes links with which other node, whereas the centrality term quantifies the number of links that nodes make. We show, for a diverse set of networks, that this decomposition can provide a tight fit to observed networks. Then we provide three applications. First, we show that the model allows very accurate prediction of missing links in partially known networks. Second, when node characteristics are known, we show how the matching-centrality decomposition can be related to this external information. Consequently, it offers us a simple and versatile tool to explore how node characteristics explain network architecture. Finally, we demonstrate the efficiency and flexibility of the model to forecast the links that a novel node would create if it were to join an existing network.
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Affiliation(s)
- Rudolf P Rohr
- Department of Biology-Ecology and Evolution, University of Fribourg, Chemin du Musée 10, Fribourg 1700, Switzerland Integrative Ecology Group, Estación Biológica de Doñana, EBD-CSIC, Calle Américo Vespucio s/n, Sevilla 41092, Spain
| | - Russell E Naisbit
- Department of Biology-Ecology and Evolution, University of Fribourg, Chemin du Musée 10, Fribourg 1700, Switzerland
| | - Christian Mazza
- Department of Mathematics, University of Fribourg, Chemin du Musée 23, Fribourg 1700, Switzerland
| | - Louis-Félix Bersier
- Department of Biology-Ecology and Evolution, University of Fribourg, Chemin du Musée 10, Fribourg 1700, Switzerland
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10
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Li H, Huang Y, Yu Y, Li G, Karamanos Y. Self-Catalyzed Assembly of Peptide Scaffolded Nanozyme as a Dynamic Biosensing System. ACS APPLIED MATERIALS & INTERFACES 2016; 8:2833-2839. [PMID: 26752458 DOI: 10.1021/acsami.5b11567] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In this work, a new strategy of biosensor design is developed based on the assembly of amyloid beta and its multiple interactions with other bioactive species. These interactions can enable amyloid beta peptide as a multifunctional sensing element, so the immobilization of sensing probe and the step-by-step modification of the sensing interface have all been dispensed with. Instead, the kinetics of the assembly of a peptide-based catalytic network serves to convert the quantity of analyte into amplified signal readout. The designed dynamic assembling and biosensing system has also been successfully applied in detecting the activity of polyglutamylation, an essential post translation modification controlling cell skeleton and cell cycle, in biological complex samples. Further studies reveal that the serum abundance of a polyglutamylase, tubulin tyrosine ligase-like protein 12, may show parallel with the degree of development of prostate cancer and the discrimination between early cancerous development and benign conditions. And the obtained result is more distinct than that based on PSA detection, the current gold standard. This study may also point to the prospective of extending this design strategy to broader range of biosensing applications in the future.
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Affiliation(s)
- Hao Li
- State Key Laboratory of Pharmaceutical Biotechnology and Collaborative Innovation Center of Chemistry for Life Sciences, Department of Biochemistry, Nanjing University , Nanjing 210093, China
| | - Yue Huang
- State Key Laboratory of Pharmaceutical Biotechnology and Collaborative Innovation Center of Chemistry for Life Sciences, Department of Biochemistry, Nanjing University , Nanjing 210093, China
| | - Yue Yu
- Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School , Nanjing 210008, China
| | - Genxi Li
- State Key Laboratory of Pharmaceutical Biotechnology and Collaborative Innovation Center of Chemistry for Life Sciences, Department of Biochemistry, Nanjing University , Nanjing 210093, China
- Laboratory of Biosensing Technology, School of Life Sciences, Shanghai University , Shanghai 200444, China
| | - Yannis Karamanos
- Laboratoire de la Barrière Hémato-encéphalique, Faculté des Sciences, Université d'Artois , rue Souvraz SP18, 62307 Lens Cedex, France
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Abstract
The past decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information and to analyze this information so as to infer both the functions of individual molecules and how they interact to modulate the behavior of biological systems. Here, we review these techniques, focusing on the construction of physical protein-protein interaction networks, and highlighting approaches that incorporate protein structure, which is becoming an increasingly important component of systems-level computational techniques. We also discuss how network analyses are being applied to enhance our basic understanding of biological systems and their disregulation, as well as how these networks are being used in drug development.
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Affiliation(s)
- Donald Petrey
- Center for Computational Biology and Bioinformatics, Department of Systems Biology
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12
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Protein-protein interactions prediction based on iterative clique extension with gene ontology filtering. ScientificWorldJournal 2014; 2014:523634. [PMID: 24578640 PMCID: PMC3919085 DOI: 10.1155/2014/523634] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2013] [Accepted: 11/05/2013] [Indexed: 01/11/2023] Open
Abstract
Cliques (maximal complete subnets) in protein-protein interaction (PPI) network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO) annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP) and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.
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Medina-Franco JL, Méndez-Lucio O, Martinez-Mayorga K. The Interplay Between Molecular Modeling and Chemoinformatics to Characterize Protein–Ligand and Protein–Protein Interactions Landscapes for Drug Discovery. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2014; 96:1-37. [DOI: 10.1016/bs.apcsb.2014.06.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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14
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On the use of knowledge-based potentials for the evaluation of models of protein-protein, protein-DNA, and protein-RNA interactions. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2014; 94:77-120. [PMID: 24629186 DOI: 10.1016/b978-0-12-800168-4.00004-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Proteins are the bricks and mortar of cells, playing structural and functional roles. In order to perform their function, they interact with each other as well as with other biomolecules such as DNA or RNA. Therefore, to fathom the function of a protein, we require knowing its partners and the atomic details of its interactions (i.e., the structure of the complex). However, the amount of protein interactions with an experimentally determined three-dimensional structure is scarce. Therefore, computational techniques such as homology modeling are foremost to fill this gap. Protein interactions can be modeled using as templates the interactions of homologous proteins, if the structure of the complex is known, or using docking methods. In both approaches, the estimation of the quality of models is essential. There are several ways to address this problem. In this review, we focus on the use of knowledge-based potentials for the analysis of protein interactions. We describe the procedure to derive statistical potentials and split them into different energetic terms that can be used for different purposes. We extensively discuss the fields where knowledge-based potentials have been successfully applied to (1) model protein-protein, protein-DNA, and protein-RNA interactions and (2) predict binding sites (in the protein and in the DNA). Moreover, we provide ready-to-use resources for docking and benchmarking protein interactions.
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15
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Bonet J, Planas-Iglesias J, Garcia-Garcia J, Marín-López MA, Fernandez-Fuentes N, Oliva B. ArchDB 2014: structural classification of loops in proteins. Nucleic Acids Res 2013; 42:D315-9. [PMID: 24265221 PMCID: PMC3964960 DOI: 10.1093/nar/gkt1189] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
The function of a protein is determined by its three-dimensional structure, which is formed by regular (i.e. β-strands and α-helices) and non-periodic structural units such as loops. Compared to regular structural elements, non-periodic, non-repetitive conformational units enclose a much higher degree of variability—raising difficulties in the identification of regularities, and yet represent an important part of the structure of a protein. Indeed, loops often play a pivotal role in the function of a protein and different aspects of protein folding and dynamics. Therefore, the structural classification of protein loops is an important subject with clear applications in homology modelling, protein structure prediction, protein design (e.g. enzyme design and catalytic loops) and function prediction. ArchDB, the database presented here (freely available at http://sbi.imim.es/archdb), represents such a resource and has been an important asset for the scientific community throughout the years. In this article, we present a completely reworked and updated version of ArchDB. The new version of ArchDB features a novel, fast and user-friendly web-based interface, and a novel graph-based, computationally efficient, clustering algorithm. The current version of ArchDB classifies 149,134 loops in 5739 classes and 9608 subclasses.
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Affiliation(s)
- Jaume Bonet
- Structural Bioinformatics Lab (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Research Park of Biomedicine (PRBB), Barcelona, Catalonia, 08950, Spain and Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, SY23 3DA Aberystwyth, Ceredigion, UK
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16
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iPPI-DB: a manually curated and interactive database of small non-peptide inhibitors of protein-protein interactions. Drug Discov Today 2013; 18:958-68. [PMID: 23688585 DOI: 10.1016/j.drudis.2013.05.003] [Citation(s) in RCA: 83] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2013] [Revised: 05/06/2013] [Accepted: 05/10/2013] [Indexed: 01/05/2023]
Abstract
The development of small molecule drugs targeting protein-protein interactions (PPI) represents a major challenge, in part owing to the misunderstanding of the PPI chemical space. To this end, we have manually collected the structures, the physicochemical and pharmacological profiles of 1650 PPI inhibitors across 13 families of PPI targets in a database named iPPI-DB. To access iPPI-DB, we propose a user-friendly web application (www.ippidb.cdithem.fr) with customizable queries and intuitive visualizing functionalities for associated properties of the compounds. This could assist scientists to design the next generation of PPI drugs. In this review, we describe iPPI-DB in the context of other low molecular weight molecule databases.
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Karaca E, Bonvin AMJJ. On the usefulness of ion-mobility mass spectrometry and SAXS data in scoring docking decoys. ACTA CRYSTALLOGRAPHICA SECTION D: BIOLOGICAL CRYSTALLOGRAPHY 2013; 69:683-94. [DOI: 10.1107/s0907444913007063] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2012] [Accepted: 03/13/2013] [Indexed: 12/20/2022]
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18
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Karaca E, Bonvin AM. Advances in integrative modeling of biomolecular complexes. Methods 2013; 59:372-81. [DOI: 10.1016/j.ymeth.2012.12.004] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2012] [Revised: 11/30/2012] [Accepted: 12/14/2012] [Indexed: 11/25/2022] Open
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