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Pallante L, Cannariato M, Androutsos L, Zizzi EA, Bompotas A, Hada X, Grasso G, Kalogeras A, Mavroudi S, Di Benedetto G, Theofilatos K, Deriu MA. VirtuousPocketome: a computational tool for screening protein-ligand complexes to identify similar binding sites. Sci Rep 2024; 14:6296. [PMID: 38491261 PMCID: PMC10943019 DOI: 10.1038/s41598-024-56893-7] [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: 01/09/2024] [Accepted: 03/12/2024] [Indexed: 03/18/2024] Open
Abstract
Protein residues within binding pockets play a critical role in determining the range of ligands that can interact with a protein, influencing its structure and function. Identifying structural similarities in proteins offers valuable insights into their function and activation mechanisms, aiding in predicting protein-ligand interactions, anticipating off-target effects, and facilitating the development of therapeutic agents. Numerous computational methods assessing global or local similarity in protein cavities have emerged, but their utilization is impeded by complexity, impractical automation for amino acid pattern searches, and an inability to evaluate the dynamics of scrutinized protein-ligand systems. Here, we present a general, automatic and unbiased computational pipeline, named VirtuousPocketome, aimed at screening huge databases of proteins for similar binding pockets starting from an interested protein-ligand complex. We demonstrate the pipeline's potential by exploring a recently-solved human bitter taste receptor, i.e. the TAS2R46, complexed with strychnine. We pinpointed 145 proteins sharing similar binding sites compared to the analysed bitter taste receptor and the enrichment analysis highlighted the related biological processes, molecular functions and cellular components. This work represents the foundation for future studies aimed at understanding the effective role of tastants outside the gustatory system: this could pave the way towards the rationalization of the diet as a supplement to standard pharmacological treatments and the design of novel tastants-inspired compounds to target other proteins involved in specific diseases or disorders. The proposed pipeline is publicly accessible, can be applied to any protein-ligand complex, and could be expanded to screen any database of protein structures.
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Affiliation(s)
- Lorenzo Pallante
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy
| | - Marco Cannariato
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy
| | | | - Eric A Zizzi
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy
| | - Agorakis Bompotas
- Industrial Systems Institute, Athena Research Center, 265 04, Patras, Greece
| | - Xhesika Hada
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy
| | - Gianvito Grasso
- Dalle Molle Institute for Artificial Intelligence IDSIA USI-SUPSI, 6962, Lugano-Viganello, Switzerland
| | | | - Seferina Mavroudi
- Department of Nursing, School of Health Rehabilitation Sciences, University of Patras, 265 04, Patras, Greece
| | | | | | - Marco A Deriu
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy.
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Eguida M, Rognan D. Estimating the Similarity between Protein Pockets. Int J Mol Sci 2022; 23:12462. [PMID: 36293316 PMCID: PMC9604425 DOI: 10.3390/ijms232012462] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 10/15/2022] [Accepted: 10/16/2022] [Indexed: 10/28/2023] Open
Abstract
With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.
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Affiliation(s)
| | - Didier Rognan
- Laboratoire d’Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
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3
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Riziotis IG, Thornton JM. Capturing the geometry, function, and evolution of enzymes with 3D templates. Protein Sci 2022; 31:e4363. [PMID: 35762726 PMCID: PMC9207746 DOI: 10.1002/pro.4363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/06/2022] [Accepted: 05/14/2022] [Indexed: 11/05/2022]
Abstract
Structural templates are 3D signatures representing protein functional sites, such as ligand binding cavities, metal coordination motifs, or catalytic sites. Here we explore methods to generate template libraries and algorithms to query structures for conserved 3D motifs. Applications of templates are discussed, as well as some exemplar cases for examining evolutionary links in enzymes. We also introduce the concept of using more than one template per structure to represent flexible sites, as an approach to better understand catalysis through snapshots captured in enzyme structures. Functional annotation from structure is an important topic that has recently resurfaced due to the new more accurate methods of protein structure prediction. Therefore, we anticipate that template‐based functional site detection will be a powerful tool in the task of characterizing a vast number of new protein models.
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Chaudhari R, Fong LW, Tan Z, Huang B, Zhang S. An up-to-date overview of computational polypharmacology in modern drug discovery. Expert Opin Drug Discov 2020; 15:1025-1044. [PMID: 32452701 PMCID: PMC7415563 DOI: 10.1080/17460441.2020.1767063] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 05/06/2020] [Indexed: 12/30/2022]
Abstract
INTRODUCTION In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success. AREAS COVERED In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies. EXPERT OPINION Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.
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Affiliation(s)
- Rajan Chaudhari
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
| | - Long Wolf Fong
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
- MD Anderson UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Avenue, Houston, Texas 77030, United States
| | - Zhi Tan
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
| | - Beibei Huang
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
| | - Shuxing Zhang
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
- MD Anderson UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Avenue, Houston, Texas 77030, United States
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5
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Rey J, Rasolohery I, Tufféry P, Guyon F, Moroy G. PatchSearch: a web server for off-target protein identification. Nucleic Acids Res 2020; 47:W365-W372. [PMID: 31131411 PMCID: PMC6602448 DOI: 10.1093/nar/gkz478] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 04/26/2019] [Accepted: 05/21/2019] [Indexed: 01/17/2023] Open
Abstract
The large number of proteins found in the human body implies that a drug may interact with many proteins, called off-target proteins, besides its intended target. The PatchSearch web server provides an automated workflow that allows users to identify structurally conserved binding sites at the protein surfaces in a set of user-supplied protein structures. Thus, this web server may help to detect potential off-target protein. It takes as input a protein complexed with a ligand and identifies within user-defined or predefined collections of protein structures, those having a binding site compatible with this ligand in terms of geometry and physicochemical properties. It is based on a non-sequential local alignment of the patch over the entire protein surface. Then the PatchSearch web server proposes a ligand binding mode for the potential off-target, as well as an estimated affinity calculated by the Vinardo scoring function. This novel tool is able to efficiently detects potential interactions of ligands with distant off-target proteins. Furthermore, by facilitating the discovery of unexpected off-targets, PatchSearch could contribute to the repurposing of existing drugs. The server is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/PatchSearch.
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Affiliation(s)
- Julien Rey
- Université Paris Diderot, Sorbonne Paris Cité, INSERM UMRS-973, Molécules Thérapeutiques in silico (MTi), F-75205 Paris, France.,Ressource Parisienne en Bioinformatique Structurale (RPBS), Paris, France
| | - Inès Rasolohery
- Université Paris Diderot, Sorbonne Paris Cité, INSERM UMRS-973, Molécules Thérapeutiques in silico (MTi), F-75205 Paris, France
| | - Pierre Tufféry
- Université Paris Diderot, Sorbonne Paris Cité, INSERM UMRS-973, Molécules Thérapeutiques in silico (MTi), F-75205 Paris, France.,Ressource Parisienne en Bioinformatique Structurale (RPBS), Paris, France
| | - Frédéric Guyon
- Université Paris Diderot, Sorbonne Paris Cité, INSERM UMRS-973, Molécules Thérapeutiques in silico (MTi), F-75205 Paris, France
| | - Gautier Moroy
- Université Paris Diderot, Sorbonne Paris Cité, INSERM UMRS-973, Molécules Thérapeutiques in silico (MTi), F-75205 Paris, France
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6
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Sydow D, Burggraaff L, Szengel A, van Vlijmen HWT, IJzerman AP, van Westen GJP, Volkamer A. Advances and Challenges in Computational Target Prediction. J Chem Inf Model 2019; 59:1728-1742. [DOI: 10.1021/acs.jcim.8b00832] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Dominique Sydow
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Lindsey Burggraaff
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Angelika Szengel
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Herman W. T. van Vlijmen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Adriaan P. IJzerman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Gerard J. P. van Westen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Andrea Volkamer
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
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Abstract
Drugs modulate disease states through their actions on targets in the body. Determining these targets aids the focused development of new treatments, and helps to better characterize those already employed. One means of accomplishing this is through the deployment of in silico methodologies, harnessing computational analytical and predictive power to produce educated hypotheses for experimental verification. Here, we provide an overview of the current state of the art, describe some of the well-established methods in detail, and reflect on how they, and emerging technologies promoting the incorporation of complex and heterogeneous data-sets, can be employed to improve our understanding of (poly)pharmacology.
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Affiliation(s)
- Ryan Byrne
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland.
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Touvrey C, Courageux C, Guillon V, Terreux R, Nachon F, Brazzolotto X. X-ray structures of human bile-salt activated lipase conjugated to nerve agents surrogates. Toxicology 2018; 411:15-23. [PMID: 30359675 DOI: 10.1016/j.tox.2018.10.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 10/06/2018] [Accepted: 10/20/2018] [Indexed: 01/19/2023]
Abstract
The efficiency of human butyrylcholinesterase (BChE) as a stoichiometric bioscavenger of nerve agents is well established. However, wide use is currently limited by production and purification costs. Aiming at identifying an alternative human protein bioscavenger, we looked for an original scaffold candidate by virtual screening of the Protein Data Bank for functional similarity using the "Surfing the Molecules" software (sumo-pbil.ibcp.fr) and a search model based on the BChE active site topology. Besides the expected acetylcholinesterase and butyrylcholinesterase, we identified a set of bile salt activated lipases structures, among which the human pancreatic lipase (hBAL) that shares 34% identity with BChE. We produced the recombinant enzyme in mammalian cells, purified it, and measured the inhibition constants for paraoxon and surrogates of VX, sarin and tabun. We solved the X-ray structure of apo hBAL and conjugates with paraoxon and the surrogates at resolutions in the 2-Å range. These structures allow the assessment of hBAL for scavenging nerve agents. They revealed that hBAL has inverted stereoselectivity for the surrogates of nerve agent compared to human cholinesterases. We observed a remarkable flip of the catalytic histidine driven by the chelation of Zn2+. Dealkylation of the conjugate, aka aging, was solely observed for paraoxon.
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Affiliation(s)
- Cédric Touvrey
- Institut de Biologie et Chimie des Protéines, 7 Passage du Vercors, 69367 Lyon Cedex 07, France
| | - Charlotte Courageux
- Institut de Recherche Biomédicale des Armées, Département de Toxicologie et Risques Chimiques, Brétigny sur Orge, 91220, France
| | - Virginia Guillon
- Institut de Recherche Biomédicale des Armées, Département de Toxicologie et Risques Chimiques, Brétigny sur Orge, 91220, France
| | - Raphael Terreux
- Institut de Biologie et Chimie des Protéines, 7 Passage du Vercors, 69367 Lyon Cedex 07, France
| | - Florian Nachon
- Institut de Recherche Biomédicale des Armées, Département de Toxicologie et Risques Chimiques, Brétigny sur Orge, 91220, France.
| | - Xavier Brazzolotto
- Institut de Recherche Biomédicale des Armées, Département de Toxicologie et Risques Chimiques, Brétigny sur Orge, 91220, France
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9
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Leinweber M, Fober T, Freisleben B. GPU-Based Point Cloud Superpositioning for Structural Comparisons of Protein Binding Sites. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:740-752. [PMID: 27845672 DOI: 10.1109/tcbb.2016.2625793] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
In this paper, we present a novel approach to solve the labeled point cloud superpositioning problem for performing structural comparisons of protein binding sites. The solution is based on a parallel evolution strategy that operates on large populations and runs on GPU hardware. The proposed evolution strategy reduces the likelihood of getting stuck in a local optimum of the multimodal real-valued optimization problem represented by labeled point cloud superpositioning. The performance of the GPU-based parallel evolution strategy is compared to a previously proposed CPU-based sequential approach for labeled point cloud superpositioning, indicating that the GPU-based parallel evolution strategy leads to qualitatively better results and significantly shorter runtimes, with speed improvements of up to a factor of 1,500 for large populations. Binary classification tests based on the ATP, NADH, and FAD protein subsets of CavBase, a database containing putative binding sites, show average classification rate improvements from about 92 percent (CPU) to 96 percent (GPU). Further experiments indicate that the proposed GPU-based labeled point cloud superpositioning approach can be superior to traditional protein comparison approaches based on sequence alignments.
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10
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Passeri GI, Trisciuzzi D, Alberga D, Siragusa L, Leonetti F, Mangiatordi GF, Nicolotti O. Strategies of Virtual Screening in Medicinal Chemistry. ACTA ACUST UNITED AC 2018. [DOI: 10.4018/ijqspr.2018010108] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Virtual screening represents an effective computational strategy to rise-up the chances of finding new bioactive compounds by accelerating the time needed to move from an initial intuition to market. Classically, the most pursued approaches rely on ligand- and structure-based studies, the former employed when structural data information about the target is missing while the latter employed when X-ray/NMR solved or homology models are instead available for the target. The authors will focus on the most advanced techniques applied in this area. In particular, they will survey the key concepts of virtual screening by discussing how to properly select chemical libraries, how to make database curation, how to applying and- and structure-based techniques, how to wisely use post-processing methods. Emphasis will be also given to the most meaningful databases used in VS protocols. For the ease of discussion several examples will be presented.
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Affiliation(s)
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Domenico Alberga
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Lydia Siragusa
- Molecular Discovery Ltd., Pinner, Middlesex, London, United Kingdom
| | - Francesco Leonetti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Giuseppe F. Mangiatordi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
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11
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Cummings CF, Pedchenko V, Brown KL, Colon S, Rafi M, Jones-Paris C, Pokydeshava E, Liu M, Pastor-Pareja JC, Stothers C, Ero-Tolliver IA, McCall AS, Vanacore R, Bhave G, Santoro S, Blackwell TS, Zent R, Pozzi A, Hudson BG. Extracellular chloride signals collagen IV network assembly during basement membrane formation. J Cell Biol 2017; 213:479-94. [PMID: 27216258 PMCID: PMC4878091 DOI: 10.1083/jcb.201510065] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 04/29/2016] [Indexed: 01/07/2023] Open
Abstract
Basement membranes are defining features of the cellular microenvironment; however, little is known regarding their assembly outside cells. We report that extracellular Cl(-) ions signal the assembly of collagen IV networks outside cells by triggering a conformational switch within collagen IV noncollagenous 1 (NC1) domains. Depletion of Cl(-) in cell culture perturbed collagen IV networks, disrupted matrix architecture, and repositioned basement membrane proteins. Phylogenetic evidence indicates this conformational switch is a fundamental mechanism of collagen IV network assembly throughout Metazoa. Using recombinant triple helical protomers, we prove that NC1 domains direct both protomer and network assembly and show in Drosophila that NC1 architecture is critical for incorporation into basement membranes. These discoveries provide an atomic-level understanding of the dynamic interactions between extracellular Cl(-) and collagen IV assembly outside cells, a critical step in the assembly and organization of basement membranes that enable tissue architecture and function. Moreover, this provides a mechanistic framework for understanding the molecular pathobiology of NC1 domains.
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Affiliation(s)
- Christopher F Cummings
- Department of Biochemistry, Vanderbilt University Medical Center, Nashville, TN 37232 Department of Medicine, Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, TN 37232 Center for Matrix Biology, Vanderbilt University Medical Center, Nashville, TN 37232
| | - Vadim Pedchenko
- Department of Medicine, Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, TN 37232 Center for Matrix Biology, Vanderbilt University Medical Center, Nashville, TN 37232
| | - Kyle L Brown
- Department of Medicine, Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, TN 37232 Center for Matrix Biology, Vanderbilt University Medical Center, Nashville, TN 37232 Center for Structural Biology, Vanderbilt University Medical Center, Nashville, TN 37232
| | - Selene Colon
- Department of Medicine, Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, TN 37232 Center for Matrix Biology, Vanderbilt University Medical Center, Nashville, TN 37232 Aspirnaut Program, Vanderbilt University Medical Center, Nashville, TN 37232
| | - Mohamed Rafi
- Department of Medicine, Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, TN 37232 Center for Matrix Biology, Vanderbilt University Medical Center, Nashville, TN 37232
| | - Celestial Jones-Paris
- Aspirnaut Program, Vanderbilt University Medical Center, Nashville, TN 37232 Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232
| | - Elena Pokydeshava
- Department of Medicine, Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, TN 37232 Center for Matrix Biology, Vanderbilt University Medical Center, Nashville, TN 37232
| | - Min Liu
- School of Life Sciences, Tsinghua University, Beijing 100084, China
| | | | - Cody Stothers
- Department of Biology, Vanderbilt University Medical Center, Nashville, TN 37232 Aspirnaut Program, Vanderbilt University Medical Center, Nashville, TN 37232
| | - Isi A Ero-Tolliver
- Department of Medicine, Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, TN 37232 Center for Matrix Biology, Vanderbilt University Medical Center, Nashville, TN 37232 Aspirnaut Program, Vanderbilt University Medical Center, Nashville, TN 37232
| | - A Scott McCall
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN 37232
| | - Roberto Vanacore
- Department of Medicine, Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, TN 37232 Center for Matrix Biology, Vanderbilt University Medical Center, Nashville, TN 37232
| | - Gautam Bhave
- Department of Medicine, Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, TN 37232 Center for Matrix Biology, Vanderbilt University Medical Center, Nashville, TN 37232
| | - Samuel Santoro
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232
| | - Timothy S Blackwell
- Department of Medicine, Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN 37232
| | - Roy Zent
- Department of Medicine, Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, TN 37232 Center for Matrix Biology, Vanderbilt University Medical Center, Nashville, TN 37232 Department of Cell and Developmental Biology, Vanderbilt University Medical Center, Nashville, TN 37232 Department of Cancer Biology, Vanderbilt University Medical Center, Nashville, TN 37232
| | - Ambra Pozzi
- Department of Medicine, Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, TN 37232 Center for Matrix Biology, Vanderbilt University Medical Center, Nashville, TN 37232 Department of Cancer Biology, Vanderbilt University Medical Center, Nashville, TN 37232 Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN 37232
| | - Billy G Hudson
- Department of Biochemistry, Vanderbilt University Medical Center, Nashville, TN 37232 Department of Medicine, Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, TN 37232 Center for Matrix Biology, Vanderbilt University Medical Center, Nashville, TN 37232 Aspirnaut Program, Vanderbilt University Medical Center, Nashville, TN 37232 Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232 Department of Cell and Developmental Biology, Vanderbilt University Medical Center, Nashville, TN 37232 Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232 Vanderbilt Institute of Chemical Biology, Vanderbilt University Medical Center, Nashville, TN 37232
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12
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Rasolohery I, Moroy G, Guyon F. PatchSearch: A Fast Computational Method for Off-Target Detection. J Chem Inf Model 2017; 57:769-777. [PMID: 28282119 DOI: 10.1021/acs.jcim.6b00529] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Many therapeutic molecules are known to bind several proteins, which can be different from the initially targeted one. Such unexpected interactions with proteins called off-targets can lead to adverse effects. Potential off-target identification is important to predict to avoid drug side effects or to discover new targets for existing drugs. We propose a new program named PatchSearch that implements local nonsequential searching for similar binding sites on protein surfaces with a controlled amount of flexibility. It is based on detection of quasi-cliques in product graphs representing all the possible matchings between two compared structures. This method has been benchmarked on a large diversity of ligands and on five data sets ranging from 12 to more than 7000 protein structures. The experiments conducted in this study show that the PatchSearch method could be useful in the early identification of off-targets. The program and the benchmarks presented in this paper are available as an R package at https://github.com/MTiPatchSearch .
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Affiliation(s)
- Inès Rasolohery
- Molécules Thérapeutiques in Silico, UMRS 973, Université Paris Diderot, INSERM , F-75013 Paris, France
| | - Gautier Moroy
- Molécules Thérapeutiques in Silico, UMRS 973, Université Paris Diderot, INSERM , F-75013 Paris, France
| | - Frédéric Guyon
- Molécules Thérapeutiques in Silico, UMRS 973, Université Paris Diderot, INSERM , F-75013 Paris, France
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13
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Chaudhari R, Tan Z, Huang B, Zhang S. Computational polypharmacology: a new paradigm for drug discovery. Expert Opin Drug Discov 2017; 12:279-291. [PMID: 28067061 PMCID: PMC7241838 DOI: 10.1080/17460441.2017.1280024] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
INTRODUCTION Over the past couple of years, the cost of drug development has sharply increased along with the high rate of clinical trial failures. Such increase in expenses is partially due to the inability of the "one drug - one target" approach to predict drug side effects and toxicities. To tackle this issue, an alternative approach, known as polypharmacology, is being adopted to study small molecule interactions with multiple targets. Apart from developing more potent and effective drugs, this approach allows for studies of off-target activities and the facilitation of drug repositioning. Although exhaustive polypharmacology studies in-vitro or in-vivo are not practical, computational methods of predicting unknown targets or side effects are being developed. Areas covered: This article describes various computational approaches that have been developed to study polypharmacology profiles of small molecules. It also provides a brief description of the algorithms used in these state-of-the-art methods. Expert opinion: Recent success in computational prediction of multi-targeting drugs has established polypharmacology as a promising alternative approach to tackle some of the daunting complications in drug discovery. This will not only help discover more effective agents, but also present tremendous opportunities to study novel target pharmacology and facilitate drug repositioning efforts in the pharmaceutical industry.
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Affiliation(s)
- Rajan Chaudhari
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
| | - Zhi Tan
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
- The University of Texas Graduate School of Biomedical Sciences, Houston, TX 77030
| | - Beibei Huang
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
| | - Shuxing Zhang
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
- The University of Texas Graduate School of Biomedical Sciences, Houston, TX 77030
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14
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Brylinski M. Local Alignment of Ligand Binding Sites in Proteins for Polypharmacology and Drug Repositioning. Methods Mol Biol 2017; 1611:109-122. [PMID: 28451975 PMCID: PMC5513668 DOI: 10.1007/978-1-4939-7015-5_9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
The administration of drugs is a key strategy in pharmacotherapy to treat diseases. Drugs are typically developed to modulate the function of specific proteins, which are directly associated with particular disease states. Nonetheless, recent studies suggest that protein-drug interactions are rather promiscuous and the majority of pharmaceuticals exhibit activity against multiple, often unrelated proteins. Certainly, the lack of selectivity often leads to drug side effects; on the other hand, these polypharmacological attributes can be used to develop drugs acting on multiple targets within a unique disease pathway, as well as to identify new targets for existing drugs, which is known as drug repositioning. To support drug development and repurposing, we developed eMatchSite, a new approach to detect those binding sites having the capability to bind similar compounds. eMatchSite is available as a standalone software and a webserver at http://www.brylinski.org/ematchsite .
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Affiliation(s)
- Michal Brylinski
- Department of Biological Sciences, Louisiana State University, 407 Choppin Hall, Baton Rouge, LA, 70803, USA.
- Center for Computation and Technology, Louisiana State University, 2054 Digital Media Center, Baton Rouge, LA, 70803, USA.
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15
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Chartier M, Najmanovich R. Detection of Binding Site Molecular Interaction Field Similarities. J Chem Inf Model 2015; 55:1600-15. [PMID: 26158641 DOI: 10.1021/acs.jcim.5b00333] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Protein binding-site similarity detection methods can be used to predict protein function and understand molecular recognition, as a tool in drug design for drug repurposing and polypharmacology, and for the prediction of the molecular determinants of drug toxicity. Here, we present IsoMIF, a method able to identify binding site molecular interaction field similarities across protein families. IsoMIF utilizes six chemical probes and the detection of subgraph isomorphisms to identify geometrically and chemically equivalent sections of protein cavity pairs. The method is validated using six distinct data sets, four of those previously used in the validation of other methods. The mean area under the receiver operator curve (AUC) obtained across data sets for IsoMIF is higher than those of other methods. Furthermore, while IsoMIF obtains consistently high AUC values across data sets, other methods perform more erratically across data sets. IsoMIF can be used to predict function from structure, to detect potential cross-reactivity or polypharmacology targets, and to help suggest bioisosteric replacements to known binding molecules. Given that IsoMIF detects spatial patterns of molecular interaction field similarities, its predictions are directly related to pharmacophores and may be readily translated into modeling decisions in structure-based drug design. IsoMIF may in principle detect similar binding sites with distinct amino acid arrangements that lead to equivalent interactions within the cavity. The source code to calculate and visualize MIFs and MIF similarities are freely available.
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Affiliation(s)
- Matthieu Chartier
- Department of Biochemistry, Faculty of Medicine and Health Sciences, University of Sherbrooke , 12e Avenue Nord, Sherbrooke, J1H 5N4 Québec, Canada
| | - Rafael Najmanovich
- Department of Biochemistry, Faculty of Medicine and Health Sciences, University of Sherbrooke , 12e Avenue Nord, Sherbrooke, J1H 5N4 Québec, Canada
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16
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Flores DI, Sotelo-Mundo RR, Brizuela CA. A simple extension to the CMASA method for the prediction of catalytic residues in the presence of single point mutations. PLoS One 2014; 9:e108513. [PMID: 25268770 PMCID: PMC4182483 DOI: 10.1371/journal.pone.0108513] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2014] [Accepted: 08/31/2014] [Indexed: 11/23/2022] Open
Abstract
The automatic identification of catalytic residues still remains an important challenge in structural bioinformatics. Sequence-based methods are good alternatives when the query shares a high percentage of identity with a well-annotated enzyme. However, when the homology is not apparent, which occurs with many structures from the structural genome initiative, structural information should be exploited. A local structural comparison is preferred to a global structural comparison when predicting functional residues. CMASA is a recently proposed method for predicting catalytic residues based on a local structure comparison. The method achieves high accuracy and a high value for the Matthews correlation coefficient. However, point substitutions or a lack of relevant data strongly affect the performance of the method. In the present study, we propose a simple extension to the CMASA method to overcome this difficulty. Extensive computational experiments are shown as proof of concept instances, as well as for a few real cases. The results show that the extension performs well when the catalytic site contains mutated residues or when some residues are missing. The proposed modification could correctly predict the catalytic residues of a mutant thymidylate synthase, 1EVF. It also successfully predicted the catalytic residues for 3HRC despite the lack of information for a relevant side chain atom in the PDB file.
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Affiliation(s)
- David I. Flores
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, Baja California, México
| | | | - Carlos A. Brizuela
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, Baja California, México
- * E-mail:
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17
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Brylinski M. eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models. PLoS Comput Biol 2014; 10:e1003829. [PMID: 25232727 PMCID: PMC4168975 DOI: 10.1371/journal.pcbi.1003829] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2014] [Accepted: 07/24/2014] [Indexed: 01/26/2023] Open
Abstract
Detecting similarities between ligand binding sites in the absence of global homology between target proteins has been recognized as one of the critical components of modern drug discovery. Local binding site alignments can be constructed using sequence order-independent techniques, however, to achieve a high accuracy, many current algorithms for binding site comparison require high-quality experimental protein structures, preferably in the bound conformational state. This, in turn, complicates proteome scale applications, where only various quality structure models are available for the majority of gene products. To improve the state-of-the-art, we developed eMatchSite, a new method for constructing sequence order-independent alignments of ligand binding sites in protein models. Large-scale benchmarking calculations using adenine-binding pockets in crystal structures demonstrate that eMatchSite generates accurate alignments for almost three times more protein pairs than SOIPPA. More importantly, eMatchSite offers a high tolerance to structural distortions in ligand binding regions in protein models. For example, the percentage of correctly aligned pairs of adenine-binding sites in weakly homologous protein models is only 4–9% lower than those aligned using crystal structures. This represents a significant improvement over other algorithms, e.g. the performance of eMatchSite in recognizing similar binding sites is 6% and 13% higher than that of SiteEngine using high- and moderate-quality protein models, respectively. Constructing biologically correct alignments using predicted ligand binding sites in protein models opens up the possibility to investigate drug-protein interaction networks for complete proteomes with prospective systems-level applications in polypharmacology and rational drug repositioning. eMatchSite is freely available to the academic community as a web-server and a stand-alone software distribution at http://www.brylinski.org/ematchsite.
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Affiliation(s)
- Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana, United States of America
- Center for Computation & Technology, Louisiana State University, Baton Rouge, Louisiana, United States of America
- * E-mail:
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18
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Micale G, Pulvirenti A, Giugno R, Ferro A. Proteins comparison through probabilistic optimal structure local alignment. Front Genet 2014; 5:302. [PMID: 25228906 PMCID: PMC4151033 DOI: 10.3389/fgene.2014.00302] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2014] [Accepted: 08/12/2014] [Indexed: 11/13/2022] Open
Abstract
Multiple local structure comparison helps to identify common structural motifs or conserved binding sites in 3D structures in distantly related proteins. Since there is no best way to compare structures and evaluate the alignment, a wide variety of techniques and different similarity scoring schemes have been proposed. Existing algorithms usually compute the best superposition of two structures or attempt to solve it as an optimization problem in a simpler setting (e.g., considering contact maps or distance matrices). Here, we present PROPOSAL (PROteins comparison through Probabilistic Optimal Structure local ALignment), a stochastic algorithm based on iterative sampling for multiple local alignment of protein structures. Our method can efficiently find conserved motifs across a set of protein structures. Only the distances between all pairs of residues in the structures are computed. To show the accuracy and the effectiveness of PROPOSAL we tested it on a few families of protein structures. We also compared PROPOSAL with two state-of-the-art tools for pairwise local alignment on a dataset of manually annotated motifs. PROPOSAL is available as a Java 2D standalone application or a command line program at http://ferrolab.dmi.unict.it/proposal/proposal.html.
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Affiliation(s)
- Giovanni Micale
- Department of Computer Science, University of Pisa Pisa, Italy
| | - Alfredo Pulvirenti
- Department of Clinical and Molecular Biomedicine, University of Catania Catania, Italy
| | - Rosalba Giugno
- Department of Clinical and Molecular Biomedicine, University of Catania Catania, Italy
| | - Alfredo Ferro
- Department of Clinical and Molecular Biomedicine, University of Catania Catania, Italy
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19
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Abstract
The amount of known protein structures is continuously growing, exhibited in over 95,000 3D structures freely available via the PDB. Over the last decade, pharmaceutical research has sparked interest in computationally extracting information from this large data pool, resulting in a homology-driven knowledge transfer from annotated to new structures. Studying protein structures with respect to understanding and modulating their functional behavior means analyzing their centers of action. Therefore, the detection and description of potential binding sites on the protein surface is a major step towards protein classification and assessment. Subsequently, these representations can be incorporated to compare proteins, and to predict their druggability or function. Especially in the context of target identification and polypharmacology, automated tools for large-scale target comparisons are highly needed. In this article, developments for automated structure-based target assessment are reviewed and remaining challenges as well as future perspectives are discussed.
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20
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Abstract
Drug action can be rationalized as interaction of a molecule with proteins in a regulatory network of targets from a specific biological system. Both drug and side effects are often governed by interaction of the drug molecule with many, often unrelated, targets. Accordingly, arrays of protein–ligand interaction data from numerous in vitro profiling assays today provide growing evidence of polypharmacological drug interactions, even for marketed drugs. In vitro off-target profiling has therefore become an important tool in early drug discovery to learn about potential off-target liabilities, which are sometimes beneficial, but more often safety relevant. The rapidly developing field of in silico profiling approaches is complementing in vitro profiling. These approaches capitalize from large amounts of biochemical data from multiple sources to be exploited for optimizing undesirable side effects in pharmaceutical research. Therefore, current in silico profiling models are nowadays perceived as valuable tools in drug discovery, and promise a platform to support optimally informed decisions.
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21
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Nemoto W, Saito A, Oikawa H. Recent advances in functional region prediction by using structural and evolutionary information - Remaining problems and future extensions. Comput Struct Biotechnol J 2013; 8:e201308007. [PMID: 24688747 PMCID: PMC3962155 DOI: 10.5936/csbj.201308007] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2013] [Revised: 11/12/2013] [Accepted: 11/13/2013] [Indexed: 11/22/2022] Open
Abstract
Structural genomics projects have solved many new structures with unknown functions. One strategy to investigate the function of a structure is to computationally find the functionally important residues or regions on it. Therefore, the development of functional region prediction methods has become an important research subject. An effective approach is to use a method employing structural and evolutionary information, such as the evolutionary trace (ET) method. ET ranks the residues of a protein structure by calculating the scores for relative evolutionary importance, and locates functionally important sites by identifying spatial clusters of highly ranked residues. After ET was developed, numerous ET-like methods were subsequently reported, and many of them are in practical use, although they require certain conditions. In this mini review, we first introduce the remaining problems and the recent improvements in the methods using structural and evolutionary information. We then summarize the recent developments of the methods. Finally, we conclude by describing possible extensions of the evolution- and structure-based methods.
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Affiliation(s)
- Wataru Nemoto
- Division of Life Science and Engineering, School of Science and Engineering, Tokyo Denki University (TDU), Ishizaka, Hatoyama-cho, Hiki-gun, Saitama, 350-0394, Japan
| | - Akira Saito
- Division of Life Science and Engineering, School of Science and Engineering, Tokyo Denki University (TDU), Ishizaka, Hatoyama-cho, Hiki-gun, Saitama, 350-0394, Japan
| | - Hayato Oikawa
- Division of Life Science and Engineering, School of Science and Engineering, Tokyo Denki University (TDU), Ishizaka, Hatoyama-cho, Hiki-gun, Saitama, 350-0394, Japan
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22
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Rahimi A, Madadkar-Sobhani A, Touserkani R, Goliaei B. Efficacy of function specific 3D-motifs in enzyme classification according to their EC-numbers. J Theor Biol 2013; 336:36-43. [PMID: 23871713 DOI: 10.1016/j.jtbi.2013.07.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2013] [Revised: 06/05/2013] [Accepted: 07/02/2013] [Indexed: 11/28/2022]
Abstract
Due to the increasing number of protein structures with unknown function originated from structural genomics projects, protein function prediction has become an important subject in bioinformatics. Among diverse function prediction methods, exploring known 3D-motifs, which are associated with functional elements in unknown protein structures is one of the most biologically meaningful methods. Homologous enzymes inherit such motifs in their active sites from common ancestors. However, slight differences in the properties of these motifs, results in variation in the reactions and substrates of the enzymes. In this study, we examined the possibility of discriminating highly related active site patterns according to their EC-numbers by 3D-motifs. For each EC-number, the spatial arrangement of an active site, which has minimum average distance to other active sites with the same function, was selected as a representative 3D-motif. In order to characterize the motifs, various points in active site elements were tested. The results demonstrated the possibility of predicting full EC-number of enzymes by 3D-motifs. However, the discriminating power of 3D-motifs varies among different enzyme families and depends on selecting the appropriate points and features.
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Affiliation(s)
- Amir Rahimi
- Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
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23
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Jalencas X, Mestres J. Identification of Similar Binding Sites to Detect Distant Polypharmacology. Mol Inform 2013; 32:976-90. [PMID: 27481143 DOI: 10.1002/minf.201300082] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2013] [Accepted: 07/29/2013] [Indexed: 01/19/2023]
Abstract
The ability of small molecules to interact with multiple proteins is referred to as polypharmacology. This property is often linked to the therapeutic action of drugs but it is known also to be responsible for many of their side effects. Because of its importance, the development of computational methods that can predict drug polypharmacology has become an important line of research that led recently to the identification of many novel targets for known drugs. Nowadays, the majority of these methods are based on measuring the similarity of a query molecule against the hundreds of thousands of molecules for which pharmacological data on thousands of proteins are available in public sources. However, similarity-based methods are inherently biased by the chemical coverage offered by the active molecules present in those public repositories, which limits significantly their capacity to predict interactions with proteins structurally and functionally unrelated to any of the already known targets for drugs. It is in this respect that structure-based methods aiming at identifying similar binding sites may offer an alternative complementary means to ligand-based methods for detecting distant polypharmacology. The different existing approaches to binding site detection, representation, comparison, and fragmentation are reviewed and recent successful applications presented.
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Affiliation(s)
- Xavier Jalencas
- Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Research Institute & University Pompeu Fabra, Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain fax: +34 93 3160550
| | - Jordi Mestres
- Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Research Institute & University Pompeu Fabra, Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain fax: +34 93 3160550.
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24
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Murakami Y, Kinoshita K, Kinjo AR, Nakamura H. Exhaustive comparison and classification of ligand-binding surfaces in proteins. Protein Sci 2013; 22:1379-91. [PMID: 23934772 PMCID: PMC3795496 DOI: 10.1002/pro.2329] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2013] [Revised: 07/29/2013] [Accepted: 08/05/2013] [Indexed: 12/03/2022]
Abstract
Many proteins function by interacting with other small molecules (ligands). Identification of ligand-binding sites (LBS) in proteins can therefore help to infer their molecular functions. A comprehensive comparison among local structures of LBSs was previously performed, in order to understand their relationships and to classify their structural motifs. However, similar exhaustive comparison among local surfaces of LBSs (patches) has never been performed, due to computational complexity. To enhance our understanding of LBSs, it is worth performing such comparisons among patches and classifying them based on similarities of their surface configurations and electrostatic potentials. In this study, we first developed a rapid method to compare two patches. We then clustered patches corresponding to the same PDB chemical component identifier for a ligand, and selected a representative patch from each cluster. We subsequently exhaustively as compared the representative patches and clustered them using similarity score, PatSim. Finally, the resultant PatSim scores were compared with similarities of atomic structures of the LBSs and those of the ligand-binding protein sequences and functions. Consequently, we classified the patches into ∼2000 well-characterized clusters. We found that about 63% of these clusters are used in identical protein folds, although about 25% of the clusters are conserved in distantly related proteins and even in proteins with cross-fold similarity. Furthermore, we showed that patches with higher PatSim score have potential to be involved in similar biological processes.
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Affiliation(s)
- Yoichi Murakami
- Graduate School of Information Sciences, Tohoku University, 6-3-09 Aramaki-aza-aoba, Aoba-ku, Sendai, Miyagi, 982-0036, Japan
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25
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Nilmeier JP, Kirshner DA, Wong SE, Lightstone FC. Rapid catalytic template searching as an enzyme function prediction procedure. PLoS One 2013; 8:e62535. [PMID: 23675414 PMCID: PMC3651201 DOI: 10.1371/journal.pone.0062535] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2013] [Accepted: 03/22/2013] [Indexed: 11/18/2022] Open
Abstract
We present an enzyme protein function identification algorithm, Catalytic Site Identification (CatSId), based on identification of catalytic residues. The method is optimized for highly accurate template identification across a diverse template library and is also very efficient in regards to time and scalability of comparisons. The algorithm matches three-dimensional residue arrangements in a query protein to a library of manually annotated, catalytic residues--The Catalytic Site Atlas (CSA). Two main processes are involved. The first process is a rapid protein-to-template matching algorithm that scales quadratically with target protein size and linearly with template size. The second process incorporates a number of physical descriptors, including binding site predictions, in a logistic scoring procedure to re-score matches found in Process 1. This approach shows very good performance overall, with a Receiver-Operator-Characteristic Area Under Curve (AUC) of 0.971 for the training set evaluated. The procedure is able to process cofactors, ions, nonstandard residues, and point substitutions for residues and ions in a robust and integrated fashion. Sites with only two critical (catalytic) residues are challenging cases, resulting in AUCs of 0.9411 and 0.5413 for the training and test sets, respectively. The remaining sites show excellent performance with AUCs greater than 0.90 for both the training and test data on templates of size greater than two critical (catalytic) residues. The procedure has considerable promise for larger scale searches.
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Affiliation(s)
- Jerome P. Nilmeier
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California, United States of America
| | - Daniel A. Kirshner
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California, United States of America
| | - Sergio E. Wong
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California, United States of America
| | - Felice C. Lightstone
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California, United States of America
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26
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Chemogenomics in drug discovery: computational methods based on the comparison of binding sites. Future Med Chem 2013; 4:1971-9. [PMID: 23088277 DOI: 10.4155/fmc.12.147] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Novel computational methods for understanding relationships between ligands and all possible biological targets have emerged in recent years. Proteins are connected to each other based on the similarity of their ligands or based on the similarity of their binding sites. The assumption is that compounds sharing chemical similarity should share targets and that targets with a similar binding site should also share ligands. A large number of computational techniques have been developed to assess ligand and binding site similarity, which can be used to make quantitative predictions of the most probable biological target of a given compound. This review covers the recent advances in new computational methods for relating biological targets based on the similarity of their binding sites. Binding site comparisons are used for the prediction of their most likely ligands, their possible cross reactivity and selectivity. These comparisons can also be used to infer the function of novel uncharacterized proteins.
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27
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von Behren MM, Volkamer A, Henzler AM, Schomburg KT, Urbaczek S, Rarey M. Fast protein binding site comparison via an index-based screening technology. J Chem Inf Model 2013; 53:411-22. [PMID: 23390978 DOI: 10.1021/ci300469h] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
We present TrixP, a new index-based method for fast protein binding site comparison and function prediction. TrixP determines binding site similarities based on the comparison of descriptors that encode pharmacophoric and spatial features. Therefore, it adopts the efficient core components of TrixX, a structure-based virtual screening technology for large compound libraries. TrixP expands this technology by new components in order to allow a screening of protein libraries. TrixP accounts for the inherent flexibility of proteins employing a partial shape matching routine. After the identification of structures with matching pharmacophoric features and geometric shape, TrixP superimposes the binding sites and, finally, assesses their similarity according to the fit of pharmacophoric properties. TrixP is able to find analogies between closely and distantly related binding sites. Recovery rates of 81.8% for similar binding site pairs, assisted by rejecting rates of 99.5% for dissimilar pairs on a test data set containing 1331 pairs, confirm this ability. TrixP exclusively identifies members of the same protein family on top ranking positions out of a library consisting of 9802 binding sites. Furthermore, 30 predicted kinase binding sites can almost perfectly be classified into their known subfamilies.
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Affiliation(s)
- Mathias M von Behren
- Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
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28
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Structural and functional analysis of multi-interface domains. PLoS One 2012; 7:e50821. [PMID: 23272073 PMCID: PMC3522720 DOI: 10.1371/journal.pone.0050821] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2012] [Accepted: 10/29/2012] [Indexed: 02/03/2023] Open
Abstract
A multi-interface domain is a domain that can shape multiple and distinctive binding sites to contact with many other domains, forming a hub in domain-domain interaction networks. The functions played by the multiple interfaces are usually different, but there is no strict bijection between the functions and interfaces as some subsets of the interfaces play the same function. This work applies graph theory and algorithms to discover fingerprints for the multiple interfaces of a domain and to establish associations between the interfaces and functions, based on a huge set of multi-interface proteins from PDB. We found that about 40% of proteins have the multi-interface property, however the involved multi-interface domains account for only a tiny fraction (1.8%) of the total number of domains. The interfaces of these domains are distinguishable in terms of their fingerprints, indicating the functional specificity of the multiple interfaces in a domain. Furthermore, we observed that both cooperative and distinctive structural patterns, which will be useful for protein engineering, exist in the multiple interfaces of a domain.
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29
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Crystal structure of ATV(ORF273), a new fold for a thermo- and acido-stable protein from the Acidianus two-tailed virus. PLoS One 2012; 7:e45847. [PMID: 23056221 PMCID: PMC3466262 DOI: 10.1371/journal.pone.0045847] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2012] [Accepted: 08/23/2012] [Indexed: 11/19/2022] Open
Abstract
Acidianus two-tailed virus (ATV) infects crenarchaea of the genus Acidianus living in terrestrial thermal springs at extremely high temperatures and low pH. ATV is a member of the Bicaudaviridae virus family and undergoes extra-cellular development of two tails, a process that is unique in the viral world. To understand this intriguing phenomenon, we have undertaken structural studies of ATV virion proteins and here we present the crystal structure of one of these proteins, ATV. ATV forms tetramers in solution and a molecular envelope is provided for the tetramer, computed from small-angle X-ray scattering (SAXS) data. The crystal structure has properties typical of hyperthermostable proteins, including a relatively high number of salt bridges. However, the protein also exhibits flexible loops and surface pockets. Remarkably, ATV displays a new protein fold, consistent with the absence of homologues of this protein in public sequence databases.
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30
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Wood DJ, Vlieg JD, Wagener M, Ritschel T. Pharmacophore Fingerprint-Based Approach to Binding Site Subpocket Similarity and Its Application to Bioisostere Replacement. J Chem Inf Model 2012; 52:2031-43. [DOI: 10.1021/ci3000776] [Citation(s) in RCA: 69] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- David J. Wood
- Computational Drug Discovery,
CMBI 260, NCMLS, Radboud University Medical Centre, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Jacob de Vlieg
- Computational Drug Discovery,
CMBI 260, NCMLS, Radboud University Medical Centre, PO Box 9101, 6500 HB Nijmegen, The Netherlands
- MSD, Department of Molecular Design and Informatics, Molenstraat 110,
5342 CC Oss, PO Box 20, 5340 BH Oss, The Netherlands
| | - Markus Wagener
- MSD, Department of Molecular Design and Informatics, Molenstraat 110,
5342 CC Oss, PO Box 20, 5340 BH Oss, The Netherlands
| | - Tina Ritschel
- Computational Drug Discovery,
CMBI 260, NCMLS, Radboud University Medical Centre, PO Box 9101, 6500 HB Nijmegen, The Netherlands
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31
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Structure-based computational analysis of protein binding sites for function and druggability prediction. J Biotechnol 2012; 159:123-34. [DOI: 10.1016/j.jbiotec.2011.12.005] [Citation(s) in RCA: 81] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2011] [Revised: 12/02/2011] [Accepted: 12/06/2011] [Indexed: 11/19/2022]
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Kinjo AR, Nakamura H. GIRAF: a method for fast search and flexible alignment of ligand binding interfaces in proteins at atomic resolution. Biophysics (Nagoya-shi) 2012; 8:79-94. [PMID: 27493524 PMCID: PMC4629647 DOI: 10.2142/biophysics.8.79] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2012] [Accepted: 04/03/2012] [Indexed: 12/01/2022] Open
Abstract
Comparison and classification of protein structures are fundamental means to understand protein functions. Due to the computational difficulty and the ever-increasing amount of structural data, however, it is in general not feasible to perform exhaustive all-against-all structure comparisons necessary for comprehensive classifications. To efficiently handle such situations, we have previously proposed a method, now called GIRAF. We herein describe further improvements in the GIRAF protein structure search and alignment method. The GIRAF method achieves extremely efficient search of similar structures of ligand binding sites of proteins by exploiting database indexing of structural features of local coordinate frames. In addition, it produces refined atom-wise alignments by iterative applications of the Hungarian method to the bipartite graph defined for a pair of superimposed structures. By combining the refined alignments based on different local coordinate frames, it is made possible to align structures involving domain movements. We provide detailed accounts for the database design, the search and alignment algorithms as well as some benchmark results.
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Affiliation(s)
- Akira R Kinjo
- Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | - Haruki Nakamura
- Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
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Ueno K, Mineta K, Ito K, Endo T. Exploring functionally related enzymes using radially distributed properties of active sites around the reacting points of bound ligands. BMC STRUCTURAL BIOLOGY 2012; 12:5. [PMID: 22536854 PMCID: PMC3408369 DOI: 10.1186/1472-6807-12-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2011] [Accepted: 04/26/2012] [Indexed: 11/10/2022]
Abstract
BACKGROUND Structural genomics approaches, particularly those solving the 3D structures of many proteins with unknown functions, have increased the desire for structure-based function predictions. However, prediction of enzyme function is difficult because one member of a superfamily may catalyze a different reaction than other members, whereas members of different superfamilies can catalyze the same reaction. In addition, conformational changes, mutations or the absence of a particular catalytic residue can prevent inference of the mechanism by which catalytic residues stabilize and promote the elementary reaction. A major hurdle for alignment-based methods for prediction of function is the absence (despite its importance) of a measure of similarity of the physicochemical properties of catalytic sites. To solve this problem, the physicochemical features radially distributed around catalytic sites should be considered in addition to structural and sequence similarities. RESULTS We showed that radial distribution functions (RDFs), which are associated with the local structural and physicochemical properties of catalytic active sites, are capable of clustering oxidoreductases and transferases by function. The catalytic sites of these enzymes were also characterized using the RDFs. The RDFs provided a measure of the similarity among the catalytic sites, detecting conformational changes caused by mutation of catalytic residues. Furthermore, the RDFs reinforced the classification of enzyme functions based on conventional sequence and structural alignments. CONCLUSIONS Our results demonstrate that the application of RDFs provides advantages in the functional classification of enzymes by providing information about catalytic sites.
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Affiliation(s)
- Keisuke Ueno
- Division of Bioinformatics, Hokkaido University Research Center for Zoonosis Control, North 20 West 10, Sapporo, Hokkaido 001-0020, Japan
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Sehnal D, Vařeková RS, Huber HJ, Geidl S, Ionescu CM, Wimmerová M, Koča J. SiteBinder: an improved approach for comparing multiple protein structural motifs. J Chem Inf Model 2012; 52:343-59. [PMID: 22296449 DOI: 10.1021/ci200444d] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
There is a paramount need to develop new techniques and tools that will extract as much information as possible from the ever growing repository of protein 3D structures. We report here on the development of a software tool for the multiple superimposition of large sets of protein structural motifs. Our superimposition methodology performs a systematic search for the atom pairing that provides the best fit. During this search, the RMSD values for all chemically relevant pairings are calculated by quaternion algebra. The number of evaluated pairings is markedly decreased by using PDB annotations for atoms. This approach guarantees that the best fit will be found and can be applied even when sequence similarity is low or does not exist at all. We have implemented this methodology in the Web application SiteBinder, which is able to process up to thousands of protein structural motifs in a very short time, and which provides an intuitive and user-friendly interface. Our benchmarking analysis has shown the robustness, efficiency, and versatility of our methodology and its implementation by the successful superimposition of 1000 experimentally determined structures for each of 32 eukaryotic linear motifs. We also demonstrate the applicability of SiteBinder using three case studies. We first compared the structures of 61 PA-IIL sugar binding sites containing nine different sugars, and we found that the sugar binding sites of PA-IIL and its mutants have a conserved structure despite their binding different sugars. We then superimposed over 300 zinc finger central motifs and revealed that the molecular structure in the vicinity of the Zn atom is highly conserved. Finally, we superimposed 12 BH3 domains from pro-apoptotic proteins. Our findings come to support the hypothesis that there is a structural basis for the functional segregation of BH3-only proteins into activators and enablers.
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Affiliation(s)
- David Sehnal
- National Centre for Biomolecular Research, Faculty of Science and CEITEC-Central European Institute of Technology, Masaryk University Brno, Kamenice 5, 62500 Brno-Bohunice, Czech Republic
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Kalliokoski T, Vulpetti A. Large-Scale Evaluation of CavBase for Analyzing the Polypharmacology of Kinase Inhibitors. Mol Inform 2011; 30:923-5. [PMID: 27468147 DOI: 10.1002/minf.201100112] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2011] [Accepted: 10/20/2011] [Indexed: 11/09/2022]
Affiliation(s)
- Tuomo Kalliokoski
- CADD, Global Discovery Chemistry, Novartis Institutes for Biomedical Research, CH4002 Basel, Switzerland.
| | - Anna Vulpetti
- CADD, Global Discovery Chemistry, Novartis Institutes for Biomedical Research, CH4002 Basel, Switzerland
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36
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Ito JI, Tabei Y, Shimizu K, Tomii K, Tsuda K. PDB-scale analysis of known and putative ligand-binding sites with structural sketches. Proteins 2011; 80:747-63. [PMID: 22113700 DOI: 10.1002/prot.23232] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2010] [Revised: 10/13/2011] [Accepted: 10/18/2011] [Indexed: 11/06/2022]
Abstract
Computational investigation of protein functions is one of the most urgent and demanding tasks in the field of structural bioinformatics. Exhaustive pairwise comparison of known and putative ligand-binding sites, across protein families and folds, is essential in elucidating the biological functions and evolutionary relationships of proteins. Given the vast amounts of data available now, existing 3D structural comparison methods are not adequate due to their computation time complexity. In this article, we propose a new bit string representation of binding sites called structural sketches, which is obtained by random projections of triplet descriptors. It allows us to use ultra-fast all-pair similarity search methods for strings with strictly controlled error rates. Exhaustive comparison of 1.2 million known and putative binding sites finished in ∼30 h on a single core to yield 88 million similar binding site pairs. Careful investigation of 3.5 million pairs verified by TM-align revealed several notable analogous sites across distinct protein families or folds. In particular, we succeeded in finding highly plausible functions of several pockets via strong structural analogies. These results indicate that our method is a promising tool for functional annotation of binding sites derived from structural genomics projects.
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Affiliation(s)
- Jun-Ichi Ito
- Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8568, Japan
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Stegemann B, Klebe G. Cofactor-binding sites in proteins of deviating sequence: comparative analysis and clustering in torsion angle, cavity, and fold space. Proteins 2011; 80:626-48. [PMID: 22095739 DOI: 10.1002/prot.23226] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2011] [Revised: 09/29/2011] [Accepted: 10/10/2011] [Indexed: 12/13/2022]
Abstract
Small molecules are recognized in protein-binding pockets through surface-exposed physicochemical properties. To optimize binding, they have to adopt a conformation corresponding to a local energy minimum within the formed protein-ligand complex. However, their conformational flexibility makes them competent to bind not only to homologous proteins of the same family but also to proteins of remote similarity with respect to the shape of the binding pockets and folding pattern. Considering drug action, such observations can give rise to unexpected and undesired cross reactivity. In this study, datasets of six different cofactors (ADP, ATP, NAD(P)(H), FAD, and acetyl CoA, sharing an adenosine diphosphate moiety as common substructure), observed in multiple crystal structures of protein-cofactor complexes exhibiting sequence identity below 25%, have been analyzed for the conformational properties of the bound ligands, the distribution of physicochemical properties in the accommodating protein-binding pockets, and the local folding patterns next to the cofactor-binding site. State-of-the-art clustering techniques have been applied to group the different protein-cofactor complexes in the different spaces. Interestingly, clustering in cavity (Cavbase) and fold space (DALI) reveals virtually the same data structuring. Remarkable relationships can be found among the different spaces. They provide information on how conformations are conserved across the host proteins and which distinct local cavity and fold motifs recognize the different portions of the cofactors. In those cases, where different cofactors are found to be accommodated in a similar fashion to the same fold motifs, only a commonly shared substructure of the cofactors is used for the recognition process.
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Affiliation(s)
- Björn Stegemann
- Institut für Pharmazeutische Chemie, Philipps-Universität Marburg, Marbacher Weg 6, D-35032 Marburg, Germany
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38
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Pérez-Nueno VI, Ritchie DW. Identifying and characterizing promiscuous targets: implications for virtual screening. Expert Opin Drug Discov 2011; 7:1-17. [PMID: 22468890 DOI: 10.1517/17460441.2011.632406] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
INTRODUCTION Ligand-based shape matching approaches have become established as important and popular virtual screening (VS) techniques. However, despite their relative success, the question of how to best choose the initial query compounds and their conformations remains largely unsolved. This issue gains importance when dealing with promiscuous targets, that is, proteins that bind multiple ligand scaffold families in one or more binding site. Conventional shape matching VS approaches assume that there is only one binding mode for a given protein target. This may be true for some targets, but it is certainly not true in all cases. Several recent studies have shown that some protein targets bind to different ligands in different ways. AREAS COVERED The authors discuss the concept of promiscuity in the context of virtual drug screening, and present and analyze several examples of promiscuous targets. The article also reports on the impact of the query conformation on the performance of shape-based VS and the potential to improve VS performance by using consensus shape clustering techniques. EXPERT OPINION The notion of polypharmacology is becoming highly relevant in drug discovery. Understanding and exploiting promiscuity present challenges and opportunities for drug discovery endeavors. The examples of promiscuity presented here suggest that promiscuous targets and ligands are much more common than previously assumed, and this should be taken into account in practical VS protocols. Although some progress has been made, there is a need to develop more sophisticated computational techniques and protocols that can identify and characterize promiscuous targets on a genomic scale.
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39
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Kochańczyk M. Prediction of functionally important residues in globular proteins from unusual central distances of amino acids. BMC STRUCTURAL BIOLOGY 2011; 11:34. [PMID: 21923943 PMCID: PMC3188475 DOI: 10.1186/1472-6807-11-34] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2011] [Accepted: 09/18/2011] [Indexed: 12/12/2022]
Abstract
BACKGROUND Well-performing automated protein function recognition approaches usually comprise several complementary techniques. Beside constructing better consensus, their predictive power can be improved by either adding or refining independent modules that explore orthogonal features of proteins. In this work, we demonstrated how the exploration of global atomic distributions can be used to indicate functionally important residues. RESULTS Using a set of carefully selected globular proteins, we parametrized continuous probability density functions describing preferred central distances of individual protein atoms. Relative preferred burials were estimated using mixture models of radial density functions dependent on the amino acid composition of a protein under consideration. The unexpectedness of extraordinary locations of atoms was evaluated in the information-theoretic manner and used directly for the identification of key amino acids. In the validation study, we tested capabilities of a tool built upon our approach, called SurpResi, by searching for binding sites interacting with ligands. The tool indicated multiple candidate sites achieving success rates comparable to several geometric methods. We also showed that the unexpectedness is a property of regions involved in protein-protein interactions, and thus can be used for the ranking of protein docking predictions. The computational approach implemented in this work is freely available via a Web interface at http://www.bioinformatics.org/surpresi. CONCLUSIONS Probabilistic analysis of atomic central distances in globular proteins is capable of capturing distinct orientational preferences of amino acids as resulting from different sizes, charges and hydrophobic characters of their side chains. When idealized spatial preferences can be inferred from the sole amino acid composition of a protein, residues located in hydrophobically unfavorable environments can be easily detected. Such residues turn out to be often directly involved in binding ligands or interfacing with other proteins.
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Affiliation(s)
- Marek Kochańczyk
- Faculty of Physics, Jagiellonian University, ul, Reymonta 4, 30-059 Krakow, Poland.
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40
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Mernberger M, Klebe G, Hüllermeier E. SEGA: semiglobal graph alignment for structure-based protein comparison. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2011; 8:1330-1343. [PMID: 21339532 DOI: 10.1109/tcbb.2011.35] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Comparative analysis is a topic of utmost importance in structural bioinformatics. Recently, a structural counterpart to sequence alignment, called multiple graph alignment, was introduced as a tool for the comparison of protein structures in general and protein binding sites in particular. Using approximate graph matching techniques, this method enables the identification of approximately conserved patterns in functionally related structures. In this paper, we introduce a new method for computing graph alignments motivated by two problems of the original approach, a conceptual and a computational one. First, the existing approach is of limited usefulness for structures that only share common substructures. Second, the goal to find a globally optimal alignment leads to an optimization problem that is computationally intractable. To overcome these disadvantages, we propose a semiglobal approach to graph alignment in analogy to semiglobal sequence alignment that combines the advantages of local and global graph matching.
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Affiliation(s)
- Marco Mernberger
- Department of Mathematics and Computer Science, Philipps-Universität Marburg, Hans-Meerwein-Straße 6, Marburg D-35032, Germany.
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41
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Finzel BC, Akavaram R, Ragipindi A, Van Voorst JR, Cahn M, Davis ME, Pokross ME, Sheriff S, Baldwin ET. Conserved Core Substructures in the Overlay of Protein–Ligand Complexes. J Chem Inf Model 2011; 51:1931-41. [DOI: 10.1021/ci100475y] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Barry C. Finzel
- Department of Medicinal Chemistry, University of Minnesota College of Pharmacy, Minneapolis, Minnesota 55455, United States
| | - Ramprasad Akavaram
- Department of Medicinal Chemistry, University of Minnesota College of Pharmacy, Minneapolis, Minnesota 55455, United States
| | - Aravind Ragipindi
- Department of Medicinal Chemistry, University of Minnesota College of Pharmacy, Minneapolis, Minnesota 55455, United States
| | - Jeffrey R. Van Voorst
- Department of Medicinal Chemistry, University of Minnesota College of Pharmacy, Minneapolis, Minnesota 55455, United States
| | - Matthew Cahn
- BioPharma Information Technologies, Bristol-Myers Squibb Company, Princeton, New Jersey 08543, United States
| | - Malcolm E. Davis
- Research & Development, Chemical and Protein Technologies, Molecular Sciences and Candidate Optimization, Bristol-Myers Squibb Company, Princeton, New Jersey 08543, United States
| | - Matt E. Pokross
- Research & Development, Chemical and Protein Technologies, Molecular Sciences and Candidate Optimization, Bristol-Myers Squibb Company, Princeton, New Jersey 08543, United States
| | - Steven Sheriff
- Research & Development, Chemical and Protein Technologies, Molecular Sciences and Candidate Optimization, Bristol-Myers Squibb Company, Princeton, New Jersey 08543, United States
| | - Eric T. Baldwin
- Research & Development, Chemical and Protein Technologies, Molecular Sciences and Candidate Optimization, Bristol-Myers Squibb Company, Princeton, New Jersey 08543, United States
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42
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Marchewka D, Banach M, Roterman I. Internal force field in proteins seen by divergence entropy. Bioinformation 2011; 6:300-2. [PMID: 21769190 PMCID: PMC3134777 DOI: 10.6026/97320630006300] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2011] [Accepted: 06/27/2011] [Indexed: 11/29/2022] Open
Abstract
The characteristic distribution of non-binding interactions in a protein is described. It establishes that hydrophobic interactions can be characterized by suitable 3D Gauss functions while electrostatic interactions generally follow a random distribution. The implementation of this observation suggests differentiated optimization procedure for these two types of interactions. The electrostatic interaction may follow traditional energy optimization while the criteria for convergence shall measure the accordance with 3-D Gauss function.
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Affiliation(s)
- Damian Marchewka
- Department of Bioinformatics and Telemedicine, Jagiellonian University, Medical College Krakow, Poland
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Krakow, Poland
| | - Mateusz Banach
- Department of Bioinformatics and Telemedicine, Jagiellonian University, Medical College Krakow, Poland
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Krakow, Poland
| | - Irena Roterman
- Department of Bioinformatics and Telemedicine, Jagiellonian University, Medical College Krakow, Poland
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43
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Moriaud F, Adcock SA, Vorotyntsev A, Doppelt-Azeroual O, Richard SB, Delfaud F. A Computational Fragment Approach by Mining the Protein Data Bank: Library Design and Bioisosterism. LIBRARY DESIGN, SEARCH METHODS, AND APPLICATIONS OF FRAGMENT-BASED DRUG DESIGN 2011. [DOI: 10.1021/bk-2011-1076.ch005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- F. Moriaud
- Felix Concordia SARL, 400 av Roumanille Bât. 7, BP 309 06906 Sophia-Antipolis, France
- MEDIT SA, 2 rue du Belvedere, 91120 Palaiseau, France
| | - S. A. Adcock
- Felix Concordia SARL, 400 av Roumanille Bât. 7, BP 309 06906 Sophia-Antipolis, France
- MEDIT SA, 2 rue du Belvedere, 91120 Palaiseau, France
| | - A. Vorotyntsev
- Felix Concordia SARL, 400 av Roumanille Bât. 7, BP 309 06906 Sophia-Antipolis, France
- MEDIT SA, 2 rue du Belvedere, 91120 Palaiseau, France
| | - O. Doppelt-Azeroual
- Felix Concordia SARL, 400 av Roumanille Bât. 7, BP 309 06906 Sophia-Antipolis, France
- MEDIT SA, 2 rue du Belvedere, 91120 Palaiseau, France
| | - S. B. Richard
- Felix Concordia SARL, 400 av Roumanille Bât. 7, BP 309 06906 Sophia-Antipolis, France
- MEDIT SA, 2 rue du Belvedere, 91120 Palaiseau, France
| | - F. Delfaud
- Felix Concordia SARL, 400 av Roumanille Bât. 7, BP 309 06906 Sophia-Antipolis, France
- MEDIT SA, 2 rue du Belvedere, 91120 Palaiseau, France
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Bienstock RJ. Overview: Fragment-Based Drug Design. LIBRARY DESIGN, SEARCH METHODS, AND APPLICATIONS OF FRAGMENT-BASED DRUG DESIGN 2011. [DOI: 10.1021/bk-2011-1076.ch001] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Rachelle J. Bienstock
- National Institute of Environmental Health Sciences, P.O. Box 12233, MD F0-011, Research Triangle Park, North Carolina 27709
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45
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Prymula K, Jadczyk T, Roterman I. Catalytic residues in hydrolases: analysis of methods designed for ligand-binding site prediction. J Comput Aided Mol Des 2010; 25:117-33. [PMID: 21104192 PMCID: PMC3032897 DOI: 10.1007/s10822-010-9402-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2010] [Accepted: 11/08/2010] [Indexed: 11/26/2022]
Abstract
The comparison of eight tools applicable to ligand-binding site prediction is presented. The methods examined cover three types of approaches: the geometrical (CASTp, PASS, Pocket-Finder), the physicochemical (Q-SiteFinder, FOD) and the knowledge-based (ConSurf, SuMo, WebFEATURE). The accuracy of predictions was measured in reference to the catalytic residues documented in the Catalytic Site Atlas. The test was performed on a set comprising selected chains of hydrolases. The results were analysed with regard to size, polarity, secondary structure, accessible solvent area of predicted sites as well as parameters commonly used in machine learning (F-measure, MCC). The relative accuracies of predictions are presented in the ROC space, allowing determination of the optimal methods by means of the ROC convex hull. Additionally the minimum expected cost analysis was performed. Both advantages and disadvantages of the eight methods are presented. Characterization of protein chains in respect to the level of difficulty in the active site prediction is introduced. The main reasons for failures are discussed. Overall, the best performance offers SuMo followed by FOD, while Pocket-Finder is the best method among the geometrical approaches.
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Affiliation(s)
- Katarzyna Prymula
- Faculty of Chemistry, Jagiellonian University, 3 Ingardena Street, 30-060 Krakow, Poland
- Department of Bioinformatics and Telemedicine, Medical College, Jagiellonian University, 7E Kopernika Street, 31-034 Krakow, Poland
| | - Tomasz Jadczyk
- Department of Electronics, AGH University of Science and Technology, 30 Mickiewicza Avenue, 30-059 Krakow, Poland
| | - Irena Roterman
- Department of Bioinformatics and Telemedicine, Medical College, Jagiellonian University, 16 Lazarza Street, 31-530 Krakow, Poland
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46
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Sonavane S, Chakrabarti P. Prediction of active site cleft using support vector machines. J Chem Inf Model 2010; 50:2266-73. [PMID: 21080689 DOI: 10.1021/ci1002922] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Computational tools are available today for the detection and delineation of the clefts and cavities in protein 3D structure and ranking them on the basis of probable binding site clefts. There is a need to improve the ranking of clefts and accuracy of predicting catalytic site clefts. Our results show that the distance of the clefts from protein centroid and sequence entropy of the lining residues, when used in conjunction with the volume, are valuable descriptors for predicting the catalytic site. We have applied the SVM approach for recognizing and ranking the active site clefts and tested its performance using different combinations of attributes. In both the ligand-bound and the unbound forms of structures, our method correctly predicts the active site clefts in 73% of cases at rank one. If we consider the results at rank 3 (i.e., the correct solution is among one of the top three solutions), the correctly predicted cases are 94% and 90% for the bound and the unbound forms of structures, respectively. Our approach improves the ranking of binding site clefts in comparison with CASTp and is comparable to other existing methods like Fpocket. Although the data set for training the SVM approach is rather small in size, the results are encouraging for the method to be used as complementary to other existing tools.
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Affiliation(s)
- Shrihari Sonavane
- Department of Biochemistry and Bioinformatics Centre, Bose Institute, P-1/12 CIT Scheme VIIM, Kolkata 700 054, India
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47
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Moll M, Bryant DH, Kavraki LE. The LabelHash algorithm for substructure matching. BMC Bioinformatics 2010; 11:555. [PMID: 21070651 PMCID: PMC2996407 DOI: 10.1186/1471-2105-11-555] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2010] [Accepted: 11/11/2010] [Indexed: 08/30/2023] Open
Abstract
Background There is an increasing number of proteins with known structure but unknown function. Determining their function would have a significant impact on understanding diseases and designing new therapeutics. However, experimental protein function determination is expensive and very time-consuming. Computational methods can facilitate function determination by identifying proteins that have high structural and chemical similarity. Results We present LabelHash, a novel algorithm for matching substructural motifs to large collections of protein structures. The algorithm consists of two phases. In the first phase the proteins are preprocessed in a fashion that allows for instant lookup of partial matches to any motif. In the second phase, partial matches for a given motif are expanded to complete matches. The general applicability of the algorithm is demonstrated with three different case studies. First, we show that we can accurately identify members of the enolase superfamily with a single motif. Next, we demonstrate how LabelHash can complement SOIPPA, an algorithm for motif identification and pairwise substructure alignment. Finally, a large collection of Catalytic Site Atlas motifs is used to benchmark the performance of the algorithm. LabelHash runs very efficiently in parallel; matching a motif against all proteins in the 95% sequence identity filtered non-redundant Protein Data Bank typically takes no more than a few minutes. The LabelHash algorithm is available through a web server and as a suite of standalone programs at http://labelhash.kavrakilab.org. The output of the LabelHash algorithm can be further analyzed with Chimera through a plugin that we developed for this purpose. Conclusions LabelHash is an efficient, versatile algorithm for large-scale substructure matching. When LabelHash is running in parallel, motifs can typically be matched against the entire PDB on the order of minutes. The algorithm is able to identify functional homologs beyond the twilight zone of sequence identity and even beyond fold similarity. The three case studies presented in this paper illustrate the versatility of the algorithm.
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Affiliation(s)
- Mark Moll
- Department of Computer Science, Rice University, Houston, TX 77005, USA.
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Reisen F, Weisel M, Kriegl JM, Schneider G. Self-organizing fuzzy graphs for structure-based comparison of protein pockets. J Proteome Res 2010; 9:6498-510. [PMID: 20883038 DOI: 10.1021/pr100719n] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Patterns of receptor-ligand interaction can be conserved in functionally equivalent proteins even in the absence of sequence homology. Therefore, structural comparison of ligand-binding pockets and their pharmacophoric features allow for the characterization of so-called "orphan" proteins with known three-dimensional structure but unknown function, and predict ligand promiscuity of binding pockets. We present an algorithm for rapid pocket comparison (PoLiMorph), in which protein pockets are represented by self-organizing graphs that fill the volume of the cavity. Vertices in these three-dimensional frameworks contain information about the local ligand-receptor interaction potential coded by fuzzy property labels. For framework matching, we developed a fast heuristic based on the maximum dispersion problem, as an alternative to techniques utilizing clique detection or geometric hashing algorithms. A sophisticated scoring function was applied that incorporates knowledge about property distributions and ligand-receptor interaction patterns. In an all-against-all virtual screening experiment with 207 pocket frameworks extracted from a subset of PDBbind, PoLiMorph correctly assigned 81% of 69 distinct structural classes and demonstrated sustained ability to group pockets accommodating the same ligand chemotype. We determined a score threshold that indicates "true" pocket similarity with high reliability, which not only supports structure-based drug design but also allows for sequence-independent studies of the proteome.
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Affiliation(s)
- Felix Reisen
- Computer-Assisted Drug Design, Eidgenössische Technische Hochschule, Zürich, Zürich, Switzerland
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Schmidtke P, Souaille C, Estienne F, Baurin N, Kroemer RT. Large-scale comparison of four binding site detection algorithms. J Chem Inf Model 2010; 50:2191-200. [PMID: 20828173 DOI: 10.1021/ci1000289] [Citation(s) in RCA: 54] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
A large-scale evaluation and comparison of four cavity detection algorithms was carried out. The algorithms SiteFinder, fpocket, PocketFinder, and SiteMap were evaluated on a protein test set containing 5416 protein-ligand complexes and 9900 apo forms, corresponding to a subset of the set used earlier for benchmarking the PocketFinder algorithm. For the holo structures, all four algorithms correctly identified a similar amount of pockets (around 95%). SiteFinder, using optimized parameters, SiteMap, and fpocket showed similar pocket ranking performance, which was defined by ranking the correct binding site on rank 1 of the predictions or within the first 5 ranks of the predictions. On the apo structures, PocketFinder especially and also SiteFinder (optimized parameters) performed best, identifying 96% and 84% of all binding sites, respectively. The fpocket program predicts binding sites most accurately among the algorithms evaluated here. SiteFinder needed an average calculation time of 1.6 s compared with 2 min for SiteMap and around 2 s for fpocket.
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Affiliation(s)
- Peter Schmidtke
- Sanofi-Aventis VA Research Centre, Structure Design & Informatics, 13 quai Jules Guesde, BP14, 94403 Vitry-sur-Seine, France
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50
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Doppelt-Azeroual O, Delfaud F, Moriaud F, de Brevern AG. Fast and automated functional classification with MED-SuMo: an application on purine-binding proteins. Protein Sci 2010; 19:847-67. [PMID: 20162627 DOI: 10.1002/pro.364] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Ligand-protein interactions are essential for biological processes, and precise characterization of protein binding sites is crucial to understand protein functions. MED-SuMo is a powerful technology to localize similar local regions on protein surfaces. Its heuristic is based on a 3D representation of macromolecules using specific surface chemical features associating chemical characteristics with geometrical properties. MED-SMA is an automated and fast method to classify binding sites. It is based on MED-SuMo technology, which builds a similarity graph, and it uses the Markov Clustering algorithm. Purine binding sites are well studied as drug targets. Here, purine binding sites of the Protein DataBank (PDB) are classified. Proteins potentially inhibited or activated through the same mechanism are gathered. Results are analyzed according to PROSITE annotations and to carefully refined functional annotations extracted from the PDB. As expected, binding sites associated with related mechanisms are gathered, for example, the Small GTPases. Nevertheless, protein kinases from different Kinome families are also found together, for example, Aurora-A and CDK2 proteins which are inhibited by the same drugs. Representative examples of different clusters are presented. The effectiveness of the MED-SMA approach is demonstrated as it gathers binding sites of proteins with similar structure-activity relationships. Moreover, an efficient new protocol associates structures absent of cocrystallized ligands to the purine clusters enabling those structures to be associated with a specific binding mechanism. Applications of this classification by binding mode similarity include target-based drug design and prediction of cross-reactivity and therefore potential toxic side effects.
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Affiliation(s)
- Olivia Doppelt-Azeroual
- INSERM UMR-S 665, Dynamique des Structures et Interactions des Macromolécules Biologiques (DSIMB), Université Paris Diderot-Paris 7, Institut National de la Transfusion Sanguine (INTS), 6, rue Alexandre Cabanel, 75739 Paris cedex 15, France.
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