Methods for predicting protein-ligand binding sites.

Ligand binding is required for many proteins to function properly. A large number of bioinformatics tools have been developed to predict ligand binding sites as a first step in understanding a protein's function or to facilitate docking computations in virtual screening based drug design. The prediction usually requires only the three-dimensional structure (experimentally determined or computationally modeled) of the target protein to be searched for ligand binding site(s), and Web servers have been built, allowing the free and simple use of prediction tools. In this chapter, we review the underlying concepts of the methods used by various tools, and discuss their different features and the related issues of ligand binding site prediction. Some cautionary notes about the use of these tools are also provided.

[1]  Martin Zacharias,et al.  In silico prediction of binding sites on proteins. , 2010, Current medicinal chemistry.

[2]  Yong Zhou,et al.  Roll: a new algorithm for the detection of protein pockets and cavities with a rolling probe sphere , 2010, Bioinform..

[3]  Alasdair T. R. Laurie,et al.  Methods for the prediction of protein-ligand binding sites for structure-based drug design and virtual ligand screening. , 2006, Current protein & peptide science.

[4]  Jie Liang,et al.  CASTp: Computed Atlas of Surface Topography of proteins , 2003, Nucleic Acids Res..

[5]  Keehyoung Joo,et al.  Protein‐binding site prediction based on three‐dimensional protein modeling , 2009, Proteins.

[6]  Hui Sun Lee,et al.  Ligand Binding Site Detection by Local Structure Alignment and Its Performance Complementarity , 2013, J. Chem. Inf. Model..

[7]  Sonali Chavan,et al.  Prediction of protein-mannose binding sites using random forest , 2012, Bioinformation.

[8]  Nagasuma Chandra,et al.  PocketDepth: a new depth based algorithm for identification of ligand binding sites in proteins. , 2008, Journal of structural biology.

[9]  R. Nussinov,et al.  Favorable scaffolds: proteins with different sequence, structure and function may associate in similar ways. , 2005, Protein engineering, design & selection : PEDS.

[10]  Shugo Nakamura,et al.  Highly accurate method for ligand‐binding site prediction in unbound state (apo) protein structures , 2008, Proteins.

[11]  Lei Xie,et al.  Detecting evolutionary relationships across existing fold space, using sequence order-independent profile–profile alignments , 2008, Proceedings of the National Academy of Sciences.

[12]  Mark N. Wass,et al.  Convergent evolution of enzyme active sites is not a rare phenomenon. , 2007, Journal of molecular biology.

[13]  M Hendlich,et al.  LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. , 1997, Journal of molecular graphics & modelling.

[14]  Kai-Cheng Hsu,et al.  KIDFamMap: a database of kinase-inhibitor-disease family maps for kinase inhibitor selectivity and binding mechanisms , 2012, Nucleic Acids Res..

[15]  Neha S. Gandhi,et al.  Prediction of heparin binding sites in bone morphogenetic proteins (BMPs). , 2012, Biochimica et biophysica acta.

[16]  Yen-Jen Oyang,et al.  MEDock: a web server for efficient prediction of ligand binding sites based on a novel optimization algorithm , 2005, Nucleic Acids Res..

[17]  Alfonso Valencia,et al.  firestar—advances in the prediction of functionally important residues , 2011, Nucleic Acids Res..

[18]  R. Abagyan,et al.  Pocketome via Comprehensive Identification and Classification of Ligand Binding Envelopes* , 2005, Molecular & Cellular Proteomics.

[19]  Richard M. Jackson,et al.  Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites , 2005, Bioinform..

[20]  Yang Zhang,et al.  I-TASSER server for protein 3D structure prediction , 2008, BMC Bioinformatics.

[21]  R. Wade,et al.  Computational approaches to identifying and characterizing protein binding sites for ligand design , 2009, Journal of molecular recognition : JMR.

[22]  G. Schneider,et al.  PocketPicker: analysis of ligand binding-sites with shape descriptors , 2007, Chemistry Central Journal.

[23]  Neha S. Gandhi,et al.  Computational analyses of the catalytic and heparin-binding sites and their interactions with glycosaminoglycans in glycoside hydrolase family 79 endo-β-D-glucuronidase (heparanase). , 2012, Glycobiology.

[24]  D. Baker,et al.  Improvement in protein functional site prediction by distinguishing structural and functional constraints on protein family evolution using computational design , 2005, Nucleic acids research.

[25]  G. Kellogg,et al.  A novel and efficient tool for locating and characterizing protein cavities and binding sites , 2010, Proteins.

[26]  H. Wolfson,et al.  A new, structurally nonredundant, diverse data set of protein–protein interfaces and its implications , 2004, Protein science : a publication of the Protein Society.

[27]  Gonzalo López,et al.  Assessment of ligand binding residue predictions in CASP8 , 2009, Proteins.

[28]  Michael J. E. Sternberg,et al.  3DLigandSite: predicting ligand-binding sites using similar structures , 2010, Nucleic Acids Res..

[29]  Yang Zhang,et al.  Template‐based modeling and free modeling by I‐TASSER in CASP7 , 2007, Proteins.

[30]  Herbert Edelsbrunner,et al.  Measuring proteins and voids in proteins , 1995, Proceedings of the Twenty-Eighth Annual Hawaii International Conference on System Sciences.

[31]  H. Edelsbrunner,et al.  Anatomy of protein pockets and cavities: Measurement of binding site geometry and implications for ligand design , 1998, Protein science : a publication of the Protein Society.

[32]  Hongbo Zhu,et al.  MSPocket: an orientation-independent algorithm for the detection of ligand binding pockets , 2011, Bioinform..

[33]  Vincent Le Guilloux,et al.  Fpocket: An open source platform for ligand pocket detection , 2009, BMC Bioinformatics.

[34]  S. Cole.  Comparative mycobacterial genomics as a tool for drug target and antigen discovery , 2002, European Respiratory Journal.

[35]  Bingding Huang,et al.  MetaPocket: a meta approach to improve protein ligand binding site prediction. , 2009, Omics : a journal of integrative biology.

[36]  Philip E. Bourne,et al.  PROMISCUOUS: a database for network-based drug-repositioning , 2010, Nucleic Acids Res..

[37]  Ming-Jing Hwang,et al.  Ligand-binding site prediction using ligand-interacting and binding site-enriched protein triangles , 2012, Bioinform..

[38]  B. Honig,et al.  On the nature of cavities on protein surfaces: Application to the identification of drug‐binding sites , 2006, Proteins.

[39]  Gail J. Bartlett,et al.  Using a neural network and spatial clustering to predict the location of active sites in enzymes. , 2003, Journal of molecular biology.

[40]  K. Houck,et al.  The Hypolipidemic Natural Product Guggulsterone Is a Promiscuous Steroid Receptor Ligand , 2005, Molecular Pharmacology.

[41]  Stéphanie Pérot,et al.  Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery. , 2010, Drug discovery today.

[42]  Torsten Schwede,et al.  Assessment of ligand‐binding residue predictions in CASP9 , 2011, Proteins.

[43]  I. Bahar,et al.  Coupling between catalytic site and collective dynamics: a requirement for mechanochemical activity of enzymes. , 2005, Structure.

[44]  Karl H. Clodfelter,et al.  Identification of substrate binding sites in enzymes by computational solvent mapping. , 2003, Journal of molecular biology.

[45]  Jun Hu,et al.  TargetATPsite: A template‐free method for ATP‐binding sites prediction with residue evolution image sparse representation and classifier ensemble , 2013, J. Comput. Chem..

[46]  B. Rost,et al.  Comparing function and structure between entire proteomes , 2001, Protein science : a publication of the Protein Society.

[47]  R. Abagyan,et al.  Comprehensive identification of "druggable" protein ligand binding sites. , 2004, Genome informatics. International Conference on Genome Informatics.

[48]  Pieter F. W. Stouten,et al.  Fast prediction and visualization of protein binding pockets with PASS , 2000, J. Comput. Aided Mol. Des..

[49]  Haruki Nakamura,et al.  Prediction of ligand‐binding sites of proteins by molecular docking calculation for a random ligand library , 2011, Protein science : a publication of the Protein Society.

[50]  I. D. de Esch,et al.  KLIFS: a knowledge-based structural database to navigate kinase-ligand interaction space. , 2014, Journal of medicinal chemistry.

[51]  Robert Fredriksson,et al.  Mapping the human membrane proteome : a majority of the human membrane proteins can be classified according to function and evolutionary origin , 2015 .

[52]  Jie Liang,et al.  CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues , 2006, Nucleic Acids Res..

[53]  Y. Goldgur,et al.  Crystal structure of the ligand-binding domain of the promiscuous EphA4 receptor reveals two distinct conformations. , 2010, Biochemical and biophysical research communications.

[54]  R. Pope,et al.  Quantification of membrane and membrane-bound proteins in normal and malignant breast cancer cells isolated from the same patient with primary breast carcinoma. , 2006, Journal of proteome research.

[55]  Ming-Jing Hwang,et al.  An interaction-motif-based scoring function for protein-ligand docking , 2010, BMC Bioinformatics.

[56]  Liam J. McGuffin,et al.  The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction , 2011, Nucleic Acids Res..

[57]  J. Thornton,et al.  A method for localizing ligand binding pockets in protein structures , 2005, Proteins.

[58]  Dima Kozakov,et al.  BIOINFORMATICS APPLICATIONS NOTE , 2022 .

[59]  Michal Brylinski,et al.  FINDSITE: a combined evolution/structure-based approach to protein function prediction , 2009, Briefings Bioinform..

[60]  Roman A. Laskowski,et al.  PDBsum: summaries and analyses of PDB structures , 2001, Nucleic Acids Res..

[61]  Vincent Le Guilloux,et al.  fpocket: online tools for protein ensemble pocket detection and tracking , 2010, Nucleic Acids Res..

[62]  Dario Ghersi,et al.  SITEHOUND-web: a server for ligand binding site identification in protein structures , 2009, Nucleic Acids Res..

[63]  David S. Goodsell,et al.  The RCSB Protein Data Bank: new resources for research and education , 2012, Nucleic Acids Res..

[64]  M. Swindells,et al.  Protein clefts in molecular recognition and function. , 1996, Protein science : a publication of the Protein Society.

[65]  Hiroki Shirai,et al.  Use of Amino Acid Composition to Predict Ligand-Binding Sites , 2007, J. Chem. Inf. Model..

[66]  Dario Ghersi,et al.  EASYMIFS and SITEHOUND: a toolkit for the identification of ligand-binding sites in protein structures , 2009, Bioinform..

[67]  A. Elofsson,et al.  Structure is three to ten times more conserved than sequence—A study of structural response in protein cores , 2009, Proteins.

[68]  V A McKusick,et al.  On the naming of clinical disorders, with particular reference to eponyms. , 1998, Medicine.

[69]  David A. Lee,et al.  Predicting protein function from sequence and structure , 2007, Nature Reviews Molecular Cell Biology.

[70]  Maria Jesus Martin,et al.  The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003 , 2003, Nucleic Acids Res..

[71]  M. Schroeder,et al.  LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation , 2006, BMC Structural Biology.

[72]  T. Kawabata.  Detection of multiscale pockets on protein surfaces using mathematical morphology , 2010, Proteins.

[73]  Paul Taylor,et al.  Identification of protein binding surfaces using surface triplet propensities , 2010, Bioinform..

[74]  Liam J. McGuffin,et al.  FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins , 2011, BMC Bioinformatics.

[75]  Sean Ekins,et al.  Challenges Predicting Ligand-Receptor Interactions of Promiscuous Proteins: The Nuclear Receptor PXR , 2009, PLoS Comput. Biol..

[76]  Chih-Min Chang,et al.  Evolutionary information hidden in a single protein structure , 2012, Proteins.

[77]  Yu Li,et al.  Identification of cavities on protein surface using multiple computational approaches for drug binding site prediction , 2011, Bioinform..

[78]  Yang Zhang,et al.  Recognizing protein-ligand binding sites by global structural alignment and local geometry refinement. , 2012, Structure.

[79]  Yang Zhang,et al.  I‐TASSER: Fully automated protein structure prediction in CASP8 , 2009, Proteins.

[80]  Adam Yao,et al.  LISE: a server using ligand-interacting and site-enriched protein triangles for prediction of ligand-binding sites , 2013, Nucleic Acids Res..

[81]  Yang Zhang,et al.  I-TASSER: a unified platform for automated protein structure and function prediction , 2010, Nature Protocols.

[82]  R. Laskowski.  SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions. , 1995, Journal of molecular graphics.

[83]  M. Thumm,et al.  Structural and functional characterization of the two phosphoinositide binding sites of PROPPINs, a β-propeller protein family , 2012, Proceedings of the National Academy of Sciences.

[84]  J. Skolnick,et al.  A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation , 2008, Proceedings of the National Academy of Sciences.

[85]  Mallur S. Madhusudhan,et al.  DEPTH: a web server to compute depth and predict small-molecule binding cavities in proteins , 2011, Nucleic Acids Res..

[86]  Dusanka Janezic,et al.  ProBiS algorithm for detection of structurally similar protein binding sites by local structural alignment , 2010, Bioinform..

[87]  Maxim Totrov,et al.  Ligand binding site superposition and comparison based on Atomic Property Fields: identification of distant homologues, convergent evolution and PDB-wide clustering of binding sites , 2011, BMC Bioinformatics.

[88]  Mona Singh,et al.  Predicting Protein Ligand Binding Sites by Combining Evolutionary Sequence Conservation and 3D Structure , 2009, PLoS Comput. Biol..

[89]  Michael J E Sternberg,et al.  Prediction of ligand binding sites using homologous structures and conservation at CASP8 , 2009, Proteins.

[90]  Daniel Kuhn,et al.  DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment , 2012, Bioinform..

[91]  Michal Brylinski,et al.  eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands , 2013, Journal of Computer-Aided Molecular Design.

[92]  Yang Zhang.  Progress and challenges in protein structure prediction. , 2008, Current opinion in structural biology.

[93]  Dusanka Janezic,et al.  ProBiS: a web server for detection of structurally similar protein binding sites , 2010, Nucleic Acids Res..

[94]  Alfonso Valencia,et al.  firestar—prediction of functionally important residues using structural templates and alignment reliability , 2007, Nucleic Acids Res..