Binding Site Comparison - Software and Applications

This article provides an overview of different binding site comparison software, the underlying algorithms, and successful applications. After a short introduction of binding site characteristics and their representation, the article provides insights into the model principles, their definitions, and measures for binding site similarity. The broad variety of approaches is represented by an exemplary selection of useful applications, especially in medicinal chemistry. Although this text is by no means a complete review of software tools and applications, it offers a foundation for further reading and allows for an easy access to this largely underestimated field of molecular modeling.

[1]  Matthias Rarey,et al.  Maximum common subgraph isomorphism algorithms and their applications in molecular science: a review , 2011 .

[2]  Didier Rognan,et al.  Encoding Protein-Ligand Interaction Patterns in Fingerprints and Graphs , 2013, J. Chem. Inf. Model..

[3]  Matthew J. O’Meara,et al.  The Recognition of Identical Ligands by Unrelated Proteins. , 2015, ACS chemical biology.

[4]  David Rogers,et al.  Extended-Connectivity Fingerprints , 2010, J. Chem. Inf. Model..

[5]  Benjamin A. Shoemaker,et al.  IBIS (Inferred Biomolecular Interaction Server) reports, predicts and integrates multiple types of conserved interactions for proteins , 2011, Nucleic Acids Res..

[6]  P. Goodford A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. , 1985, Journal of medicinal chemistry.

[7]  Károly Héberger,et al.  Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? , 2015, Journal of Cheminformatics.

[8]  W. Kabsch,et al.  Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features , 1983, Biopolymers.

[9]  E. Kellenberger,et al.  A simple and fuzzy method to align and compare druggable ligand‐binding sites , 2008, Proteins.

[10]  Yanjie Wang,et al.  A comparative analysis of protein targets of withdrawn cardiovascular drugs in human and mouse , 2012, Journal of Clinical Bioinformatics.

[11]  H. Wolfson,et al.  Structure‐based in silico identification of ubiquitin‐binding domains provides insights into the ALIX‐V:ubiquitin complex and retrovirus budding , 2013, The EMBO journal.

[12]  Akira Tanaka,et al.  The worst-case time complexity for generating all maximal cliques and computational experiments , 2006, Theor. Comput. Sci..

[13]  Ajay N. Jain,et al.  Surface‐based protein binding pocket similarity , 2011, Proteins.

[14]  Nathanael Weill,et al.  Alignment-Free Ultra-High-Throughput Comparison of Druggable Protein-Ligand Binding Sites , 2010, J. Chem. Inf. Model..

[15]  Didier Rognan,et al.  Comparison and Druggability Prediction of Protein-Ligand Binding Sites from Pharmacophore-Annotated Cavity Shapes , 2012, J. Chem. Inf. Model..

[16]  C. Ehrt,et al.  Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design. , 2016, Journal of medicinal chemistry.

[17]  H. Wolfson,et al.  Recognition of Functional Sites in Protein Structures☆ , 2004, Journal of Molecular Biology.

[18]  Didier Rognan,et al.  sc-PDB: an Annotated Database of Druggable Binding Sites from the Protein Data Bank , 2006, J. Chem. Inf. Model..

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

[20]  P. Myler,et al.  Crystal structures of Mycobacterial MeaB and MMAA-like GTPases , 2015, Journal of Structural and Functional Genomics.

[21]  Maxim Totrov,et al.  Atomic Property Fields: Generalized 3D Pharmacophoric Potential for Automated Ligand Superposition, Pharmacophore Elucidation and 3D QSAR , 2007, Chemical biology & drug design.

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

[23]  Jonathan C. Fuller,et al.  Protein Binding Pocket Dynamics. , 2016, Accounts of chemical research.

[24]  Matthias Rarey,et al.  Exploiting structural information for drug-target assessment. , 2014, Future medicinal chemistry.

[25]  Jans H. Alzate-Morales,et al.  Similarities between the Binding Sites of SB-206553 at Serotonin Type 2 and Alpha7 Acetylcholine Nicotinic Receptors: Rationale for Its Polypharmacological Profile , 2015, PloS one.

[26]  Janez Konc,et al.  Binding site comparison for function prediction and pharmaceutical discovery. , 2014, Current opinion in structural biology.

[27]  James E. J. Mills,et al.  High-Throughput Virtual Screening of Proteins Using GRID Molecular Interaction Fields , 2010, J. Chem. Inf. Model..

[28]  A. Mclachlan Tests for comparing related amino-acid sequences. Cytochrome c and cytochrome c 551 . , 1971, Journal of molecular biology.

[29]  Didier Rognan,et al.  sc-PDB: a 3D-database of ligandable binding sites—10 years on , 2014, Nucleic Acids Res..

[30]  Burton H. Bloom,et al.  Space/time trade-offs in hash coding with allowable errors , 1970, CACM.

[31]  Philip E. Bourne,et al.  A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discovery , 2009, Bioinform..

[32]  Rafael Najmanovich,et al.  Detection of Binding Site Molecular Interaction Field Similarities , 2015, J. Chem. Inf. Model..

[33]  Peter Willett,et al.  Maximum common subgraph isomorphism algorithms for the matching of chemical structures , 2002, J. Comput. Aided Mol. Des..

[34]  Ming-Jing Hwang,et al.  Methods for predicting protein-ligand binding sites. , 2015, Methods in molecular biology.

[35]  Ivan Viola,et al.  Visual Analysis of Biomolecular Cavities: State of the Art , 2016, Comput. Graph. Forum.

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

[37]  Nathan J. Brown Algorithms for chemoinformatics , 2011 .

[38]  Kalidas Yeturu,et al.  PocketMatch: A new algorithm to compare binding sites in protein structures , 2008, BMC Bioinformatics.

[39]  Jon Louis Bentley,et al.  Multidimensional binary search trees used for associative searching , 1975, CACM.

[40]  Gerhard Klebe,et al.  From Structure to Function: A New Approach to Detect Functional Similarity among Proteins Independent from Sequence and Fold Homology. , 2001, Angewandte Chemie.

[41]  Philip E. Bourne,et al.  Drug Discovery Using Chemical Systems Biology: Weak Inhibition of Multiple Kinases May Contribute to the Anti-Cancer Effect of Nelfinavir , 2011, PLoS Comput. Biol..

[42]  J. Moon,et al.  On cliques in graphs , 1965 .

[43]  J. Skolnick,et al.  TM-align: a protein structure alignment algorithm based on the TM-score , 2005, Nucleic acids research.

[44]  Louis-Philippe Morency,et al.  NRGsuite: a PyMOL plugin to perform docking simulations in real time using FlexAID , 2015, Bioinform..

[45]  Narayanaswamy Srinivasan,et al.  Resolving protein structure‐function‐binding site relationships from a binding site similarity network perspective , 2017, Proteins.

[46]  C. Bron,et al.  Algorithm 457: finding all cliques of an undirected graph , 1973 .

[47]  Jordi Mestres,et al.  Identification of Similar Binding Sites to Detect Distant Polypharmacology , 2013, Molecular informatics.

[48]  G. Klebe,et al.  A new method to detect related function among proteins independent of sequence and fold homology. , 2002, Journal of molecular biology.

[49]  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.

[50]  G. Levi A note on the derivation of maximal common subgraphs of two directed or undirected graphs , 1973 .

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

[52]  Russ B. Altman,et al.  Variations in the Binding Pocket of an Inhibitor of the Bacterial Division Protein FtsZ across Genotypes and Species , 2015, PLoS Comput. Biol..

[53]  G. Schneider,et al.  In Silico Adoption of an Orphan Nuclear Receptor NR4A1 , 2015, PloS one.

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

[55]  Rasmus Pagh,et al.  Cuckoo Hashing , 2001, Encyclopedia of Algorithms.

[56]  Tina Ritschel,et al.  KRIPO – a structure-based pharmacophores approach explains polypharmacological effects , 2014, Journal of Cheminformatics.

[57]  M. Medina,et al.  Structure‐based classification of FAD binding sites: A comparative study of structural alignment tools , 2016, Proteins.

[58]  Didier Rognan,et al.  How to Measure the Similarity Between Protein Ligand-Binding Sites? , 2008 .

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

[60]  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.

[61]  Gabriele Cruciani,et al.  A Common Reference Framework for Analyzing/Comparing Proteins and Ligands. Fingerprints for Ligands And Proteins (FLAP): Theory and Application , 2007, J. Chem. Inf. Model..

[62]  Neal K. Broomhead,et al.  Can We Rely on Computational Predictions To Correctly Identify Ligand Binding Sites on Novel Protein Drug Targets? Assessment of Binding Site Prediction Methods and a Protocol for Validation of Predicted Binding Sites , 2016, Cell Biochemistry and Biophysics.

[63]  Matthias Rarey,et al.  Protein–ligand interaction databases: advanced tools to mine activity data and interactions on a structural level , 2014 .