Alignment-Independent Comparison of Binding Sites Based on DrugScore Potential Fields Encoded by 3D Zernike Descriptors

Analyzing protein binding sites provides detailed insights into the biological processes proteins are involved in, e.g., into drug-target interactions, and so is of crucial importance in drug discovery. Herein, we present novel alignment-independent binding site descriptors based on DrugScore potential fields. The potential fields are transformed to a set of information-rich descriptors using a series expansion in 3D Zernike polynomials. The resulting Zernike descriptors show a promising performance in detecting similarities among proteins with low pairwise sequence identities that bind identical ligands, as well as within subfamilies of one target class. Furthermore, the Zernike descriptors are robust against structural variations among protein binding sites. Finally, the Zernike descriptors show a high data compression power, and computing similarities between binding sites based on these descriptors is highly efficient. Consequently, the Zernike descriptors are a useful tool for computational binding site analysis, e.g., to predict the function of novel proteins, off-targets for drug candidates, or novel targets for known drugs.

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

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

[3]  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.

[4]  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..

[5]  Jean-Philippe Vert,et al.  A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3D: application to ligand prediction , 2010, BMC Bioinformatics.

[6]  William L. Jorgensen,et al.  Journal of Chemical Information and Modeling , 2005, J. Chem. Inf. Model..

[7]  Holger Gohlke,et al.  DrugScoreRNAKnowledge-Based Scoring Function To Predict RNA-Ligand Interactions , 2007, J. Chem. Inf. Model..

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

[9]  Holger Gohlke,et al.  Elastic Potential Grids: Accurate and Efficient Representation of Intermolecular Interactions for Fully Flexible Docking , 2009, ChemMedChem.

[10]  Aqeel Ahmed,et al.  Protein Flexibility and Mobility in Structure-Based Drug Design , 2007 .

[11]  G. Klebe,et al.  Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. , 2002, Angewandte Chemie.

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

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

[14]  Daisuke Kihara,et al.  Application of 3D Zernike descriptors to shape-based ligand similarity searching , 2009, J. Cheminformatics.

[15]  Manuel Pastor,et al.  Development and Validation of AMANDA, a New Algorithm for Selecting Highly Relevant Regions in Molecular Interaction Fields , 2008, J. Chem. Inf. Model..

[16]  Gabriele Cruciani,et al.  Molecular fields in drug discovery: getting old or reaching maturity? , 2010, Drug discovery today.

[17]  Jonathan W. Essex,et al.  Pocket-Space Maps To Identify Novel Binding-Site Conformations in Proteins , 2011, J. Chem. Inf. Model..

[18]  Gerhard Klebe,et al.  Docking into knowledge-based potential fields: a comparative evaluation of DrugScore. , 2002, Journal of medicinal chemistry.

[19]  D. Kihara,et al.  Real‐time ligand binding pocket database search using local surface descriptors , 2010, Proteins.

[20]  H. Gohlke,et al.  Structure-based computational analysis of protein binding sites for function and druggability prediction. , 2012, Journal of biotechnology.

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

[22]  M. Jacobson,et al.  Binding-Site Assessment by Virtual Fragment Screening , 2010, PloS one.

[23]  H. Matter,et al.  Structural classification of protein kinases using 3D molecular interaction field analysis of their ligand binding sites: target family landscapes. , 2002, Journal of medicinal chemistry.

[24]  Didier Rognan,et al.  Binding of Protein Kinase Inhibitors to Synapsin I Inferred from Pair-Wise Binding Site Similarity Measurements , 2010, PloS one.

[25]  Jie Liang,et al.  Predicting Protein Function and Binding Profile via Matching of Local Evolutionary and Geometric Surface Patterns , 2009 .

[26]  Mika A. Kastenholz,et al.  GRID/CPCA: a new computational tool to design selective ligands. , 2000, Journal of medicinal chemistry.

[27]  G. Klebe,et al.  Knowledge-based scoring function to predict protein-ligand interactions. , 2000, Journal of molecular biology.

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

[29]  Philip E. Bourne,et al.  Drug Discovery Using Chemical Systems Biology: Repositioning the Safe Medicine Comtan to Treat Multi-Drug and Extensively Drug Resistant Tuberculosis , 2009, PLoS Comput. Biol..

[30]  Reinhard Klein,et al.  Shape retrieval using 3D Zernike descriptors , 2004, Comput. Aided Des..

[31]  G. Klebe,et al.  Unexpected nanomolar inhibition of carbonic anhydrase by COX-2-selective celecoxib: new pharmacological opportunities due to related binding site recognition. , 2004, Journal of medicinal chemistry.

[32]  Anna Vulpetti,et al.  Predicting Polypharmacology by Binding Site Similarity: From Kinases to the Protein Universe , 2010, J. Chem. Inf. Model..

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

[34]  D. J. Price,et al.  Assessing scoring functions for protein-ligand interactions. , 2004, Journal of medicinal chemistry.

[35]  Lora Mak,et al.  An extension of spherical harmonics to region-based rotationally invariant descriptors for molecular shape description and comparison. , 2008, Journal of molecular graphics & modelling.

[36]  Nikolaos Canterakis,et al.  3D Zernike Moments and Zernike Affine Invariants for 3D Image Analysis and Recognition , 1999 .

[37]  Renxiao Wang,et al.  Comparative evaluation of 11 scoring functions for molecular docking. , 2003, Journal of medicinal chemistry.

[38]  David J. Wild,et al.  Grand challenges for cheminformatics , 2009, J. Cheminformatics.

[39]  Scott Grandison,et al.  The Application of 3D Zernike Moments for the Description of "Model-Free" Molecular Structure, Functional Motion, and Structural Reliability , 2009, J. Comput. Biol..

[40]  A. Hopkins,et al.  The druggable genome , 2002, Nature Reviews Drug Discovery.

[41]  Bernard Pirard,et al.  Peroxisome Proliferator-Activated Receptors target family landscape: A chemometrical approach to ligand selectivity based on protein binding site analysis , 2003, J. Comput. Aided Mol. Des..

[42]  Holger Gohlke,et al.  DrugScorePPI webserver: fast and accurate in silico alanine scanning for scoring protein–protein interactions , 2010, Nucleic Acids Res..

[43]  S. Pickett,et al.  GRid-INdependent descriptors (GRIND): a novel class of alignment-independent three-dimensional molecular descriptors. , 2000, Journal of medicinal chemistry.

[44]  Gerrit Groenhof,et al.  GROMACS: Fast, flexible, and free , 2005, J. Comput. Chem..

[45]  Gerhard Klebe,et al.  Predicting binding modes, binding affinities and ‘hot spots’ for protein-ligand complexes using a knowledge-based scoring function , 2000 .

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

[47]  Holger Gohlke,et al.  Target flexibility: an emerging consideration in drug discovery and design. , 2008, Journal of medicinal chemistry.

[48]  Daisuke Kihara,et al.  Potential for Protein Surface Shape Analysis Using Spherical Harmonics and 3D Zernike Descriptors , 2009, Cell Biochemistry and Biophysics.

[49]  Gabriele Cruciani,et al.  A Common Reference Framework for Analyzing/Comparing Proteins and Ligands. Fingerprints for Liands and Proteins (FLAP): Theory and Application. , 2007 .

[50]  J. Thornton,et al.  Shape variation in protein binding pockets and their ligands. , 2007, Journal of molecular biology.