PLIC: protein–ligand interaction clusters

Most of the biological processes are governed through specific protein–ligand interactions. Discerning different components that contribute toward a favorable protein– ligand interaction could contribute significantly toward better understanding protein function, rationalizing drug design and obtaining design principles for protein engineering. The Protein Data Bank (PDB) currently hosts the structure of ∼68 000 protein–ligand complexes. Although several databases exist that classify proteins according to sequence and structure, a mere handful of them annotate and classify protein–ligand interactions and provide information on different attributes of molecular recognition. In this study, an exhaustive comparison of all the biologically relevant ligand-binding sites (84 846 sites) has been conducted using PocketMatch: a rapid, parallel, in-house algorithm. PocketMatch quantifies the similarity between binding sites based on structural descriptors and residue attributes. A similarity network was constructed using binding sites whose PocketMatch scores exceeded a high similarity threshold (0.80). The binding site similarity network was clustered into discrete sets of similar sites using the Markov clustering (MCL) algorithm. Furthermore, various computational tools have been used to study different attributes of interactions within the individual clusters. The attributes can be roughly divided into (i) binding site characteristics including pocket shape, nature of residues and interaction profiles with different kinds of atomic probes, (ii) atomic contacts consisting of various types of polar, hydrophobic and aromatic contacts along with binding site water molecules that could play crucial roles in protein–ligand interactions and (iii) binding energetics involved in interactions derived from scoring functions developed for docking. For each ligand-binding site in each protein in the PDB, site similarity information, clusters they belong to and description of site attributes are provided as a relational database—protein–ligand interaction clusters (PLIC). Database URL: http://proline.biochem.iisc.ernet.in/PLIC

[1]  Keunwan Park,et al.  Binding similarity network of ligand , 2008, Proteins.

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

[3]  Yasuo Tabei,et al.  PoSSuM: a database of similar protein–ligand binding and putative pockets , 2011, Nucleic Acids Res..

[4]  David Baker,et al.  Computational design of ligand-binding proteins with high affinity and selectivity , 2013, Nature.

[5]  Gregory D. Schuler,et al.  Database resources of the National Center for Biotechnology Information: update , 2004, Nucleic acids research.

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

[7]  R N Maini,et al.  Infliximab and methotrexate in the treatment of rheumatoid arthritis. Anti-Tumor Necrosis Factor Trial in Rheumatoid Arthritis with Concomitant Therapy Study Group. , 2000, The New England journal of medicine.

[8]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[9]  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..

[10]  A. Petroianu,et al.  Modified therapy with 5‐fluorouracil, doxorubicin, and methotrexate in advanced gastric cancer , 1993, Cancer.

[11]  Kalidas Yeturu,et al.  PocketAlign A Novel Algorithm for Aligning Binding Sites in Protein Structures , 2011, J. Chem. Inf. Model..

[12]  Renxiao Wang,et al.  The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. , 2004, Journal of medicinal chemistry.

[13]  E. Birney,et al.  Pfam: the protein families database , 2013, Nucleic Acids Res..

[14]  Dusanka Janezic,et al.  ProBiS-2012: web server and web services for detection of structurally similar binding sites in proteins , 2012, Nucleic Acids Res..

[15]  Gerhard Klebe,et al.  Relibase: design and development of a database for comprehensive analysis of protein-ligand interactions. , 2003, Journal of molecular biology.

[16]  Roman A. Laskowski,et al.  LigPlot+: Multiple Ligand-Protein Interaction Diagrams for Drug Discovery , 2011, J. Chem. Inf. Model..

[17]  Yang Zhang,et al.  BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions , 2012, Nucleic Acids Res..

[18]  Cathy H. Wu,et al.  UniProt: the Universal Protein knowledgebase , 2004, Nucleic Acids Res..

[19]  L. Holm,et al.  The Pfam protein families database , 2005, Nucleic Acids Res..

[20]  Didier Rognan,et al.  sc-PDB: a database for identifying variations and multiplicity of 'druggable' binding sites in proteins , 2011, Bioinform..

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

[22]  W. Bleyer.  The clinical pharmacology of methotrexate. new applications of an old drug , 1978, Cancer.

[23]  Maria Jesus Martin,et al.  SIFTS: Structure Integration with Function, Taxonomy and Sequences resource , 2012, Nucleic Acids Res..

[24]  Ziding Zhang,et al.  Similarity networks of protein binding sites , 2005, Proteins.

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

[26]  Ruben Abagyan,et al.  Pocketome: an encyclopedia of small-molecule binding sites in 4D , 2011, Nucleic Acids Res..

[27]  Tom L. Blundell,et al.  CREDO: a structural interactomics database for drug discovery , 2013, Database J. Biol. Databases Curation.

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

[29]  D S Goodsell,et al.  Automated docking of flexible ligands: Applications of autodock , 1996, Journal of molecular recognition : JMR.

[30]  Haruki Nakamura,et al.  Comprehensive structural classification of ligand-binding motifs in proteins. , 2008, Structure.

[31]  Anna Maria Gallina,et al.  PLI: a web-based tool for the comparison of protein-ligand interactions observed on PDB structures , 2013, Bioinform..

[32]  Chris Morley,et al.  Open Babel: An open chemical toolbox , 2011, J. Cheminformatics.

[33]  Richard D. Smith,et al.  Binding MOAD, a high-quality protein–ligand database , 2007, Nucleic Acids Res..

[34]  Stijn van Dongen,et al.  Using MCL to extract clusters from networks. , 2012, Methods in molecular biology.

[35]  Xin Wen,et al.  BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities , 2006, Nucleic Acids Res..

[36]  Jaime Prilusky,et al.  SPACE: a suite of tools for protein structure prediction and analysis based on complementarity and environment , 2005, Nucleic Acids Res..

[37]  Alex Bateman,et al.  The InterPro database, an integrated documentation resource for protein families, domains and functional sites , 2001, Nucleic Acids Res..

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

[39]  Ian Sillitoe,et al.  Extending CATH: increasing coverage of the protein structure universe and linking structure with function , 2010, Nucleic Acids Res..