3D-PP: a tool for discovering conserved 3D protein patterns

Most of drugs interact with more than one molecular target. This fact typically would be seen like as an undesired feature of a pharmacological treatment, however, current trends in drug discovery has put hope and several efforts in the improved efficiency and efficacy that have been showed by some promiscuous drugs. Indeed, several approaches for predict the polypharmacological profile of drugs have been recently developed. In this line, the structure of proteins has gained special interest. The structure of proteins is several times more conserved than their sequence. Moreover, even in those cases where a close evolutionary relationship exists between two proteins, it is possible that their global structures are not conserving, and only share partial three-dimensional (3D) patterns, which define in most cases, their biological functions. Interestingly, several tools have been developed for the identification of similar 3D patterns, however, usually demand a known query or only consider the observed data (e.g. orthosteric binding sites in PDB, annotated motif, known ligands, etc.). Nevertheless, some approaches shows that 3D amino acids conservation is a enough prove for consider these residues as part of an active site or a binding site of a protein structure, even when no prior knowledge of functional residues are available. Thus, considering all unknown or unobserved 3D patterns (e.g. allosteric binding sites), for the discovery, search and characterization of putative common binding sites between a set of protein structures, cold be more informative than explore only known sites. Here, we present 3D-PP, a new free access web server to discover all conserved 3D amino acid patterns among a set of protein structures including those coming from both, X-ray crystallographic experiments and in silico comparative modelling. The preprocessing modules of 3D-PP were developed in Python and all data generated are processed and organized automatically in a scalable high-performance graph database. References (1–5) 1. Anighoro A, Bajorath J, Rastelli G. Polypharmacology: Challenges and Opportunities in Drug Discovery. J Med Chem [Internet]. 2014;dx.doi.org/10.1021/jm5006463. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24946140 2. Konc J, Janežic D. Binding site comparison for function prediction and pharmaceutical discovery. Curr Opin Struct Biol [Internet]. 2014 Apr [cited 2014 Sep 3];25:34–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24878342 3. Nadzirin N, Gardiner EJ, Willett P, Artymiuk PJ, Firdaus-Raih M. SPRITE and ASSAM: Web servers for side chain 3D-motif searching in protein structures. Nucleic Acids Res. 2012;40(W1). 4. Martinez-bazan N, Muntes-mulero V, Gomez-villamor S. DEX : High-Performance Exploration on Large Graphs for Information Retrieval. Artif Intell [Internet]. 2007;573–82. Available from: http://portal.acm.org/citation.cfm?doid=1321440.1321521 5. Nunez-Vivanco G, Valdes-Jimenez A, Besoain F, Reyes-Parada M. Geomfinder: A multi-feature identifier of similar three-dimensional protein patterns: A ligand-independent approach. J Cheminform. 2016;8(1).