PLIC: protein–ligand interaction clusters

Abstract

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

ProtCID: A data resource for structural information on protein interactions

“…Several servers analyze interfaces in either the asymmetric units and/or the deposited biological assemblies of PDB entries [6][7][8][9][10][11] , and some are intended to predict which interfaces may be biologically relevant by measuring conservation scores and physical features and using machine learning predictors [12][13][14] . Interactions with peptides, nucleic acids, and ligands have also been analyzed and presented in several webservers and databases 9,[15][16][17][18][19][20][21] .…”

Identification of new potent inhibitors of dengue virus NS3 protease from traditional Chinese medicine database

“…It is pivotal for complete comprehension of the molecular mechanisms of biological frameworks [1]. Five selected TCM compounds (eriodictyol 7-O-glucuronide, luteolin 8-C-beta-glucopyranoside, (-)-epicatechin-3-O-gallate, 6-O-trans-p-coumaroylgeniposide and luteolin-7-O-glucoside) were docked again into the receptor structure to form complex structures using MTiAutoDock server [14].…”

A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs)

“…However, the number of available binding site comparison methods is still growing [6] and it is becoming increasingly difficult to choose the most suitable method for a specific area of research. Although binding site similarities can now be retrieved from elaborate similarity databases [7][8][9][10], it is often advisable to perform additional comparisons, or it may even be necessary if proprietary structures are used. The importance of selecting an appropriate program and underlying binding site similarity metric is comprehensively summarized in an article by Kellenberger et al [11].…”