PepSite: prediction of peptide-binding sites from protein surfaces

Abstract

Complex biological functions emerge through intricate protein–protein interaction networks. An important class of protein–protein interaction corresponds to peptide-mediated interactions, in which a short peptide stretch from one partner interacts with a large protein surface from the other partner. Protein–peptide interactions are typically of low affinity and involved in regulatory mechanisms, dynamically reshaping protein interaction networks. Due to the relatively small interaction surface, modulation of protein–peptide interactions is feasible and highly attractive for therapeutic purposes. Unfortunately, the number of available 3D structures of protein–peptide interfaces is very limited. For typical cases where a protein–peptide structure of interest is not available, the PepSite web server can be used to predict peptide-binding spots from protein surfaces alone. The PepSite method relies on preferred peptide-binding environments calculated from a set of known protein–peptide 3D structures, combined with distance constraints derived from known peptides. We present an updated version of the web server that is orders of magnitude faster than the original implementation, returning results in seconds instead of minutes or hours. The PepSite web server is available at http://pepsite2.russelllab.org.

Fully Blind Peptide-Protein Docking with pepATTRACT

“…Information about the native binding site can be obtained from experiments (Acharya et al, 2014;Clarke et al, 2011) and easily included during the docking process to generate high-quality complex structures. If experimental data are unavailable, bioinformatic prediction tools could be used to identify possible binding sites (Ben-Shimon and Eisenstein, 2010; Dundas et al, 2006;Lavi et al, 2013;Petsalaki et al, 2009;Saladin et al, 2014;Trabuco et al, 2012;Verschueren et al, 2013). As a special case of bioinformatic prediction, the contacts from the best pepATTRACT models can be extracted as an interface post-prediction (see the section on Binding Site Prediction).…”

“…A number of peptide-protein docking and binding site prediction tools have been developed to date (Antes, 2010;Bordner and Abagyan, 2006;Dagliyan et al, 2011;Donsky and Wolfson, 2011;Dundas et al, 2006;Heté nyi and van der Spoel, 2002;Lavi et al, 2013;Luitz and Zacharias, 2014;Niv and Weinstein, 2005;Petsalaki et al, 2009;Raveh et al, 2011;Rosenfeld et al, 1995;Saladin et al, 2014;Staneva and Wallin, 2009;Trabuco et al, 2012;Unal et al, 2010;Verschueren et al, 2013). Global docking and binding site prediction methods (Ben-Shimon and Eisenstein, 2010;Dagliyan et al, 2011;Dundas et al, 2006;Lavi et al, 2013;Petsalaki et al, 2009;Saladin et al, 2014;Trabuco et al, 2012;Verschueren et al, 2013) often identify the correct binding site but do not yield high-quality models for the peptide conformation (London et al, 2013b). The ligand docking approach Autodock was adapted to fully blind peptide docking, but is limited to short fragments (Heté nyi and van der Spoel, 2002;London et al, 2013b;Unal et al, 2010).…”

Antioxidative and Amylase Inhibitor Peptides from Basil Seeds

“…The docking analysis was performed using Pepsite-beta system (Trabuco et al 2012). Porcine pancreatic a-amylase (according to RCSB Protein Data Bank ID number: 1PIG) was used as enzyme model.…”

Role of computer-aided drug design in modern drug discovery

“…The binding site of a small molecule compound can be predicted using available tools such as (Somarowthu and Ondrechen 2012), and many others. Alternatively, specific approaches that can identify peptide binding sites include PepSite (Trabuco et al 2012), PeptiMap (Lavi et al 2013), and PEPSiteFinder (Saladin et al 2014). Lastly, difficulties in detecting allosteric sites can be alleviated by recently developed open-access web servers such as SPACER (Goncearenco et al 2013), MCPath (Kaya et al 2013), Allosite (Huang et al 2013), and PARS (Panjkovich and Daura 2014).…”