ClusPro PeptiDock: efficient global docking of peptide recognition motifs using FFT.

Kathryn A Porter,Bing Xia,Nawsad Alam,Dmitri Beglov,Ora Schueler-Furman,Tanggis Bohnuud,Dima Kozakov,Alfonso Valencia

doi:10.1093/bioinformatics/btx216

Abstract

SummaryWe present an approach for the efficient docking of peptide motifs to their free receptor structures. Using a motif based search, we can retrieve structural fragments from the Protein Data Bank (PDB) that are very similar to the peptide’s final, bound conformation. We use a Fast Fourier Transform (FFT) based docking method to quickly perform global rigid body docking of these fragments to the receptor. According to CAPRI peptide docking criteria, an acceptable conformation can often be found among the top-ranking predictions.Availability and ImplementationThe method is available as part of the protein-protein docking server ClusPro at https://peptidock.cluspro.org/nousername.php.Supplementary information Supplementary data are available at Bioinformatics online.

Highlights

Fast Fourier Transform (FFT) based sampling approaches have been shown to be very effective for modeling protein–protein interactions
If we assume that proteins undergo minimal conformational changes upon binding, we can exhaustively sample mutual protein orientations using a molecular mechanics like energy function in an efficient manner (Chen et al, 2003; Kozakov et al, 2006; Tovchigrechko and Vakser, 2006; Viswanath et al, 2014). This assumption has been adequate in the Critical Assessment of PRediction of Interactions (CAPRI), a community wide docking experiment (Lensink et al, 2016) where FFT based approaches have consistently been among the top performers (Lensink and Wodak, 2013)
While these approaches were based on sequence and secondary structure similarity, we use motif based fragment extraction to focus on a relevant subset of conformations, taking advantage of the specific features of the motif docking challenge. By combining this resulting fragment library with systematic Fast Fourier Transform (FFT) grid based sampling using accurate molecular mechanics potentials (Kozakov et al, 2006, 2014), we efficiently sample and discriminate near native docked peptide models with success rates similar to domain–domain protein docking, despite significant peptide flexibility. We demonstrate this on a diverse set of domain–motif interactions, and make the method freely available as part of the protein–protein docking server ClusPro

Summary

Introduction

Fast Fourier Transform (FFT) based sampling approaches have been shown to be very effective for modeling protein–protein interactions. By combining this resulting fragment library with systematic Fast Fourier Transform (FFT) grid based sampling using accurate molecular mechanics potentials (Kozakov et al, 2006, 2014), we efficiently sample and discriminate near native docked peptide models with success rates similar to domain–domain protein docking, despite significant peptide flexibility We demonstrate this on a diverse set of domain–motif interactions, and make the method freely available as part of the protein–protein docking server ClusPro. 2 Materials and methods (1) Preparing the input structures: The structure of the free receptor is represented as an independent binding unit that is defined as either a single domain, or repeated, non-decomposable domains (Lavi et al, 2013). Overview of the protocol is presented in Supplementary Figure S1

Docking performance

Findings

Conclusions