AutoDock CrankPep: combining folding and docking to predict protein–peptide complexes

Abstract

Motivation Protein–peptide interactions mediate a wide variety of cellular and biological functions. Methods for predicting these interactions have garnered a lot of interest over the past few years, as witnessed by the rapidly growing number of peptide-based therapeutic molecules currently in clinical trials. The size and flexibility of peptides has shown to be challenging for existing automated docking software programs. Results Here we present AutoDock CrankPep or ADCP in short, a novel approach to dock flexible peptides into rigid receptors. ADCP folds a peptide in the potential field created by the protein to predict the protein–peptide complex. We show that it outperforms leading peptide docking methods on two protein–peptide datasets commonly used for benchmarking docking methods: LEADS-PEP and peptiDB, comprised of peptides with up to 15 amino acids in length. Beyond these datasets, ADCP reliably docked a set of protein–peptide complexes containing peptides ranging in lengths from 16 to 20 amino acids. The robust performance of ADCP on these longer peptides enables accurate modeling of peptide-mediated protein–protein interactions and interactions with disordered proteins. Availability and implementation ADCP is distributed under the LGPL 2.0 open source license and is available at http://adcp.scripps.edu. The source code is available at https://github.com/ccsb-scripps/ADCP. Supplementary information Supplementary data are available at Bioinformatics online.

Mining peptides against SARS-CoV-2 entry from constructed RBMs-hACE2 isomer libraries using machine learning and molecular docking

“…These demonstrations raised a hypothesis that the isomer pools established from wild type templates could reserve a certain number of candidates retaining desired target specificity, binding affinity, distribution properties and stability. This was finally verified by mining constructed isomer libraries using a SVM classifier and peptide docking [34][35][36] (Figure 3…”

“…These efforts along with recent studies have offered a great hope for discovery of the peptide-based prophylactic and therapeutic agents against COVID-19 [11][12][13][14]31,32 , however, starting from a few potential templates, in silico peptide design and properties prediction across binding affinity, specificity, stability and membrane permeability are still very challenging. Herein a novel design strategy was introduced by mining constructed RBMs-hACE2 isomer libraries using feature filters, supervised classifier and peptide-protein docking [34][35][36] .…”

AutoGridFR: Improvements on AutoDock Affinity Maps and Associated Software Tools

“…Reasonable end points for this vector are such that the ligand is not buried deeply inside the receptor or located too far from it to interact. Such a set of points can be specified in the target file and will be used by AutoDockFR [17] and AutoDock CrankPep [18] to sample the position of the ligand more efficiently during docking. The points from the fills identified by AutoSite 1.0 offer a reasonable set of such points.…”

Modeling Electrostatic Force in Protein-Protein Recognition

“…Macromolecular interactions, in particular the protein-protein interactions, have been studied by many labs, both computationally and experimentally (Bagher et al, 2019; Bolla et al, 2019; Dholey et al, 2019; Ferreira et al, 2019). Some works have focused on the dynamics associated with binding, e.g., de-hydration (Ferrario and Pleiss, 2019; Gao et al, 2019; Mishra et al, 2019) and, others have worked on predicting the binding mode via various docking or homology based techniques (Hwang et al, 2017; Meyer et al, 2018; Agrawal et al, 2019; Porter et al, 2019; Wang and Dokholyan, 2019; Zhang and Sanner, 2019; Zheng et al, 2019). Of particular interest to our work are the computational investigations of Zhou (1997), Alsallaq and Zhou (2008a,b), and Pang et al (2012) on modeling association rates of macromolecular binding.…”