Hierarchical Flexible Peptide Docking by Conformer Generation and Ensemble Docking of Peptides

Abstract

Given the importance of peptide-mediated protein interactions in cellular processes, protein−peptide docking has received increasing attention. Here, we have developed a Hierarchical flexible Peptide Docking approach through fast generation and ensemble docking of peptide conformations, which is referred to as HPepDock. Tested on the LEADS-PEP benchmark data set of 53 diverse complexes with peptides of 3−12 residues, HPepDock performed significantly better than the 11 docking protocols of five small-molecule docking programs (DOCK, AutoDock, Auto-Dock Vina, Surflex, and GOLD) in predicting near-native binding conformations. HPepDock was also evaluated on the 19 bound/unbound and 10 unbound/unbound protein− peptide complexes of the Glide SP-PEP benchmark and showed an overall better performance than Glide SP-PEP+MM-GBSA and FlexPepDock in both bound and unbound docking. HPepDock is computationally efficient, and the average running time for docking a peptide is ∼15 min with the range from about 1 min for short peptides to around 40 min for long peptides.

PepVis: An integrated peptide virtual screening pipeline for ensemble and flexible docking protocols

“…The data tabulated for ModPep + Vina, ModPep + Vina+ZRANK2, ModPep + ZDOCK, and ModPep + ZDOCK+ZRANK2 are from this present study, whereas the other data were cited from earlier studies (Hauser & Windshügel, ; Zhou, Li, et al, ; Tables S1–S4). Among the freely available and offline tools, ModPep + Vina was found to be a better combination, as it showed a median RMSD of 6.1 Å with LEADS‐PEP complexes when only the top best pose was considered (Table S1), while others showed a higher median RMSD.…”

“…In addition, results of similar works reported earlier, wherein LEADS‐PEP datasets (Hauser & Windshügel, ) were modeled and validated using different modes with different docking tools were also cotabulated for comparative analysis. These data include outputs from Autodock, Vina, Surflex, GOLD, DOCK, and HPepDock, wherein the peptides in case of DOCK and HPepDock were modeled using ensemble docking approach using ModPep, as per an earlier study (Zhou, Li, et al, ). The LEADS‐PEP benchmarking dataset with ADCP was not compared in this study, as our benchmarking is primarily aimed at Vina‐based ensemble and flexible docking protocols.…”

“…The peptides for all the 53 protein–peptide complexes in LEADS‐PEP datasets were modeled based on the protocol as described in an earlier study (Zhou, Li, et al, ). To start with, the peptide sequences were given as input to PSIPRED for secondary structure prediction, based on which the 3D structures were predicted using ModPep, wherein 1,000 conformations per peptide were generated.…”

“…Molecular docking of peptides can be further categorized as ensemble and de novo docking. In ensemble docking, the problem of conformational peptide sampling is overcome by sampling the peptide conformations in a predefined manner and docked rigidly with the receptor (Yan, Xu, & Zou, ; Zhou, Jin, Li, & Huang, ; Zhou, Li, et al, ). In case of de novo docking, peptide conformations are sampled on the fly and docked with the receptor, and thus are a computationally expensive process due to large number of rotatable bonds in peptides unlike small molecules.…”

Recognition of gluconeogenic enzymes; Icl1, Fbp1, and Mdh2 by Gid4 ligase: A molecular docking study

“…HPEPDOCK is a newly released (in the year 2018) web‐based software used to blindly dock peptides into proteins using a hierarchical algorithm . MODPEP is utilized by the software to generate ensemble conformations of the peptide and perform docking using each conformation . For the top 10 conformations, HPEPDOCK gets a success rate of 72.6% compared to the well‐established HADDOCK (success rate 45.2% for peptide‐protein docking protocol) .…”