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

Abstract

Peptide therapeutics is proven to be highly potential in the treatment of various diseases due to its specificity, biological safety, and cost‐effectiveness. Many of the FDA‐approved peptides are currently available for therapeutic applications. In the current postgenomic era, high‐throughput computational screening of drugs and peptides are highly exploited in peptide therapeutics for cost‐effective and robustness. However, there is a paucity of efficient pipelines that automate virtual screening process of peptides through integration of open‐source tools that are optimal to perform ensemble and flexible docking protocols. Hence, in this study, we developed a GUI‐based pipeline named PepVis for automated script generation for large‐scale peptide modeling and virtual screening. PepVis integrates Modpep and Gromacs for peptide structure modeling and optimization; AutoDock Vina, ZDOCK, and AutoDock CrankPep for virtual screening of peptides; ZRANK2 for rescoring of protein–peptide complexes, and FlexPepDock for flexible refinement of protein–peptide complexes. Benchmarking of ensemble docking through PepVis infers that ModPep + Vina to outperform ModPep + ZDock in terms of detecting near‐natives from LEADS‐PEP dataset. PepVis is built modular to incorporate many other docking algorithms in the future. This pipeline is distributed freely under the GNU GPL license and can be downloaded at https://github.com/inpacdb/PepVis.

A Computer-Aided Approach for the Discovery of D-Peptides as Inhibitors of SARS-CoV-2 Main Protease

“…However, this approach, coined as de novo docking, is not suitable for larger peptides, as exhaustive conformational sampling becomes prohibitive due to the presence of many freely-rotatable covalent bonds (Yan et al, 2017;Ansar and Vetrivel, 2019). This hurdle can be potentially overcome by conducting ensemble docking, which involves the generation of peptide conformations that can be subsequently docked into the protein binding site through rigid docking (Yan et al, 2017;Ciemny et al, 2018;Zhou et al, 2018;Ansar and Vetrivel, 2019). Moreover, MD simulationbased refinement steps can improve the accuracy of docked peptide poses (Trellet et al, 2013;Schindler et al, 2015;Yan et al, 2016;Ciemny et al, 2018;Hashemi et al, 2021).…”

“…Despite the availability of multiple peptide/protein docking tools (Ciemny et al, 2018;Hashemi et al, 2021), their efficient integration into SBVS campaigns is not straightforward (Ansar and Vetrivel, 2019). Recently, Ansar and Vetrivel presented PepVis, a pipeline for peptide SBVS following an ensemble docking approach (Ansar and Vetrivel, 2019). Their pipeline involves the generation of multiple peptide conformations that are rigidly docked into the targets and subsequently rescored and refined.…”

“…Nonetheless, at least one work has reported two L-pentapeptides as potential 3CL pro inhibitors by screening a 70,000-peptide library (Porto, 2021), using AutoDock Vina for the docking simulations (Trott and Olson, 2010). Remarkably, AutoDock Vina outperformed other freely-available docking algorithms, such as AutoDock and ZDOCK (Chen and Weng, 2002;Morris et al, 2009), in a benchmark study that presented a pipeline for peptide SBVS (Ansar and Vetrivel, 2019).…”

Targeting Tumors Using Peptides

“…The virtual screening methods discussed so far make use of different structure determination and docking protocols, but not all of them are freely available. Ansar and coworkers [95] developed a graphical user interface-based pipeline that integrates different existent tools for performing the complete process of virtual screening of peptides. The pipeline is called PepVis, and it features both ensemble and flexible de novo docking protocols.…”

KinomeRun: An interactive utility for kinome target screening and interaction fingerprint analysis towards holistic visualization on kinome tree

“…Computer‐aided drug design has substantially reduced the time and cost of experiments involved in the drug development process (Ansar, Sadhasivam, & Vetrivel, 2019; Ansar & Vetrivel, 2019; Bajorath, 2015; Vetrivel, Arunkumar, & Dorairaj, 2007; Vetrivel & Subramanian, 2015; Vetrivel Umashankar, 2011a, 2011b). Target screening has been an integral part of the computational drug designing process.…”