Automated in silico EGFR Peptide Inhibitor Elongation using Self-evolving Peptide Algorithm

Abstract

Background: The vast diversity of peptide sequences may hinder the effectiveness of screening for potential peptide therapeutics as if searching for a needle in a haystack. This study aims to develop a new self-evolving peptide algorithm (SEPA), for easy virtual screening of small linear peptides (three to six amino acids) as potential therapeutic agents with the collaborative use of freely available software that can be run on any operating system equipped with a Bash scripting terminal. Mitogen-inducible Gene 6 (Mig6) protein, a cytoplasmic protein responsible for inhibition and regulation of epidermal growth factor receptor tyrosine kinase, was used to demonstrate the algorithm.

Objective: The objective is to propose a new method to discover potential novel peptide inhibitors via an automated peptide generation, docking and post-docking analysis algorithm that ranks short peptides by using essential hydrogen bond interaction between peptides and the target receptor.

Methods: A library of dockable dipeptides were first created using PyMOL, Open Babel and AutoDockTools, and docked into the target receptor using AutoDock Vina, automatically via a Bash script. The docked peptides were then ranked by hydrogen bond interaction-based thorough interaction analysis, where the top-ranked peptides were then elongated, docked, and ranked again. The process repeats until the user-defined peptide length is achieved.

Results: In the tested example, SEPA bash script was able to identify the tripeptide YYH ranked within top 20 based on the essential hydrogen bond interaction towards the essential amino acid residue ASP837 in the EGFR-TK receptor.

Conclusion: SEPA could be an alternative approach for the virtual screening of peptide sequences against drug targets.

Keywords: AutoDock Vina; SEPA; Self-evolving peptide algorithm; automated peptide elongation; docking; peptide inhibitor; thorough interaction analysis.