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

Area of Science:

• Computational chemistry
• Drug discovery
• Bioinformatics

Background:

• Screening diverse peptide sequences for therapeutics is challenging.
• The self-evolving peptide algorithm (SEPA) facilitates virtual screening of short linear peptides (3-6 amino acids).
• This method utilizes freely available software compatible with any operating system with a Bash terminal.

Purpose of the Study:

• To develop an automated algorithm for discovering novel peptide inhibitors.
• To rank short peptides based on essential hydrogen bond interactions with target receptors.
• To demonstrate SEPA's utility using the Mitogen-inducible Gene 6 (Mig6) protein and EGFR-TK.

Main Methods:

• A peptide library was generated using PyMOL, Open Babel, and AutoDockTools.
• Peptides were docked to the target receptor using AutoDock Vina via a Bash script.
• Ranking was based on hydrogen bond interactions, with iterative elongation, docking, and re-ranking.

Main Results:

• The SEPA Bash script identified the tripeptide YYH.
• YYH was ranked within the top 20 based on essential hydrogen bond interactions.
• The interaction was specifically with the ASP837 residue in the EGFR-TK receptor.

Conclusions:

• SEPA offers a viable alternative for virtual screening of peptide sequences.
• The algorithm streamlines the discovery of peptide-based drug candidates.
• This approach enhances the efficiency of identifying therapeutic peptides.