GAMPMS: Genetic algorithm managed peptide mutant screening.

Area of Science:

• Computational chemistry
• Molecular modeling
• Drug discovery

Background:

• Peptides are valuable molecular scaffolds for drug development due to their binding activity with receptors.
• Modifying peptide sequences can alter receptor binding, making mutant screening a key technique.
• Traditional screening methods face limitations due to the combinatorial explosion of peptide mutants.

Purpose of the Study:

• To develop and evaluate a computationally efficient method for screening peptide mutants.
• To identify optimal peptide binders for the nicotinic acetylcholine receptor (nAChR) α3β2-isoform.
• To reduce the computational burden of virtual screening in drug discovery.

Main Methods:

• Implementation of Genetic Algorithm Managed Peptide Mutant Screening (GAMPMS) for heuristic search.
• Exploration of 64,000 α-conotoxin (α-CTx) MII peptide mutants against the α3β2-nAChR binding domain.
• Comparison of GAMPMS performance against exhaustive virtual screening using AutoDock.

Main Results:

• GAMPMS significantly reduced the number of required AutoDock simulations (1140 out of 64,000).
• GAMPMS consistently identified top-performing peptides with aggregated binding energy within 98% of exhaustive screening.
• The method demonstrated high efficiency in exploring peptide mutation space.

Conclusions:

• GAMPMS offers a computationally efficient and effective approach for virtual screening of peptide mutants.
• This method accelerates the identification of potent peptide drug candidates.
• GAMPMS is a valuable tool for probing receptor-ligand binding domains and optimizing molecular scaffolds.