GAMPMS: Genetic algorithm managed peptide mutant screening. Issue 17 (15th May 2015) - Long, Thomas; McDougal, Owen M.; Andersen, Tim
Abstract : The prominence of endogenous peptide ligands targeted to receptors makes peptides with the desired binding activity good molecular scaffolds for drug development. Minor modifications to a peptide's primary sequence can significantly alter its binding properties with a receptor, and screening collections of peptide mutants is a useful technique for probing the receptor–ligand binding domain. Unfortunately, the combinatorial growth of such collections can limit the number of mutations which can be explored using structure‐based molecular docking techniques. Genetic algorithm managed peptide mutant screening (GAMPMS) uses a genetic algorithm to conduct a heuristic search of the peptide's mutation space for peptides with optimal binding activity, significantly reducing the computational requirements of the virtual screening. The GAMPMS procedure was implemented and used to explore the binding domain of the nicotinic acetylcholine receptor (nAChR) α 3 β 2 ‐isoform with a library of 64, 000 α ‐conotoxin ( α ‐CTx) MII peptide mutants. To assess GAMPMS's performance, it was compared with a virtual screening procedure that used AutoDock to predict the binding affinity of each of the α ‐CTx MII peptide mutants with the α 3 β 2 ‐nAChR. The GAMPMS implementation performed AutoDock simulations for as few as 1140 of the 64, 000 α ‐CTx MII peptide mutants and could consistently identify a set of 10 peptides with an aggregated binding energy that was at least 98% of the aggregated binding energy of the 10 top peptides from the exhaustive AutoDock screening. © 2015 Wiley Periodicals, Inc. Abstract : Genetic algorithm managed peptide mutant screening (GAMPMS) uses a genetic algorithm (GA) to perform a heuristic, structure‐based virtual screening of a peptide mutant library. The GA uses a set of operators and a fitness evaluator (e.g., AutoDock) to iteratively identify groups of peptide mutants that are likely to have a relatively high binding affinity with the target receptor. GAMPMS is able to dramatically reduce the time and resources required for virtually screening peptide mutant libraries.