Lipidomic Profile Reconstruction of TherapeuticMembrane Targets Using Physics-BasedOptimization with Limited Activity Data

Abstract

Membrane-targeting peptide motifs recognize characteristic features of biological membrane targets through specific interactions with their lipid composition. To elucidate the complex relationship between binding sequences and lipid composition, we propose an alternative computational framework to leverage limited sequence activity data by combining genetic algorithms with coarse-grained molecular dynamics simulations. We demonstrate through evolutionary optimization of plasma membrane lipid compositions how four true positive and four false positive antimicrobial peptide sequences encode sufficient information to resolve model membranes with a discriminative affinity of peptides that matches the accuracy of state-of-the-art antimicrobial classification. This finding reveals how subtle differences in lipid composition precisely control the selective binding of membrane-associated proteins, thereby regulating their trafficking, aggregation, and function within living cells. Our methodology reconstructs lipidomic profiles from limited sequence data, uniquely revealing targeting patterns for therapeutic peptides and the selectivity mechanisms of membrane-associated proteins.