Umbrella Sampling MD Simulations for Retention Prediction in Peptide Reversed-phase Liquid Chromatography

Abstract

Reversed-phase liquid chromatography (RPLC) is an essential tool for separating complex mixtures such as proteolytic digests in bottom-up proteomics. There is growing interest in methods that can predict the RPLC retention behavior of peptides and other analytes. Already, existing algorithms provide excellent performance based on empirical rules or large sets of RPLC training data. Here we explored a new type of retention prediction strategy that relies on first-principles modeling of peptide interactions with a C18 stationary phase. We recently demonstrated that molecular dynamics (MD) simulations can provide atomistic insights into the behavior of peptides under RPLC conditions (Anal. Chem. 2023, 95, 3892). However, the current work found that it is problematic to use conventional MD data for retention prediction, evident from a poor correlation between experimental retention times and MD-generated “fraction bound” values. We thus turned to umbrella sampling MD, a complementary technique that has previously been applied to probe noncovalent contacts in other types of systems. By restraining the peptide dynamic motions at various positions inside a C18-lined pore, we determined the free energy of the system as a function of peptide-stationary phase distance. ΔG_binding values determined in this way under various mobile phase conditions were linearly correlated with experimental retention times of tryptic test peptides. This work opens retention prediction avenues for novel types of stationary and mobile phases, and for peptides (or other analytes) having arbitrary chemical properties, without the need for RPLC reference data. Umbrella sampling can be used as a stand-alone tool, or it may serve to enhance existing retention prediction algorithms.