Abstract
<jats:p>Many peptides often do not have a single dominant structure. Instead, many remain disordered in water and fold when they encounter membranes or other chemical environments, a property that underlies diverse biological functions but is difficult to predict. Here we introduce ApexFold, a machine-learning framework that predicts how peptide secondary structure change across environments. ApexFold uses peptide sequence and features together with physicochemical descriptors of the surrounding medium to estimate the fractions of helical, β-like and disordered structure expected in each condition. Trained on circular dichroism measurements from 1,187 peptides assayed in water, co-solvents and membrane-mimicking micelles, ApexFold predicted solvent-induced structural shifts in independent peptide panels and outperformed static structure predictors that return a single conformation. These results show that peptide structural plasticity can be learned from sequence and environment, providing a way to prioritize peptides and experimental conditions before synthesis and structural characterization.</jats:p>