Characterizing Physicochemical Selection in Protein Evolution with Property-Informed Models (PRIME).

Standard probabilistic models of coding sequence evolution effectively identify where and when selection acts but remain agnostic to the mechanistic realization of these forces. We introduce PRIME (PRoperty Informed Models of Evolution), a framework of codon-level maximum likelihood methods-including global (G-PRIME), episodic (E-PRIME), and site-specific (S-PRIME) implementations-that explicitly model amino acid exchangeability as a function of physicochemical properties. By parameterizing attributes such as molecular volume, hydropathy, and secondary structure propensities, PRIME resolves the biophysical basis of selective constraint across both the sequence and the phylogeny. At the site level, S-PRIME leverages an explicit biophysical taxonomy to precisely categorize residues as conserved, neutral, or changing for specific properties, resolving selective signals that remain invisible to traditional rate-based metrics. Our analysis of a benchmark of 24 diverse datasets and a genome-wide screen of 18,944 mammalian genes demonstrates that biophysical realism yields substantial improvements in model fit, acting synergistically with rate variation to explain complex evolutionary patterns. We find that power to detect physicochemical constraints at individual sites is fundamentally governed by simple informational redundancy (substitutions per unique amino acid; AUC = 0.91), with sensitivity exceeding 90% in data-rich alignments. E-PRIME reveals a distinct biophysical hierarchy: while core packing and beta-sheet scaffolds are rigidly conserved, alpha-helix propensity and surface electrostatics serve as the primary substrates for adaptive tuning. Furthermore, PRIME importance weights align with aspects of the primary semantic axes of deep learning representations (ESM-2) and capture key features of experimental fitness landscapes. By transforming abstract evolutionary rates into interpretable biophysical rules, PRIME provides a useful framework for characterizing the mechanistic drivers of protein diversity.