Protein phenotype prediction moves past single sites: adding epistasis and amino acid clustering tops R² in 9 of 13 datasets
Quantitative phenotype prediction has mostly relied on how single positions correlate with a trait, which cannot capture several residues acting together. The new scheme adds second-order epistasis and position-specific amino acid clustering on top of a Monte Carlo position search, then fits a regression from sequence distances to phenotype distances. Across 13 datasets spanning temperatures, enzyme inhibition, myoglobin concentration and rhodopsin absorption wavelength, the AC-MC-E variant using all three components reached the highest R² in 9 datasets and ranked in the top three in all 13.