Evolutionary Profiles for Protein Fitness Prediction.

Abstract

Predicting the fitness impact of mutations is central to protein engineering but constrained by limited assays relative to the size of sequence space. Protein language models (pLMs) trained with masked language modeling (MLM) exhibit strong zero-shot fitness prediction;we provide a interpretive lens by regarding natural evolution as implicit reward maximization and MLM as inverse reinforcement learning (IRL), in which extant sequences act as expert demonstrations and pLM log-odds serve as fitness estimates. Building on this perspective, we introduce EvoIF, a lightweight model that integrates two complementary sources of evolutionary signal: (i) evolutionary profiles from retrieved homologs and (ii) inverse folding profiles distilled from inverse folding logits. EvoIF fuses sequence-structure representations with these profiles via a compact transition block, yielding calibrated probabilities for log-odds scoring. On ProteinGym (217 mutational assays; > 2.5M mutants), EvoIF and its MSA-enabled variant achieve competitive performance while using only 0.15% of the training data and fewer parameters than recent large models. Ablations confirm that evolutionary and inverse folding profiles are complementary, improving robustness across function types, MSA depths, taxa, and mutation depths. Code is archived on Zenodo at https://doi.org/10.5281/zenodo.20139484.