A General Harness for Protein Foundation Model Fitness Prediction

Accurate fitness prediction is central to protein engineering and understanding sequence–function relationships. With advances in deep learning, protein foundation models (PFMs) have become widely used for this task. Recent analyses, however, show that these models share preferences reflecting their training corpora, while unreliable inputs can further distort fitness predictions. Family-specific evolutionary evidence and structural context can help address these limitations by providing complementary constraints on model scores, motivating REM-Harness a general, model-agnostic, training-free Retrieval-Enhanced Mutation harness. It fuses frozen model scores with multiple sequence alignment (MSA) evidence according to model uncertainty, then applies gated background correction and score shrinkage based on structural confidence and solvent exposure. Across $1,211$ assays and $3.1$ million measured variants from ProteinGym, VenusMutHub, and the newly curated viral benchmark ViroHub, all $71$ configurations improve Spearman correlation on all $3$ benchmarks by $0.073$ on average, with broad gains across $5$ metrics. Extended analyses relate retrieval gains to model–MSA preference differences, assess domain-level gains and immune-escape cases, and quantify computational speedups. Built with RH, VenusREM2 is the first to rank highest in all function, taxon, MSA-depth, and mutation-depth categories, with a ProteinGym Average Spearman of $0.556$, $0.038$ above the prior best.