C-P.02: Generalizable Protein Language Models for Complex Variant Prediction via Multi-Task Epistatic Learning
Understanding the impact of variants on protein stability and function is a core challenge in computational biology. Protein language models (PLMs) have demonstrated strong zero-shot capabilities in variant effect prediction. However, robust supervised fine-tuning across diverse functional readouts remains challenging. Furthermore, epistatic interactions, where the effect of a single mutation depends on another one, are rarely modeled and evaluated explicitly in PLM-based approaches, and multi-mutant training has usually focused on thermodynamic stability.
In this study, we present a multi-tasking fine-tuning framework for ESM-2 that integrates a Deep Mutational Scanning (DMS) pairwise ranking objective across multiple types of DMS assays with an auxiliary MLM objective that preserves ESM-2 language-modeling perplexity, supporting generalization capabilities. Rather than training separate prediction heads from scratch, we reuse the pretrained language-modeling head and learn lightweight task-specific matrices that bias the LM logits for each task. DMS scores are then computed as unmasked pseudo log-likelihoods of the sequences with the corresponding bias. To explicitly capture non-additivity, we introduce an epistatic loss term that evaluates mutational cycles to directly match predicted and experimental epistatic terms.
Benchmarking on held-out DMS assays, including assays containing INDELs, shows that our model achieves the best overall performance among the methods evaluated in ProteinGym. Performance gains are particularly pronounced on multi-mutant assays, where explicit epistasis training substantially improves correlation with experimentally derived specific epistasis effects, and also extend to unseen assay categories, including expression and activity assays.
Co-authors: Mattia Tenuti, Gianluca Lattanzi, Alessandro Romanel
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