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Intrinsic dataset features drive mutational effect prediction by protein language models.

Luiz C Vieira¹, Sophia Lin¹, Claus O Wilke¹

¹Department of Integrative Biology, The University of Texas at Austin, Austin, TX, United States of America.

Biorxiv : the Preprint Server for Biology

|April 10, 2026

Summary

Protein language models (pLMs) show variable performance in predicting protein fitness landscapes. Dataset composition, particularly site variability, is more critical than model architecture for accurate mutational effect prediction.

Keywords:

Deep Mutational Scan Protein Language Model Transfer learning Variant Effect Prediction

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Area of Science:

Computational biology
Protein engineering
Machine learning in biology

Background:

Protein language models (pLMs) are increasingly used for predicting protein fitness landscapes.
Performance of pLMs varies significantly across different datasets, with reasons often unclear.

Purpose of the Study:

To investigate the factors influencing the performance of pLMs in predicting protein fitness landscapes.
To compare pLM performance on viral versus cellular deep-mutational-scanning (DMS) datasets.
To identify intrinsic dataset features that impact predictive accuracy.

Main Methods:

Evaluated supervised transfer learning using multiple pLMs on 41 viral and 33 cellular DMS datasets.
Analyzed dataset composition using metrics like relative variability of site means (RVSM) and fraction of highly variable sites (FHVS).
Compared pLM performance against a baseline model predicting site mean fitness and assessed data leakage by splitting training/test data by site.

Main Results:

Consistently lower predictive performance was observed on viral datasets compared to cellular datasets, irrespective of model or transfer learning strategy.
A simple baseline model predicting site mean fitness often matched or outperformed supervised pLMs.
Dataset variability metrics (RVSM, FHVS) explained differences between viral and cellular datasets and constrained model performance.
Supervised models frequently relied on site-specific effects rather than general mutational patterns, especially when data leakage was controlled.

Conclusions:

Dataset composition, particularly site variability, is a primary driver of pLM predictive success for mutational effects, not model architecture or training strategy.
Current pLMs have limitations in capturing critical site mutational constraints for fitness prediction.
Existing benchmarks may overestimate pLM performance due to data leakage and failure to account for site effects.