Meta-Learning for Cross-Task Generalization in Protein Mutation Property Prediction

TL;DR

This paper applies meta-learning with transformer models to improve cross-task generalization in protein mutation property prediction, enabling rapid adaptation and better performance across diverse datasets with limited data.

Contribution

It introduces the first use of MAML for protein mutation prediction and a novel mutation encoding strategy with separator tokens for enhanced sequence context integration.

Findings

Meta-learning improves accuracy by up to 29% across tasks.

Training efficiency is maintained regardless of dataset size.

Significant reduction in training time (up to 65%) with better generalization.

Abstract

Protein mutations can have profound effects on biological function, making accurate prediction of property changes critical for drug discovery, protein engineering, and precision medicine. Current approaches rely on fine-tuning protein-specific transformers for individual datasets, but struggle with cross-dataset generalization due to heterogeneous experimental conditions and limited target domain data. We introduce two key innovations: (1) the first application of Model-Agnostic Meta-Learning (MAML) to protein mutation property prediction, and (2) a novel mutation encoding strategy using separator tokens to directly incorporate mutations into sequence context. We build upon transformer architectures integrating them with MAML to enable rapid adaptation to new tasks through minimal gradient steps rather than learning dataset-specific patterns. Our mutation encoding addresses the critical limitation where standard transformers treat mutation positions as unknown tokens, significantly degrading performance. Evaluation across three diverse protein mutation datasets (functional fitness, thermal stability, and solubility) demonstrates significant advantages over traditional fine-tuning. In cross-task evaluation, our meta-learning approach achieves 29% better accuracy for functional fitness with 65% less training time, and 94% better accuracy for solubility with 55% faster training. The framework maintains consistent training efficiency regardless of dataset size, making it particularly valuable for industrial applications and early-stage protein design where experimental data is limited. This work establishes a systematic application of meta-learning to protein mutation analysis and introduces an effective mutation encoding strategy, offering transformative methodology for cross-domain generalization in protein engineering.