Efficiently Predicting Protein Stability Changes Upon Single-point Mutation with Large Language Models

TL;DR

This paper presents an efficient method using Large Language Models to predict protein stability changes caused by single-point mutations, addressing feature complexity and data scarcity issues in protein research.

Contribution

The study introduces an ESM-assisted approach combining sequence and structural features for improved protein stability prediction, with curated datasets for fair evaluation.

Findings

Achieved high prediction accuracy with computational efficiency.

Curated datasets prevent data leakage and enable fair comparisons.

Demonstrated effectiveness of LLMs in protein stability prediction.

Abstract

Predicting protein stability changes induced by single-point mutations has been a persistent challenge over the years, attracting immense interest from numerous researchers. The ability to precisely predict protein thermostability is pivotal for various subfields and applications in biochemistry, including drug development, protein evolution analysis, and enzyme synthesis. Despite the proposition of multiple methodologies aimed at addressing this issue, few approaches have successfully achieved optimal performance coupled with high computational efficiency. Two principal hurdles contribute to the existing challenges in this domain. The first is the complexity of extracting and aggregating sufficiently representative features from proteins. The second refers to the limited availability of experimental data for protein mutation analysis, further complicating the comprehensive evaluation of model performance on unseen data samples. With the advent of Large Language Models(LLM), such as the ESM models in protein research, profound interpretation of protein features is now accessibly aided by enormous training data. Therefore, LLMs are indeed to facilitate a wide range of protein research. In our study, we introduce an ESM-assisted efficient approach that integrates protein sequence and structural features to predict the thermostability changes in protein upon single-point mutations. Furthermore, we have curated a dataset meticulously designed to preclude data leakage, corresponding to two extensively employed test datasets, to facilitate a more equitable model comparison.