  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.
