Enhancing predictions of protein stability changes induced by single mutations using MSA-based Language Models

Protein Language Models offer a new perspective for addressing challenges in structural biology, while relying solely on sequence information. Recent studies have investigated their effectiveness in forecasting shifts in thermodynamic stability caused by single amino acid mutations, a task known for its complexity due to the sparse availability of data, constrained by experimental limitations. To tackle the problem, we fine-tune various pre-trained models using a recently released mega-scale dataset. Our approach employs a stringent policy to reduce the widespread issue of overfitting, by removing sequences from the training set when they exhibit significant similarity with the test set. The MSA Transformer emerges as the most accurate among the models under investigation, given its capability to leverage co-evolution signals encoded in aligned homologous sequences. Moreover, the optimized MSA Transformer outperforms existing methods and exhibits enhanced generalization power, leading to a notable improvement in predicting changes in protein stability resulting from point mutations. The code and data associated with this study are available at https://github.com/marco-celoria/PLM4Muts .