Enhancing predictions of protein stability changes induced by single mutations using MSA-based language models

Abstract

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 this problem, we introduce two key novelties: leveraging a protein language model that incorporates Multiple Sequence Alignments to capture evolutionary information, and using a recently released mega-scale dataset with rigorous data preprocessing to mitigate overfitting.

Authors

Francesca Cuturello, Marco Celoria, Alessio Ansuini, Alberto Cazzaniga

Journal

Bioinformatics, 2024, 40 (7)

Consult the paper