Protein stability prediction by fine-tuning a protein language model on a mega-scale dataset.

ABSTRACT: Protein stability plays a crucial role in a variety of applications, such as food processing, therapeutics, and the identification of pathogenic mutations. Engineering campaigns commonly seek to improve protein stability, and there is a strong interest in streamlining these processes to enable rapid optimization of highly stabilized proteins with fewer iterations. In this work, we explore utilizing a mega-scale dataset to develop a protein language model optimized for stability prediction. ESMtherm is trained on the folding stability of 528k natural and de novo sequences derived from 461 protein domains and can accommodate deletions, insertions, and multiple-point mutations. We show that a protein language model can be fine-tuned to predict folding stability. ESMtherm performs reasonably o

SUBMITTER: Chu SKS 

PROVIDER: S-EPMC11293664 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature