Hybrid protein language models for fitness prediction

Tuesday April 11th, 4-5 pm EST | Pascal Notin — PhD Candidate, University of Oxford

Abstract: The ability to accurately model the fitness landscape of protein sequences is critical to a wide range of applications, from quantifying the effects of human variants on disease likelihood, to predicting immune-escape mutations in viruses and designing novel biotherapeutic proteins. Deep generative models of protein sequences trained on multiple sequence alignments have been the most successful approaches so far to address these tasks. The performance of these methods is however contingent on the availability of sufficiently deep and diverse alignments for reliable training. Their potential scope is thus limited by the fact many protein families are hard, if not impossible, to align. Large language models trained on massive quantities of non-aligned protein sequences from diverse families address these problems and show potential to eventually bridge the performance gap.

Preprints: https://proceedings.mlr.press/v162/notin22a.html, https://www.biorxiv.org/content/10.1101/2022.12.07.519495v1

Website: https://www.pascalnotin.com/

Recording link: https://youtu.be/m0QVNWcRi8Y