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Title: Single-sequence protein structure prediction using language models from deep-learning
Abstract: AlphaFold2 and related computational systems predict protein structure using deep learning and co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite high prediction accuracy achieved by these systems, challenges remain in (1) prediction of orphan and rapidly evolving proteins for which an MSA cannot be generated; (2) rapid exploration of designed structures; and (3) understanding the rules governing spontaneous polypeptide folding in solution. Here we report development of an end-to-end differentiable recurrent geometric network (RGN) that uses a protein language model (AminoBERT) to learn latent structural information from unaligned proteins. A linked geometric module compactly represents Cα backbone geometry in a translationally and rotationally invariant way. On average, RGN2 outperforms AlphaFold2 and RoseTTAFold on orphan proteins and classes of designed proteins while achieving up to a 106-fold reduction in compute time. These findings demonstrate the practical and theoretical strengths of protein language models relative to MSAs in structure prediction.
Paper - www.nature.com/articles/s4158...
Speakers: Nazim Bouatta - / nazimbouatta
Twitter Prudencio: / tossouprudencio
Twitter Therence: / therence_mtl
Twitter Cas: / cas_wognum
Twitter Valence Discovery: / valence_ai
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Chapters:
00:00 - Intro
12:11 - Overview of Machine Learning and Protein Modelling
20:05 - Overview of AlphaFold2: Strengths, Limitations and Remaining Challenges
29:08 - Introducing OpenFold
46:22 - RGN2 - Single Sequence and Language Model
55:11 - Q+A