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Poster
MSA Transformer
Roshan Rao · Jason Liu · Robert Verkuil · Joshua Meier · John Canny · Pieter Abbeel · Tom Sercu · Alexander Rives

Tue Jul 20 09:00 AM -- 11:00 AM (PDT) @
in Poster Session 1 »

Unsupervised protein language models trained across millions of diverse sequences learn structure and function of proteins. Protein language models studied to date have been trained to perform inference from individual sequences. The longstanding approach in computational biology has been to make inferences from a family of evolutionarily related sequences by fitting a model to each family independently. In this work we combine the two paradigms. We introduce a protein language model which takes as input a set of sequences in the form of a multiple sequence alignment. The model interleaves row and column attention across the input sequences and is trained with a variant of the masked language modeling objective across many protein families. The performance of the model surpasses current state-of-the-art unsupervised structure learning methods by a wide margin, with far greater parameter efficiency than prior state-of-the-art protein language models.

Keywords: Variational Inference · Deep Learning -> Deep Autoencoders; Deep Learning -> Generative Models; Probabilistic Methods; Probabilistic Methods · Graphic · Computational Biology and Bioinformatics

Author Information

Roshan Rao (UC Berkeley)
Jason Liu (Facebook AI Reseach)
Robert Verkuil (Facebook AI Research)
Joshua Meier (Facebook AI Research)
John Canny (UC Berkeley)
Pieter Abbeel (UC Berkeley & Covariant)
Tom Sercu (Facebook AI Research)
Alexander Rives (NYU)

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