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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Alexander Rives, Siddharth Goyal, Joshua Meier, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, Rob Fergus
doi: https://doi.org/10.1101/622803
Alexander Rives
‡Dept. of Computer Science, New York University, USA
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  • For correspondence: arives@cs.nyu.edu maj@fb.com robfergus@fb.com
Siddharth Goyal
§Facebook AI Research, USA
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Joshua Meier
§Facebook AI Research, USA
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Demi Guo
§Facebook AI Research, USA
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Myle Ott
§Facebook AI Research, USA
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C. Lawrence Zitnick
§Facebook AI Research, USA
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Jerry Ma
§Facebook AI Research, USA
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  • For correspondence: arives@cs.nyu.edu maj@fb.com robfergus@fb.com
Rob Fergus
‡Dept. of Computer Science, New York University, USA
§Facebook AI Research, USA
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  • For correspondence: arives@cs.nyu.edu maj@fb.com robfergus@fb.com
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Abstract

In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major advances in representation learning and statistical generation. In biology, the anticipated growth of sequencing promises unprecedented data on natural sequence diversity. Learning the natural distribution of evolutionary protein sequence variation is a logical step toward predictive and generative modeling for biology. To this end we use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million sequences spanning evolutionary diversity. The resulting model maps raw sequences to representations of biological properties without labels or prior domain knowledge. The learned representation space organizes sequences at multiple levels of biological granularity from the biochemical to proteomic levels. Unsupervised learning recovers information about protein structure: secondary structure and residue-residue contacts can be identified by linear projections from the learned representations. Training language models on full sequence diversity rather than individual protein families increases recoverable information about secondary structure. The unsupervised models can be adapted with supervision from quantitative mutagenesis data to predict variant activity. Predictions from sequences alone are comparable to results from a state-of-the-art model of mutational effects that uses evolutionary and structurally derived features.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 29, 2019.
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Siddharth Goyal, Joshua Meier, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, Rob Fergus
bioRxiv 622803; doi: https://doi.org/10.1101/622803
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Siddharth Goyal, Joshua Meier, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, Rob Fergus
bioRxiv 622803; doi: https://doi.org/10.1101/622803

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