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End-to-end differentiable learning of protein structure

View ORCID ProfileMohammed AlQuraishi
doi: https://doi.org/10.1101/265231
Mohammed AlQuraishi
1Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115.
2Department of Systems Biology, Harvard Medical School, Boston, MA 02115.
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  • ORCID record for Mohammed AlQuraishi
  • For correspondence: alquraishi@hms.harvard.edu
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Abstract

Predicting protein structure from sequence is a central challenge of biochemistry. Co‐evolution methods show promise, but an explicit sequence‐to‐structure map remains elusive. Advances in deep learning that replace complex, human‐designed pipelines with differentiable models optimized end‐to‐end suggest the potential benefits of similarly reformulating structure prediction. Here we report the first end‐to‐end differentiable model of protein structure. The model couples local and global protein structure via geometric units that optimize global geometry without violating local covalent chemistry. We test our model using two challenging tasks: predicting novel folds without co‐evolutionary data and predicting known folds without structural templates. In the first task the model achieves state‐of‐the‐art accuracy and in the second it comes within 1‐2Å; competing methods using co‐evolution and experimental templates have been refined over many years and it is likely that the differentiable approach has substantial room for further improvement, with applications ranging from drug discovery to protein design.

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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 August 29, 2018.
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End-to-end differentiable learning of protein structure
Mohammed AlQuraishi
bioRxiv 265231; doi: https://doi.org/10.1101/265231
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End-to-end differentiable learning of protein structure
Mohammed AlQuraishi
bioRxiv 265231; doi: https://doi.org/10.1101/265231

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