MIA: John Ingraham, Learning protein structure with a differentiable simulator





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Published on Apr 11, 2018

Models, Inference and Algorithms
Broad Institute of MIT and Harvard
April 11th, 2018

MIA Meeting: https://www.youtube.com/watch?v=R20_s...

John Ingraham
Marks Lab, HMS

Learning protein structure with a differentiable simulator

Abstract: While the problem of predicting protein structure from sequence is among the oldest in computational biology, current methods leave a significant fraction of the protein universe out of reach. Standard methodology involves two steps: (1) defining an energy landscape, whether with physics, statistics, or homology, and (2) sampling low-energy conformations. Often, even "correct” energy landscapes that assign the lowest energy to the correct structure will not generate it as a prediction, because the conformational sampling algorithm cannot find it. We have been developing an alternative approach to bridge this gap by directly training energy landscapes in tandem with the conformational sampling algorithms that operate on them. I will talk about this approach, backpropagation through simulators in general, and how we built a deep neural energy function that is trained by backpropagating through the *entire* protein folding process.

For more information on the Broad Institute and Models, Inference and Algorithms visit: https://www.broadinstitute.org/mia

Copyright Broad Institute, 2018. All rights reserved.

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