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Article

Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures

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Department of Chemistry, University of California, Irvine, CA 92697, USA
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Department of Chemistry, San Jose State University, San Jose, CA 95192, USA
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Department of Molecular Biology & Biochemistry, University of California, Irvine, CA 92697, USA
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Departments of Sociology, Statistics and Electrical Engineering & Computer Science, University of California, Irvine, CA 92697, USA
*
Authors to whom correspondence should be addressed.
Academic Editors: Thomas R. Caulfield and Alexander T. Baker
Biomolecules 2021, 11(12), 1788; https://doi.org/10.3390/biom11121788
Received: 30 July 2021 / Revised: 11 November 2021 / Accepted: 19 November 2021 / Published: 30 November 2021
Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail—an effect that is especially acute for topological representations such as protein structure networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This “neural upscaling” procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 μs atomistic molecular dynamics trajectory of Aβ140, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs. View Full-Text
Keywords: coarse-grained models; molecular dynamics; protein structure networks; intrinsically disordered proteins; machine learning coarse-grained models; molecular dynamics; protein structure networks; intrinsically disordered proteins; machine learning
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MDPI and ACS Style

Duong, V.T.; Diessner, E.M.; Grazioli, G.; Martin, R.W.; Butts, C.T. Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures. Biomolecules 2021, 11, 1788. https://doi.org/10.3390/biom11121788

AMA Style

Duong VT, Diessner EM, Grazioli G, Martin RW, Butts CT. Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures. Biomolecules. 2021; 11(12):1788. https://doi.org/10.3390/biom11121788

Chicago/Turabian Style

Duong, Vy T., Elizabeth M. Diessner, Gianmarc Grazioli, Rachel W. Martin, and Carter T. Butts. 2021. "Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures" Biomolecules 11, no. 12: 1788. https://doi.org/10.3390/biom11121788

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