Data-Efficient Generation of Protein Conformational Ensembles with Backbone-to-Side-Chain Transformers

Chennakesavalu, Shriram; Rotskoff, Grant M.

doi:10.1021/acs.jpcb.3c08195

Title: Data-Efficient Generation of Protein Conformational Ensembles with Backbone-to-Side-Chain Transformers

Journal Article · 22 February 2024 · Journal of Physical Chemistry. B

DOI: https://doi.org/10.1021/acs.jpcb.3c08195 · OSTI ID:2341329

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Stanford Univ., CA (United States); Stanford University
Stanford Univ., CA (United States)

Excitement at the prospect of using data-driven generative models to sample configurational ensembles of biomolecular systems stems from the extraordinary success of these models on a diverse set of high-dimensional sampling tasks. Unlike image generation or even the closely related problem of protein structure prediction, there are currently no data sources with sufficient breadth to parametrize generative models for conformational ensembles. To enable discovery, a fundamentally different approach to building generative models is required: models should be able to propose rare, albeit physical, conformations that may not arise in even the largest data sets. Here, in this work, we introduce a modular strategy to generate conformations based on “backmapping” from a fixed protein backbone that (1) maintains conformational diversity of the side chains and (2) couples the side-chain fluctuations using global information about the protein conformation. Our model combines simple statistical models of side-chain conformations based on rotamer libraries with the now ubiquitous transformer architecture to sample with atomistic accuracy. Together, these ingredients provide a strategy for rapid data acquisition and hence a crucial ingredient for scalable physical simulation with generative neural networks.

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Research Organization:: Stanford Univ., CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0022917

OSTI ID:: 2341329

Alternate ID(s):: OSTI ID: 2474797

Journal Information:: Journal of Physical Chemistry. B, Journal Name: Journal of Physical Chemistry. B Journal Issue: 9 Vol. 128; ISSN 1520-6106

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
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Title: Data-Efficient Generation of Protein Conformational Ensembles with Backbone-to-Side-Chain Transformers

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