Journal
CHEMICAL SCIENCE
Volume 12, Issue 32, Pages 10802-10809Publisher
ROYAL SOC CHEMISTRY
DOI: 10.1039/d1sc01895g
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Funding
- National Science Foundation [1764415, 1751471]
- National Institute of General Medical Sciences of the National Institutes of Health [R35GM137966]
- Division Of Chemistry
- Direct For Mathematical & Physical Scien [1764415] Funding Source: National Science Foundation
- Div Of Chem, Bioeng, Env, & Transp Sys
- Directorate For Engineering [1751471] Funding Source: National Science Foundation
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The study utilizes graph neural networks (GNNs) for NMR chemical shift prediction, accurately capturing important chemical shift phenomena such as hydrogen bonding-induced downfield shift and shifts of organic molecules. These GNN models do not require feature engineering, only data training, yet are as accurate as previous empirical protein NMR models.
Inferring molecular structure from Nuclear Magnetic Resonance (NMR) measurements requires an accurate forward model that can predict chemical shifts from 3D structure. Current forward models are limited to specific molecules like proteins and state-of-the-art models are not differentiable. Thus they cannot be used with gradient methods like biased molecular dynamics. Here we use graph neural networks (GNNs) for NMR chemical shift prediction. Our GNN can model chemical shifts accurately and capture important phenomena like hydrogen bonding induced downfield shift between multiple proteins, secondary structure effects, and predict shifts of organic molecules. Previous empirical NMR models of protein NMR have relied on careful feature engineering with domain expertise. These GNNs are trained from data alone with no feature engineering yet are as accurate and can work on arbitrary molecular structures. The models are also efficient, able to compute one million chemical shifts in about 5 seconds. This work enables a new category of NMR models that have multiple interacting types of macromolecules.
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