# High Accuracy Computational Methods for Biomolecular Nuclear Magnetic Resonance Spectroscopy

> **NIH NIH U01** · UNIVERSITY OF CALIFORNIA BERKELEY · 2020 · $297,077

## Abstract

High accuracy computational methods for biomolecular nuclear magnetic resonance
spectroscopy
Nuclear magnetic resonance (NMR) spectroscopy is one of the most important condensed phase
probes of composition, structure and dynamics of biomolecules and bio-organic species. NMR
observables such as chemical shifts and spin-spin splittings can be measured to very high
accuracy, and are sensitive both to the functional groups that are present and to their detailed
geometries and chemical environment. As such these NMR measurements could be used to
develop protein structures with a quality equivalent to high resolution X-ray crystallography but
in their native aqueous environments. The connection to structure, while true in principle, is
nevertheless sometimes difficult to reveal in practice through direct assignment of the spectrum.
Simulation methods that accurately predict spectral observables from structure are a key goal for
spectral assignment. Such methods are even more crucial for the inverse problem of realizing
high quality NMR structures of folded proteins from spectra, and as powerful restraints for
determining the structural ensembles of intrinsically disordered proteins (IDPs). Existing
approaches to this problem typically rely on semi-empirical heuristics, and while they have
achieved considerable success, they also reveal limitations that significantly degrade the quality
of structural prediction. In this proposal, we will develop a new, first principles quantum
mechanical (QM) based approach to simulation of NMR spectral observables for condensed
phase biomolecules and bio-organics. Rapid prototyping of new QM methods will be enabled by
the development of a distinctive in-silico NMR laboratory that applies finite magnetic fields and
nuclear spins. From this capability, new methods for chemical shifts and spin-spin splittings will
emerge that offer improved accuracy versus cost tradeoffs, and will be employed to populate
databases that reflect protein relevant and energetically accessible environments. With such data,
both artificial neural networks and Bayesian supervised learning approaches will determine a
quantitative relationship between structure and computed NMR observable, and the resulting
eQMCalculator will be tested on the refinement of folded proteins and creation of structural
ensembles for IDPs.

## Key facts

- **NIH application ID:** 9875487
- **Project number:** 5U01GM121667-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Martin Paul Head-Gordon
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $297,077
- **Award type:** 5
- **Project period:** 2017-02-01 → 2022-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9875487

## Citation

> US National Institutes of Health, RePORTER application 9875487, High Accuracy Computational Methods for Biomolecular Nuclear Magnetic Resonance Spectroscopy (5U01GM121667-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9875487. Licensed CC0.

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