# High accuracy computational methods for biomolecular nuclear magnetic resonance spectroscopy

> **NIH NIH U01** · UNIVERSITY OF CALIFORNIA BERKELEY · 2020 · $150,000

## 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. Because they are sensitive to the biological functional groups, detailed geometries,
and chemical environments, they allow for prediction of solution phase protein structures or to
identify or verify the structure of chemical compounds in the crystalline phase. 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 NMR 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 equipment
supplement we are proposing to acquire a dedicated compute cluster for high throughput
calculations of wavefunction-based QM methods we have developed for chemical shifts that
offer improved accuracy over DFT. This will be employed to populate databases that reflect
protein and small molecule drug relevant for machine learning methods we have developed
under NIH support. With such data, machine learning and deep networks will determine a
quantitative relationship between structure and computed NMR observable, and the resulting
data science driven methods will be tested on the refinement of folded proteins and small
molecule drug prediction.

## Key facts

- **NIH application ID:** 10145510
- **Project number:** 3U01GM121667-04S1
- **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:** $150,000
- **Award type:** 3
- **Project period:** 2017-02-01 → 2022-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10145510

## Citation

> US National Institutes of Health, RePORTER application 10145510, High accuracy computational methods for biomolecular nuclear magnetic resonance spectroscopy (3U01GM121667-04S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10145510. Licensed CC0.

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