# Integrated NMR for Complex Systems

> **NIH NIH P41** · UNIVERSITY OF WISCONSIN-MADISON · 2021 · $246,051

## Abstract

TR&D 3 SUMMARY
Assigning each resonance in an NMR spectrum to individual atoms in a molecule is essential to almost every
NMR project. Automated programs are successful for assignment of smaller soluble proteins, but frequently fail
for larger proteins and complexes, asymmetric oligomers, and systems with predominantly one secondary
structure such as helical membrane proteins or beta-sheet fibrils. For most systems, the assignment process –
sample preparation, experiment selection, data processing, signal identification and data analysis to achieve
assignments – is expensive, time consuming and requires significant manual effort. This manual intervention
requires expertise to be done well, and poor execution at any step makes subsequent steps more difficult.
Here we propose to develop algorithms and guided user interfaces that will automate processing of higher
dimensional spectra, improve automated peak picking, and tailor acquisition of NMR spectra for assignment to
maximize data and minimize cost in a fully-automated, integrated data acquisition, assignment and structure
determination platform. Automation of this process will improve the reproducibility of NMR data analysis,
reduce the time and cost of NMR studies, and make NMR more accessible to the broad research community.

## Key facts

- **NIH application ID:** 10089604
- **Project number:** 1P41GM136463-01A1
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Woonghee Lee
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $246,051
- **Award type:** 1
- **Project period:** 2021-01-02 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10089604

## Citation

> US National Institutes of Health, RePORTER application 10089604, Integrated NMR for Complex Systems (1P41GM136463-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10089604. Licensed CC0.

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