# NMR Fingerprinting: Leveraging optimal control pulse design, tailored isotope labeling, and machine learning to study intractable proteins

> **NIH NIH R01** · DANA-FARBER CANCER INST · 2021 · $445,749

## Abstract

Project summary
Nuclear magnetic resonance (NMR) spectroscopy is essential for the study structure, dynamics and function of
proteins in near-native conditions. NMR studies have vital implications for therapeutic development. However,
as the number of amino acids in the protein increases, NMR signals decay (relax) faster, yielding lower
sensitivity and resolution, while the spectrum becomes more crowded. In these cases it is challenging to match
observed signals to specific nuclei in the protein (called `resonance assignment') in order to meaningfully
interpret NMR data. The overarching goal of our research is to push the boundaries of NMR enabling valuable
insight about the dynamics and functions of currently intractable proteins. The objective of this project is to
design an NMR platform consisting of coordinated, next-generation biochemical, biophysical, mathematical,
and computational techniques. Our platform is built around an original approach to NMR spectroscopy in which
new information about the local environment of each nucleus is encoded in the shape and pattern of its NMR
signal. The rationale is that these patterns are a `fingerprint' – an intricate and unique signature that encodes
key information about which atom is responsible for each resonance peak in the NMR spectrum. We will
design and realize fingerprint patterns using two innovative approaches: 1) biochemically, by selectively
introducing NMR-active isotopes into carefully chosen positions in the protein samples, and biophysically, and
2) by using specialized radiofrequency pulses to accurately control the quantum interactions that determine the
NMR spectrum. The resulting fingerprints will be decoded using established algorithmic structures from
machine learning, notably artificial neural networks. This will facilitate automated analyses that are accessible
to non-NMR specialists. Our approach to spectroscopy holds promise in the study of therapeutically important
proteins expressed in eukaryotic expression systems (e.g. G-protein coupled receptors and glycosylated
proteins). Current NMR data from such proteins shows clear dynamics and interactions with other proteins, but
cannot yet be properly interpreted because of the difficulty of relating each NMR peak to an amino acid in the
protein sequence. Our platform will deliver two significant outcomes: 1) NMR resonance assignment for
meaningful analyses of previously intractable systems. 2) Enable non-NMR specialists, to easily proceed from
expressing their protein sample to using NMR to study dynamics and interactions via assigned spectra. This
will have a positive impact on protein science and medical research. To support our mission we have
assembled a team of leading experts to test our platform with their own protein systems.

## Key facts

- **NIH application ID:** 10159285
- **Project number:** 5R01GM136859-02
- **Recipient organization:** DANA-FARBER CANCER INST
- **Principal Investigator:** Haribabu Arthanari
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $445,749
- **Award type:** 5
- **Project period:** 2020-06-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10159285

## Citation

> US National Institutes of Health, RePORTER application 10159285, NMR Fingerprinting: Leveraging optimal control pulse design, tailored isotope labeling, and machine learning to study intractable proteins (5R01GM136859-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10159285. Licensed CC0.

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