# Tools for rapid and accurate structure elucidation of natural products

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2021 · $10,489

## Abstract

Summary from the Application 5 R01 GM107550-08
“Tools for rapid and accurate
structure elucidation of natural products”
Bacteria are extraordinarily prolific sources of structurally unique and biologically active natural
products that derive from a diversity of fascinating biochemical pathways. However, the complete
structure elucidation of natural products is often the most time consuming and costly endeavor in
natural product drug discovery programs. Compounding this, advancements in genome
sequencing have accelerated the identification of unique modular biosynthetic gene clusters in
prokaryotes and revealed a wealth of new compounds yet to be isolated and biologically and
chemically characterized. Resultantly, there is an urgent and continuing need in this field to
connect biosynthetic gene clusters to their respective MS fragmentation signatures in the MS2
molecular networks. The capacity to make such connections will accelerate new compound
discovery as well as create associations between gene cluster and biosynthetic pathway, and aid
in fast and accurate structure elucidations. Combined with this informatics approach, this
proposed continuation project explores innovative methods by which to solve complex molecular
structures by enhanced MS and NMR experiments, as well as the development of new algorithms
by which to accelerate their analysis. Thus, the overarching goal of this grant is to develop efficient
methods that facilitate automated structural classification, structural feature discovery and
ultimately efficient structure elucidation of natural products (or any small molecule) and to build
an infrastructure that interacts with data input from the community. We will achieve this with the
following four specific aims: Aim 1. Integration of MS2 molecular networking with gene
cluster networking to rapidly and efficiently locate natural products that have unique
molecular architectures; Aim 2. To develop a suite of high sensitivity pulse sequences for
natural product structure elucidation; Aim 3. To develop NMR based molecular networking
strategies using Deep Convolutional Neural Networks (DCNNs) to facilitate the
categorization and structure elucidation of organic compounds; Aim 4. To integrate NMR
molecular networking and MS2 -based molecular networking as an efficient structure
characterization and elucidation strategy. By achieving these aims we will develop an
innovative workflow for finding new compounds and for determining their structures, both quickly
and accurately. The connection between gene cluster and molecule will shed light on
stereochemistry and potential halogenations and methylations. This information can then be used
in combination with more efficient NMR and MS methods to accurately determine structures.
These tools will be widely shared, such as through the Global Natural Products Social (GNPS)
Molecular Network, to enhance the overall capacity of the natural products and organic chemistry
communities to solve compl...

## Key facts

- **NIH application ID:** 10393432
- **Project number:** 3R01GM107550-09S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** GARRISON W COTTRELL
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $10,489
- **Award type:** 3
- **Project period:** 2013-09-05 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10393432

## Citation

> US National Institutes of Health, RePORTER application 10393432, Tools for rapid and accurate structure elucidation of natural products (3R01GM107550-09S1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10393432. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*