# Tools for rapid and accurate structure elucidation of natural products

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2021 · $20,415

## Abstract

Project Summary
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 communitie...

## Key facts

- **NIH application ID:** 10390224
- **Project number:** 3R01GM107550-09S2
- **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:** $20,415
- **Award type:** 3
- **Project period:** 2013-09-05 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10390224

## Citation

> US National Institutes of Health, RePORTER application 10390224, Tools for rapid and accurate structure elucidation of natural products (3R01GM107550-09S2). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10390224. Licensed CC0.

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