# Tools for rapid and accurate structure elucidation of natural products

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $514,429

## Abstract

Mapping the Secondary Metabolomes of Marine Cyanobacteria
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 complex molecular
structures.

## Key facts

- **NIH application ID:** 9921415
- **Project number:** 5R01GM107550-08
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** GARRISON W COTTRELL
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $514,429
- **Award type:** 5
- **Project period:** 2013-09-05 → 2021-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9921415

## Citation

> US National Institutes of Health, RePORTER application 9921415, Tools for rapid and accurate structure elucidation of natural products (5R01GM107550-08). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9921415. Licensed CC0.

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