# Bioinformatics and Chemical Biology Approaches for Identifying Bioactive Natural Products of Symbiotic Actinobacteria

> **NIH NIH F32** · HARVARD MEDICAL SCHOOL · 2020 · $65,310

## Abstract

Project Summary/Abstract
Fungal and bacterial pathogens are a major threat to human health. Few therapeutics exist to treat fungal
infections while bacteria are becoming increasingly resistant to existing therapeutics. Humans have been using
natural products to treat infections for thousands of years, long before the causal agents of infection were
understood. Natural products have continued to be used as therapeutics in the modern age of medicine. Rates
of rediscovery of known natural products have increased in traditional sources of natural products, such as soil
bacteria. Recently, symbiotic Actinobacteria from insect agricultural systems have been recognized as a
promising source of bioactive compounds, especially antifungal agents. These bacteria often produce natural
products that defend an insect’s fungal crop from pathogenic fungus. The work proposed here will use chemical
biology approaches such as phenotypic interaction screens, genomics, and a new bioinformatics approach to
systematically search for bioactive natural products produced by Actinobacteria symbionts and other organisms
in insect agricultural systems.
The first part of this proposal focuses on using existing techniques to identify new bioactive natural products.
Phenotypic interaction screens can identify bioactive natural products by determining if a symbiotic bacteria
produces a natural product that inhibits the growth of a fungal pathogen and vice-versa. We will then use genomic
sequencing, bioinformatics, and heterologous expression to identify and characterize biosynthetic gene clusters
(BGCs) that are not expressed in the phenotypic interaction screens.
The second part of the proposed work involves the use of a new bioinformatics technique to identify interesting
bioactive natural products. Existing bioinformatics techniques identify BGCs and predict the most likely chemical
structure of the corresponding natural product. However, they do not conclude anything concerning the functional
role that the natural product plays. The technique developed here will use machine learning to predict the function
that the natural product fulfills in the ecological context of the organism. This algorithm will facilitate the
identification of bioactive natural products with therapeutically relevant functions.

## Key facts

- **NIH application ID:** 9963295
- **Project number:** 5F32GM128267-03
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** Allison Sara Walker
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $65,310
- **Award type:** 5
- **Project period:** 2018-07-01 → 2021-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9963295

## Citation

> US National Institutes of Health, RePORTER application 9963295, Bioinformatics and Chemical Biology Approaches for Identifying Bioactive Natural Products of Symbiotic Actinobacteria (5F32GM128267-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9963295. Licensed CC0.

---

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