# Leveraging evolutionary analyses and machine learning to discover multiscale molecular features associated with antibiotic resistance

> **NIH NIH U01** · UNIVERSITY OF COLORADO DENVER · 2023 · $451,480

## Abstract

Summary
Antibiotic resistance (AR) is a high-priority urgent threat. AR pathogens such as the ESKAPE group cause
millions of infections and hundreds of thousands of deaths. While current strategies such as genetic and drug
screens have helped identify genes and mutations critical for AR in specific pathogens, there is a broad lack of
methods to help understand AR’s origin and continuous adaptation. AR can arise in a pathogen via a variety of
molecular changes, including acquiring protein domains, individual genes, or metabolic capabilities. Hence,
predicting and overcoming AR in emerging pathogens or discovering new AR mechanisms requires a holistic
understanding of AR evolution across multiple molecular scales. However, leveraging these diverse datasets is
challenging because original databases are siloed from each other. Further, the different data types are hard to
integrate in a biologically-meaningful way across scales. In this project, we describe a computational discovery
framework combining evolutionary analyses and machine learning to integrate AR data across multiple scales
to gain mechanistic insights into AR molecular features in ESKAPE pathogens and predict AR in new
(re)emerging genomes. We will implement our approach as open FAIR data repositories, open software, and
web platforms for the computational, experimental, and clinical AR communities. We will work closely with AR
collaborators, end-users, and the open software community during and following the project duration to ensure
the release of accessible, user-friendly, interactive platforms. Finally, in the post-award expansion phase, we
will work with NIAID-funded bioinformatics consortia for downstream integration of data and methods and
long-term sustainability. The framework will develop in this project will be broadly applicable to advance
understanding of AR in understudied and emerging pathogens (beyond ESKAPE) towards ending the arms
race between microbes and drugs by creating better treatment outcomes.

## Key facts

- **NIH application ID:** 10658686
- **Project number:** 1U01AI176414-01
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** Janani Ravi
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $451,480
- **Award type:** 1
- **Project period:** 2023-08-14 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10658686

## Citation

> US National Institutes of Health, RePORTER application 10658686, Leveraging evolutionary analyses and machine learning to discover multiscale molecular features associated with antibiotic resistance (1U01AI176414-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10658686. Licensed CC0.

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