# Simulation-guided spectroscopy and refinement of heterogenous conformational ensembles

> **NIH NIH R01** · GEORGIA INSTITUTE OF TECHNOLOGY · 2024 · $291,234

## Abstract

The heterogeneous conformational ensembles of flexible proteins are critical to their
function in bacterial virulence, cellular regulation, and other physiological roles, yet most
high-resolution structural methods succeed precisely by trapping such proteins in one or
two conformational states. Spectroscopic techniques such as DEER and single-molecule
FRET can yield population distributions for conformational ensembles but do this for a
small set of label-label distances. The driving hypothesis of this project is that advanced
computational models can be used not only to help refine protein conformational
ensembles using DEER or smFRET data but also to direct such experiments by predicting
the optimal placement of labels. We therefore develop new methods 1) to predict the
most informative placement for a set of labels given an initial, incomplete estimate of a
conformational ensemble and 2) to better refine conformational ensembles given
heterogeneous conformational population data, potentially from multiple experimental
sources acquired under different conditions. We apply these methods to gain insight into
flexible molecular recognition and cellular invasion by Neisseria and into cellular
regulation of the synaptic fusion machinery that controls neurotransmission.

## Key facts

- **NIH application ID:** 11018689
- **Project number:** 7R01GM138444-04
- **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Peter M Kasson
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $291,234
- **Award type:** 7
- **Project period:** 2021-09-25 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11018689

## Citation

> US National Institutes of Health, RePORTER application 11018689, Simulation-guided spectroscopy and refinement of heterogenous conformational ensembles (7R01GM138444-04). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/11018689. Licensed CC0.

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