# Resolving ensemble averaged conformations by multi-temperature x-ray crystallography

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $467,162

## Abstract

PROJECT SUMMARY/ ABSTRACT
Our ability to rationally develop new drugs or engineer proteins is limited by an inadequate understanding of
conformational dynamics of biomolecules. We have previously established that proteins sample a restricted,
yet functionally relevant, conformational ensemble in a crystalline environment. Unfortunately, the ensembles
present themselves as unresolved, spatiotemporally averaged data in X-ray diffraction experiments. Our work
has centered on overcoming this limitation by resolving ensembles with multi-temperature X-ray
crystallography and multi-conformer models Our central hypothesis is that today's structural models
disproportionately target the ensemble average, masking important functional and allosteric molecular
mechanisms. The objective of this research program is to use multi-temperature X-ray crystallography and
multi-conformer models to access multi-scale heterogeneity and shifting equilibria of proteins and protein-
ligand systems. We will pursue the following specific aims: 1. Map the conformational landscapes of PTP1B
and AR by Multitemperature Multiconformer X-ray crystallography (MMX). To examine functionally important
loop regions, we will create and validate new automated procedures for our qFit algorithm to accurately model
large backbone heterogeneity. We will apply MMX to PTP1B, a validated diabetes target, and Androgen
Receptor (AR), an important prostate cancer target with new emerging drug resistance. 2. Exploit MMX
models for allosteric ligand discovery in PTP1B and Androgen Receptor. We have already identified novel
allosteric modulators for the diabetes therapeutic target protein tyrosine phosphatase 1B (PTP1B) by
multitemperature crystallography and ligand tethering. We will determine structures and test novel ligands of
AR resistance mutants. 3. Map conformational coupling at protein-ligand binding interfaces by MMX. We will
create and validate new procedures for our qFit algorithm to model ligand conformational distributions and
solvated amino acids, which is important for ligand optimization. We will perform large-scale validation tests of
our algorithms. Our research program will have a positive impact by enabling discovery-driven, translational
ligand optimization. Robust experimental and computational methods to access conformational ensembles, in
a manner complimentary to NMR and molecular dynamics, that would open up a new and exciting and avenue
to reveal molecular mechanisms not only from conventional synchrotron data, but also from time-resolved
experiments at new XFEL lightsources and from cryoEM.

## Key facts

- **NIH application ID:** 10073519
- **Project number:** 5R01GM123159-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** James Solomon Fraser
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $467,162
- **Award type:** 5
- **Project period:** 2018-01-01 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10073519

## Citation

> US National Institutes of Health, RePORTER application 10073519, Resolving ensemble averaged conformations by multi-temperature x-ray crystallography (5R01GM123159-04). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10073519. Licensed CC0.

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