# Protein structure determination from low-resolution experimental data

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2020 · $279,050

## Abstract

Project Abstract
Determination of a protein’s three-dimensional structure is of critical importance in biology,
providing insights to biological mechanisms and important targets for drug design. While high-
resolution X-ray diffraction data provides an atomic view of cellular components, for many
interesting and biologically relevant complexes, it may only be possible to obtain low-resolution
structural information. Both cryo-electron microscopy and X-ray crystallography, when applied
to large, flexible molecular machines, often produce data of 3-6 Å resolution. Extracting detailed
atomic information from this data, critical in understanding function, the effects of mutation, or in
designing drugs is impossible due to the low number of observations and the large
conformational space proteins may adopt. I propose to develop computational methods for
extracting high-resolution atomic models from this low-resolution data, bridging the “resolution
gap” with computational methods.
My proposed research develops and extends our labs’ methods for automatically inferring
atomic accuracy models, from these “near-atomic” resolution sources of experimental data. We
develop novel conformational sampling methods, guided by experimental data, to infer atomic
information both in cases where homologous high-resolution data is available, and where it is
not. Additionally, we propose development of methods for estimating model uncertainty; these
are critical in understanding to what degree structural conclusions may be made from a
particular dataset. Finally, in pushing the resolution limit further, we develop general tools for
biomolecular forcefield optimization. These machine-learning tools will allow development of a
next-generation forcefield, critical in extending the resolution limit of data from which we can
infer atomic details.
The overall goal of the proposed research is robust and accessible methods to determine
protein structures to atomic accuracy from only sparse experimental data. Combined, the three
aims in this proposal will lead to dramatic improvements in our ability to infer atomic interactions
from sparse experimental data. This will lead to determination of structures that will reveal key
insights into how biomedically important protein complexes perform their function and what goes
wrong in human disease.

## Key facts

- **NIH application ID:** 9988448
- **Project number:** 5R01GM123089-04
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Frank P DiMaio
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $279,050
- **Award type:** 5
- **Project period:** 2017-08-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9988448

## Citation

> US National Institutes of Health, RePORTER application 9988448, Protein structure determination from low-resolution experimental data (5R01GM123089-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9988448. Licensed CC0.

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