# Protein structure determination from low-resolution experimental data > **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2024 · $309,053 ## Abstract 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. In recent years, cryo-electron microscopy (cryo-EM) has emerged as a powerful method to gain structural insights into these large molecular machines. However, for many complexes of interest, data is often: a) extremely limited in resolution, lacking atomic details, and b) highly heterogeneous in terms of data quality. Both effects are due to the inherent flexibility of many of these complexes. 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. We propose a suite of computational methods for inferring high-accuracy atomic models from this low-resolution data, revealing detailed structural insights into these complexes. In the previous granting period, we: a) developed a set of tools for accurately building and refining protein models into low-resolution experimental cryoEM maps, b) released these tools as freely available software and gave tutorials to enable cryo-electron microscopists to use these methods, and c) closely collaborated with dozens of microscopists to develop custom methodology for their particular systems of interest. In this proposal, we further these methods in several distinct directions. We develop methodology for accurately identifying ligands and accurately modelling ligand conformations in low-resolution datasets. We build off of machine- learning-guided protein structure prediction, and develop methods to more rapidly and robustly interpret low-resolution cryo-EM datasets. We feel these methods should be fast enough to be suitable for use in an on-line data processing pipeline. Finally, we develop methodology to untangle structural heterogeneity by building a heterogeneous set of protein models that best explains variations in single-particle images, providing structural insights into the conformational heterogeneity of single-particle data. The overall goal of the proposed research is robust and accessible methods to interpret – to the level of atomic accuracy – low-resolution and heterogeneous cryo-EM datasets. Combined, the three aims in this proposal will lead to dramatic improvements in our ability to infer atomic interactions from such datasets. As microscopists continue tackling more complicated protein complexes, these methods will be needed to reveal atom-level insights into how biomedically important protein complexes perform their function and what goes wrong in human disease. ## Key facts - **NIH application ID:** 10896192 - **Project number:** 5R01GM123089-08 - **Recipient organization:** UNIVERSITY OF WASHINGTON - **Principal Investigator:** Frank P DiMaio - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $309,053 - **Award type:** 5 - **Project period:** 2017-08-01 → 2026-07-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10896192 ## Citation > US National Institutes of Health, RePORTER application 10896192, Protein structure determination from low-resolution experimental data (5R01GM123089-08). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10896192. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*