# Project 1: Improved algorithms for structure refinement at multiple resolution ranges

> **NIH NIH P01** · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · 2020 · $898,608

## Abstract

Project I
Improved algorithms for structure refinement at multiple resolution ranges
Our ability to visualize macromolecules at atomic resolution has had a lasting and profound impact on
the study of biology. Obtaining the most accurate model is paramount for maximizing biological insight
and for enabling activities such as the development of new therapeutics for human health. For the most
challenging, and often very biologically important systems the availability of only low resolution
diffraction data (3-4.5 Å) has limited the accuracy of models. Therefore we will develop computational
methods that significantly improve models, by creating better methods for their refinement even when
data are limiting. This will have a broad impact on structural biologists, drug developers and molecular
modelers.
The field of X-ray crystallography has matured significantly over the last 10 years. However, there still
remain many challenges in solving and refining structures at low resolution. These same challenges are
now being encountered by researchers in the field of single particle cryo-EM. Many of the tools we have
developed for crystallography can be applied to this problem. Therefore we propose to further develop
our methods to address structure refinement when only sparse experimental data is available. Real
space algorithms will be implemented to improve the refinement of models against maps, in particular
for the cryo-EM case. The resulting procedure will be able to robustly refine the fit of models to maps
while preserving excellent stereochemistry.
Obtaining accurate stereochemistry has profound implications for the understanding of enzymatic
reactions, macromolecular interactions, and the development of novel therapeutics. We will develop
methods to generate the highest quality molecular models, which provide the greatest chemical and
biological insight, at any resolution range. To do this we will continue our collaborations that have made
use of methods from the field of structure modeling, and extend them to make use of other physically
reasonable molecular potentials developed in the fields of molecular mechanics and dynamics.
Finally, we will continue to develop and maintain the Phenix infrastructure to support both novice and
expert researchers. Diffraction image analysis will be incorporated into the Phenix system to enable
automated analysis of user data to detect possible problems. We will also implement a structure
deposition system that incorporates model and data validation, and brings together information from
multiple stages of structure solution.

## Key facts

- **NIH application ID:** 9933004
- **Project number:** 5P01GM063210-19
- **Recipient organization:** UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
- **Principal Investigator:** PAUL David ADAMS
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $898,608
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9933004

## Citation

> US National Institutes of Health, RePORTER application 9933004, Project 1: Improved algorithms for structure refinement at multiple resolution ranges (5P01GM063210-19). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9933004. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*