# Eliminating Critical Systematic Errors In Structural Biology With Next-Generation Simulation

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $308,465

## Abstract

PROJECT SUMMARY/ABSTRACT
Macromolecular Crystallography (MX) is an established and widely used method for obtaining accurate, high-
resolution 3D models of biological molecules, yet MX data contain information that has yet to be unlocked.
Single-electron changes can be clearly visible at resolutions as low as 3.5 Å if systematic errors can be
eliminated. Creating simulation technologies that can account for these errors will have significant impact on
three fronts: 1) eliminating the structural changes and other caveats of radiation damage, which ultimately
limits the amount of data available from a given sample 2) improving multi-crystal averaging and comparison
by capturing and correcting non-isomorphism, which will open the gateway to arbitrary gains in signal-to-noise,
3) discriminating hotly contested alternative interpretations such as the presence or absence of a bound ligand,
by creating simulations with more realistic solvent and protein models. To move towards damage-free data
from a synchrotron, we will start by implementing a new kind of data collection we call “painting with X-rays”
that leverages modern fast-framing detectors to combine the best features of broad-beam and micro-beam
technologies: low dose contrast and isolation of the best parts of the crystal. We will then enhance zero-dose
extrapolation to handle the rich temporal information made available by finely dividing up the available photons.
We will build on our success correcting non-isomorphism in real space into reciprocal space, enabling merging
of incomplete data such as XFEL stills into parametric structure factor frameworks. These low-dimensional
frameworks will allow selection from a continuum of 3D molecular structures by dialing in desired parameter
values, and will also be applied to cases where the parameters are known quantities, such as time-resolved,
temperature series, humidity, or other reaction coordinates and variables controlled in an experiment. We will
test these framework models against the thousands of non-isomorphous data sets that have been collected at
our beamline and report on best practice. To enable robust interpretation of experimental data, we will extend
these multi-conformer models with simulation-based solvent models. Our work will create standard protocols
for comparing solvent density to alternative interpretations and to quantitatively assess how likely each
simulated situation is compared to the real macromolecular crystallography data. In addition to distinguishing
between different interpretations of the experimental data, improving solvent models will enhance
understanding of how macromolecules influence and interact with other molecules near their surface.
Collectively, we expect the benefits of eliminating these critical systematic errors to be transformative to both
methods development and functional studies using complimentary structural techniques, such as CryoEM,
SAXS, tomography and electron diffraction, especially hybrid...

## Key facts

- **NIH application ID:** 10710387
- **Project number:** 5R01GM124149-07
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** James M Holton
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $308,465
- **Award type:** 5
- **Project period:** 2017-09-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10710387

## Citation

> US National Institutes of Health, RePORTER application 10710387, Eliminating Critical Systematic Errors In Structural Biology With Next-Generation Simulation (5R01GM124149-07). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10710387. Licensed CC0.

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