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

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2020 · $309,526

## Abstract

PROJECT SUMMARY/ABSTRACT
Data collection in macromolecular crystallography is subject to significant systematic errors that prevent
successful data collection on many systems and, ultimately, limit the accuracy of resulting structures. Creating
simulation technologies that can account for these errors will have significant impact on three fronts: 1) solving
new structures by better accounting for radiation damage, which is responsible for 80% of failed anomalous
phasing attempts, 2) improving multi-crystal averaging by simulating 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 models. To
move towards “damage-free data” from a synchrotron, we will start by calibrating radiation damage curves on
model and DBP samples. Using these curves we will incorporate realistic 3D models of radiation damage to
non-cuboid crystals (RADDOSE 3D) into our diffraction image simulator (MLFSOM) to yield a 3D Dose
Distribution and Illumination map along the crystal. This will result in a new generation of wavelength-
dependent absorption factors for the crystal to complement existing absorption corrections. At the beamline,
we will measure a 3D map of the crystal using cone beam online x-ray absorption radiography and a 2D map
of the beam profile. These advances will allow us to generate zero-dose extrapolation values, in an open
format, that account for experimental crystal and beam geometry. To improve multi-crystal averaging, we will
begin by characterizing how non-isomorphism varies as a function of humidity, radiation damage, and
functional state. By updating the classic “Crick and Magdoff” simulations of non-isomorphism with increasing
complexity, we will develop a singular value decomposition approach to parameterize non-isomorphism. Using
the corrections derived from this analysis, we will correct the non-isomorphism present in multi-crystal
experiments, enabling the determination of novel structures, including those collected using serial
crystallography at next-generation light sources. To enable enhanced simulation for robust interpretation of
experimental data, we will leverage new solvent models in macromolecular crystallography and small angle X-
ray scattering. 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 or SAXS 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 be transformative to both methods development and function...

## Key facts

- **NIH application ID:** 9951062
- **Project number:** 5R01GM124149-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** James M Holton
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $309,526
- **Award type:** 5
- **Project period:** 2017-09-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9951062

## Citation

> US National Institutes of Health, RePORTER application 9951062, Eliminating Critical Systematic Errors In Structural Biology With Next-Generation Simulation (5R01GM124149-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9951062. Licensed CC0.

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