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 functional studies.

Public Health Relevance

Data collection in macromolecular crystallography is subject to significant systematic errors that prevent successful soilution 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.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM124149-04
Application #
9951062
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Smith, Ward
Project Start
2017-09-01
Project End
2022-05-31
Budget Start
2020-06-01
Budget End
2021-05-31
Support Year
4
Fiscal Year
2020
Total Cost
Indirect Cost
Name
University of California San Francisco
Department
Biochemistry
Type
Schools of Medicine
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94118
Hurlburt, Nicholas K; Chen, Li-Hung; Stergiopoulos, Ioannis et al. (2018) Structure of the Cladosporium fulvum Avr4 effector in complex with (GlcNAc)6 reveals the ligand-binding mechanism and uncouples its intrinsic function from recognition by the Cf-4 resistance protein. PLoS Pathog 14:e1007263
Zhang, Shao-Qing; Chino, Marco; Liu, Lijun et al. (2018) De Novo Design of Tetranuclear Transition Metal Clusters Stabilized by Hydrogen-Bonded Networks in Helical Bundles. J Am Chem Soc 140:1294-1304
Blaisse, Michael R; Fu, Beverly; Chang, Michelle C Y (2018) Structural and Biochemical Studies of Substrate Selectivity in Ascaris suum Thiolases. Biochemistry 57:3155-3166
Kern, Jan; Chatterjee, Ruchira; Young, Iris D et al. (2018) Structures of the intermediates of Kok's photosynthetic water oxidation clock. Nature 563:421-425
Wall, Michael E; Wolff, Alexander M; Fraser, James S (2018) Bringing diffuse X-ray scattering into focus. Curr Opin Struct Biol 50:109-116
Suter, Scott R; Ball-Jones, Alexi; Mumbleau, Madeline M et al. (2017) Controlling miRNA-like off-target effects of an siRNA with nucleobase modifications. Org Biomol Chem 15:10029-10036