Instrumentation and experimental procedures for robust collection of X-ray diffraction data from protein crystals across physiological temperatures.
Tzanko I DoukovDaniel HerschlagFilip YabukarskiPublished in: Journal of applied crystallography (2020)
Traditional X-ray diffraction data collected at cryo-temperatures have delivered invaluable insights into the three-dimensional structures of proteins, providing the backbone of structure-function studies. While cryo-cooling mitigates radiation damage, cryo-temperatures can alter protein conformational ensembles and solvent structure. Furthermore, conformational ensembles underlie protein function and energetics, and recent advances in room-temperature X-ray crystallography have delivered conformational heterogeneity information that can be directly related to biological function. Given this capability, the next challenge is to develop a robust and broadly applicable method to collect single-crystal X-ray diffraction data at and above room temperature. This challenge is addressed herein. The approach described provides complete diffraction data sets with total collection times as short as ∼5 s from single protein crystals, dramatically increasing the quantity of data that can be collected within allocated synchrotron beam time. Its applicability was demonstrated by collecting 1.09-1.54 Å resolution data over a temperature range of 293-363 K for proteinase K, thaumatin and lysozyme crystals at BL14-1 at the Stanford Synchrotron Radiation Lightsource. The analyses presented here indicate that the diffraction data are of high quality and do not suffer from excessive dehydration or radiation damage.
Keyphrases
- room temperature
- electron microscopy
- high resolution
- electronic health record
- big data
- ionic liquid
- oxidative stress
- healthcare
- molecular dynamics
- protein protein
- magnetic resonance imaging
- radiation induced
- computed tomography
- small molecule
- magnetic resonance
- radiation therapy
- data analysis
- physical activity
- crystal structure
- deep learning
- weight loss
- artificial intelligence
- case control
- aortic dissection