Authors
- Nicole M. Scarborough
- G. M. Dilshan P. Godaliyadda
- Dong Hye Ye
- David J. Kissick
- Shijie Zhang
- Justin A. Newman
- Michael J. Sheedlo
- Azhad Chowdhury
- Robert F. Fischetti
- Chittaranjan Das
- Gregery T. Buzzard
- Charles A. Bouman
- Garth J. Simpson
Abstract
A supervised learning approach for dynamic sampling (SLADS) was developed to reduce X-ray exposure prior to data collection in protein structure determination. Implementation of this algorithm allowed reduction of the X-ray dose to the central core of the crystal by up to 20-fold compared to current raster scanning approaches. This dose reduction corresponds directly to a reduction on X-ray damage to the protein crystals prior to data collection for structure determination. Implementation at a beamline at Argonne National Laboratory suggests promise for the use of the SLADS approach to aid in the analysis of X-ray labile crystals. The potential benefits match a growing need for improvements in automated approaches for microcrystal positioning.
Keywords
DIFFRACTION, SUPERVISED, RECONSTRUCTION