2023/07/04 by Vidya Ganapati, Daniel Tchoń, Ganapati, Vidya +5
Materials Science · #Biological Physics (physics.bio-ph) #Electron and X-Ray Spectroscopy Techniques #Enzyme Structure and Function #FOS: Electrical engineering #FOS: Physical sciences #Image and Video Processing (eess.IV) #Machine Learning in Materials Science #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2307.01901
openalex publication_date 2023/07/04 · openalex created_date 2023/07/07 · openalex updated_date 2026/08/01
The Computational Crystallography Toolbox (CCTBX) is open-source software that allows for processing of crystallographic data, including from serial femtosecond crystallography (SFX), for macromolecular structure determination. We aim to use the modules in CCTBX to determine the oxidation state of individual metal atoms in a macromolecule. Changes in oxidation state are reflected in small shifts of the atom's X-ray absorption edge. These energy shifts can be extracted from the diffraction images recorded in serial femtosecond crystallography, given knowledge of a forward physics model. However, as the diffraction changes only slightly due to the absorption edge shift, inaccuracies in the forward physics model make it extremely challenging to observe the oxidation state. In this work, we describe the potential impact of using self-supervised deep learning to correct the scientific model in CCTBX and provide uncertainty quantification. We provide code for forward model simulation and data analysis, built from CCTBX modules, at https://github.com/gigantocypris/SPREAD , which can be integrated with machine learning. We describe open questions in algorithm development to help spur advances through dialog between crystallographers and machine learning researchers. New methods could help elucidate charge transfer processes in many reactions, including key events in photosynthesis.