posted on 2023-12-06, 09:29authored byKatarzyna Kapuścińska, Zofia Dukała, Mekhola Doha, Eman Ansari, Jimin Wang, Gary W. Brudvig, Bernand Brooks, Muhamed Amin
Metalloproteins
require metal ions as cofactors to catalyze specific
reactions with remarkable efficiency and specificity. In various electron
transfer reactions, metals in the active sites change their oxidation
states to facilitate the biochemical reactions. Cryogenic electron
microscopy, X-ray, and X-ray free electron laser (XFEL) crystallography
are used to image metalloproteins to understand the reaction mechanisms.
However, radiation damage in cryoEM and X-ray crystallography, and
the challenge of generating homogeneous crystals and keeping the appropriate
experimental conditions for all the crystals in XFEL crystallography,
may alter the oxidation states. Here, we build machine learning models
trained on a large data set from the Cambridge Crystallographic Data
Center to evaluate the metal oxidation states. The models yield high
accuracy scores (from 82% to 94%) for all metals in the small molecules.
Then, they were used to predict the oxidation states of more than
30 000 metal clusters in metalloproteins with Fe, Mn, Co, and
Cu in their active sites. We found that most of the metals exist in
the lower oxidation states (Fe2+ 77%, Mn2+ 85%,
Co2+ 65%, and Cu+ 64%), and these populations
correlate with the standard reduction potentials of the metal ions.
Furthermore, we found no clear correlation between these populations
and the resolution of the structures, which suggests no significant
dependence of these predictions on the resolution. Our models represent
a valuable tool for evaluating the oxidation states of the metals
in metalloproteins imaged with different techniques. The data files
and the machine learning code are available in a public GitHub repository: https://github.com/mamin03/OxitationStatesMetalloprotein.git.