Predicting the oxidation states of Mn ions in the oxygen-evolving complex of photosystem II using supervised and unsupervised machine learning

Amin, Muhamed

doi:10.1007/s11120-022-00941-8

Predicting the oxidation states of Mn ions in the oxygen-evolving complex of photosystem II using supervised and unsupervised machine learning

Journal Article · 27 July 2022 · Photosynthesis Research

DOI:https://doi.org/10.1007/s11120-022-00941-8· OSTI ID:1976709

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OSTI

Abstract

Serial Femtosecond Crystallography at the X-ray Free Electron Laser (XFEL) sources enabled the imaging of the catalytic intermediates of the oxygen evolution reaction of Photosystem II (PSII). However, due to the incoherent transition of the S-states, the resolved structures are a convolution from different catalytic states. Here, we train Decision Tree Classifier and K-means clustering models on Mn compounds obtained from the Cambridge Crystallographic Database to predict the S-state of the X-ray, XFEL, and CryoEM structures by predicting the Mn’s oxidation states in the oxygen-evolving complex. The model agrees mostly with the XFEL structures in the dark S₁state. However, significant discrepancies are observed for the excited XFEL states (S₂, S_3,and S₀) and the dark states of the X-ray and CryoEM structures. Furthermore, there is a mismatch between the predicted S-states within the two monomers of the same dimer, mainly in the excited states. We validated our model against other metalloenzymes, the valence bond model and the Mn spin densities calculated using density functional theory for two of the mismatched predictions of PSII. The model suggests designing a more optimized sample delivery and illumiation systems are crucial to precisely resolve the geometry of the advanced S-states to overcome the noncoherent S-state transition. In addition, significant radiation damage is observed in X-ray and CryoEM structures, particularly at the dangler Mn center (Mn4). Our model represents a valuable tool for investigating the electronic structure of the catalytic metal cluster of PSII to understand the water splitting mechanism.

Research Organization:: City Univ. of New York (CUNY), NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

DOE Contract Number:: SC0001423

OSTI ID:: 1976709

Journal Information:: Photosynthesis Research, Journal Name: Photosynthesis Research Journal Issue: 1 Vol. 156; ISSN 0166-8595

Publisher:: Springer

Country of Publication:: United States

Language:: English

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Predicting the oxidation states of Mn ions in the oxygen-evolving complex of photosystem II using supervised and unsupervised machine learning

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