Predicting the Oxidation States of Mn ions in the Oxygen Evolving Complex of Photosystem II Using Supervised and Unsupervised Machine Learning

TL;DR

This study employs machine learning models to predict manganese oxidation states in Photosystem II's oxygen evolving complex, revealing discrepancies in structural data and emphasizing the need for higher resolution to better understand water splitting mechanisms.

Contribution

It introduces supervised and unsupervised machine learning approaches to determine Mn oxidation states, aiding in resolving structural ambiguities in Photosystem II studies.

Findings

Model agrees with XFEL structures in dark S1 state

Discrepancies in excited S-states and between monomers

Radiation damage affects Mn centers in X-ray and CryoEM structures

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. 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-mean 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 (OEC). The model agrees mostly with the XFEL structures in the dark S1 state. However, significant discrepancies are observed for the excited XFEL states (S2, S3, 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. The model suggests that improving the resolution is crucial to precisely resolve the geometry of the illuminated 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.