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

Exploring Nitric Oxide as a Regulator in Salt Tolerance: Insights into Photosynthetic Efficiency in Maize

The growing issue of salinity is a significant threat to global agriculture, affecting diverse regions worldwide. Nitric oxide (NO) serves as an essential signal molecule in regulating photosynthetic performance under physiological and stress conditions. The present study reveals the protective effects of different concentrations (0-300 µM) of sodium nitroprusside (SNP, a donor of NO) on the functions of the main complexes within the photosynthetic apparatus of maize (Zea mays L. Kerala) under salt stress (150 mM NaCl). The data showed that SNP alleviates salt-induced oxidative stress and prevents changes in the fluidity of thylakoid membranes (Laurdan GP) and energy redistribution between the two photosystems (77K chlorophyll fluorescence ratio F735/F685). Chlorophyll fluorescence measurements demonstrated that the foliar spray with SNP under salt stress prevents the decline of photosystem II (PSII) open reaction centers (qP) and improves their efficiency (Φexc), thereby influencing QA- reoxidation. The data also revealed that SNP protects the rate constants for two pathways of QA- reoxidation (k1 and k2) from the changes caused by NaCl treatment alone. Additionally, there is a predominance of QA- interaction with plastoquinone in comparison to the recombination of electrons in QA QB- with the oxygen-evolving complex (OEC). The analysis of flash oxygen evolution showed that SNP treatment prevents a salt-induced 10% increase in PSII centers in the S0 state, i.e., protects the initial S0-S1 state distribution, and the modification of the Mn cluster in the OEC. Moreover, this study demonstrates that SNP-induced defense occurs on both the donor and acceptor sides of the PSII, leading to the protection of overall photosystems performance (PIABS) and efficient electron transfer from the PSII donor side to the reduction of PSI end electron acceptors (PItotal). This study clearly shows that the optimal protection under salt stress occurs at approximately 50-63 nmoles NO/g FW in leaves, corresponding to foliar spray with 50-150 µM SNP.

Correlating Structure with Spectroscopy in Ascorbate Peroxidase Compound II

Structural and spectroscopic investigations of compound II in ascorbate peroxidase (APX) have yielded conflicting conclusions regarding the protonation state of the crucial Fe(IV) intermediate. Neutron diffraction and crystallographic data support an iron(IV)-hydroxo formulation, whereas Mössbauer, X-ray absorption (XAS), and nuclear resonance vibrational spectroscopy (NRVS) studies appear consistent with an iron(IV)-oxo species. Here we examine APX with spectroscopy-oriented QM/MM calculations and extensive exploration of the conformational space for both possible formulations of compound II. We establish that irrespective of variations in the orientation of a vicinal arginine residue and potential reorganization of proximal water molecules and hydrogen bonding, the Fe-O distances for the oxo and hydroxo forms consistently fall within distinct, narrow, and nonoverlapping ranges. The accuracy of geometric parameters is validated by coupled-cluster calculations with the domain-based local pair natural orbital approach, DLPNO-CCSD(T). QM/MM calculations of spectroscopic properties are conducted for all structural variants, encompassing Mössbauer, optical, X-ray absorption, and X-ray emission spectroscopies and NRVS. All spectroscopic observations can be assigned uniquely to an Fe(IV)═O form. A terminal hydroxy group cannot be reconciled with the spectroscopic data. Under no conditions can the Fe(IV)═O distance be sufficiently elongated to approach the crystallographically reported Fe-O distance. The latter is consistent only with a hydroxo species, either Fe(IV) or Fe(III). Our findings strongly support the Fe(IV)═O formulation of APX-II and highlight unresolved discrepancies in the nature of samples used across different experimental studies.

Bridging the Coordination Chemistry of Small Compounds and Metalloproteins Using Machine Learning

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.

Machine learning in photosynthesis: Prospects on sustainable crop development

Improving photosynthesis is a promising avenue to increase food security. Studying photosynthetic traits with the aim to improve efficiency has been one of many strategies to increase crop yield but analyzing large data sets presents an ongoing challenge. Machine learning (ML) represents a ubiquitous tool that can provide a more elaborate data analysis. Here we review the application of ML in various domains of photosynthetic research, as well as in photosynthetic pigment studies. We highlight how correlating hyperspectral data with photosynthetic parameters to improve crop yield could be achieved through various ML algorithms. We also propose strategies to employ ML in promoting photosynthetic pigment research for furthering crop yield.

Does Serial Femtosecond Crystallography Depict State-Specific Catalytic Intermediates of the Oxygen-Evolving Complex?

Recent advances in serial femtosecond crystallography (SFX) of photosystem II (PSII), enabled by X-ray free electron lasers (XFEL), provided the first geometric models of distinct intermediates in the catalytic S-state cycle of the oxygen-evolving complex (OEC). These models are obtained by flash-advancing the OEC from the dark-stable state (S₁) to more oxidized intermediates (S₂ and S₃), eventually cycling back to the most reduced S₀. However, the interpretation of these models is controversial because geometric parameters within the Mn₄CaO₅ cluster of the OEC do not exactly match those expected from coordination chemistry for the spectroscopically verified manganese oxidation states of the distinct S-state intermediates. Here we focus on the first catalytic transition, S₁ → S₂, which represents a one-electron oxidation of the OEC. Combining geometric and electronic structure criteria, including a novel effective oxidation state approach, we analyze existing 1-flash (1F) SFX-XFEL crystallographic models that should depict the S₂ state of the OEC. We show that the 1F/S₂ equivalence is not obvious, because the Mn oxidation states and total unpaired electron counts encoded in these models are not fully consistent with those of a pure S₂ state and with the nature of the S₁ → S₂ transition. Furthermore, the oxidation state definition in two-flashed (2F) structural models is practically impossible to elucidate. Our results advise caution in the extraction of electronic structure information solely from the literal interpretation of crystallographic models and call for re-evaluation of structural and mechanistic interpretations that presume exact correspondence of such models to specific catalytic intermediates of the OEC.

Identification of the protonation and oxidation states of the oxygen-evolving complex in the low-dose X-ray crystal structure of photosystem II

In photosystem II (PSII), the O3 and O4 sites of the Mn₄CaO₅ cluster form hydrogen bonds with D1-His337 and a water molecule (W539), respectively. The low-dose X-ray structure shows that these hydrogen bond distances differ between the two homogeneous monomer units (A and B) [Tanaka et al., J. Am Chem. Soc. 2017, 139, 1718]. We investigated the origin of the differences using a quantum mechanical/molecular mechanical (QM/MM) approach. QM/MM calculations show that the short O4-O_W539 hydrogen bond (~2.5 Å) of the B monomer is reproduced when O4 is protonated in the S₁ state. The short O3-Nε_His337 hydrogen bond of the A monomer is due to the formation of a low-barrier hydrogen bond between O3 and doubly-protonated D1-His337 in the overreduced states (S_-1 or S_-2). It seems plausible that the oxidation state differs between the two monomer units in the crystal.