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.

Triplet states in the reaction center of Photosystem II.

In oxygenic photosynthesis sunlight is harvested and funneled as excitation energy into the reaction center (RC) of Photosystem II (PSII), the site of primary charge separation that initiates the photosynthetic electron transfer chain. The chlorophyll ChlD1 pigment of the RC is the primary electron donor, forming a charge-separated radical pair with the vicinal pheophytin PheoD1 (ChlD1+PheoD1-). To avert charge recombination, the electron is further transferred to plastoquinone QA, whereas the hole relaxes to a central pair of chlorophylls (PD1PD2), subsequently driving water oxidation. Spin-triplet states can form within the RC when forward electron transfer is inhibited or back reactions are favored. This can lead to formation of singlet dioxygen, with potential deleterious effects. Here we investigate the nature and properties of triplet states within the PSII RC using a multiscale quantum-mechanics/molecular-mechanics (QM/MM) approach. The low-energy spectrum of excited singlet and triplet states, of both local and charge-transfer nature, is compared using range-separated time-dependent density functional theory (TD-DFT). We further compute electron paramagnetic resonance properties (zero-field splitting parameters and hyperfine coupling constants) of relaxed triplet states and compare them with available experimental data. Moreover, the electrostatic modulation of excited state energetics and redox properties of RC pigments by the semiquinone QA- is described. The results provide a detailed electronic-level understanding of triplet states within the PSII RC and form a refined basis for discussing primary and secondary electron transfer, charge recombination pathways, and possible photoprotection mechanisms in PSII.

Ionization Energies and Redox Potentials of Hydrated Transition Metal Ions: Evaluation of Domain-Based Local Pair Natural Orbital Coupled Cluster Approaches.

Hydrated transition metal ions are prototypical systems that can be used to model properties of transition metals in complex chemical environments. These seemingly simple systems present challenges for computational chemistry and are thus crucial in evaluations of quantum chemical methods for spin-state and redox energetics. In this work, we explore the applicability of the domain-based pair natural orbital implementation of coupled cluster (DLPNO-CC) theory to the calculation of ionization energies and redox potentials for hydrated ions of all first transition row (3d) metals in the 2+/3+ oxidation states, in connection with various solvation approaches. In terms of model definition, we investigate the construction of a minimally explicitly hydrated quantum cluster with a first and second hydration layer. We report on the convergence with respect to the coupled cluster expansion and the PNO space, as well as on the role of perturbative triple excitations. A recent implementation of the conductor-like polarizable continuum model (CPCM) for the DLPNO-CC approach is employed to determine self-consistent redox potentials at the coupled cluster level. Our results establish conditions for the convergence of DLPNO-CCSD(T) energetics and stress the absolute necessity to explicitly consider the second solvation sphere even when CPCM is used. The achievable accuracy for redox potentials of a practical DLPNO-based approach is, on average, 0.13 V. Furthermore, multilayer approaches that combine a higher-level DLPNO-CCSD(T) description of the first solvation sphere with a lower-level description of the second solvation layer are investigated. The present work establishes optimal and transferable methodological choices for employing DLPNO-based coupled cluster theory, the associated CPCM implementation, and cost-efficient multilayer derivatives of the approach for open-shell transition metal systems in complex environments.

Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts.

Hundreds of catalytic methods are developed each year to meet the demand for high-purity chiral compounds. The computational design of enantioselective organocatalysts remains a significant challenge, as catalysts are typically discovered through experimental screening. Recent advances in combining quantum chemical computations and machine learning (ML) hold great potential to propel the next leap forward in asymmetric catalysis. Within the context of quantum chemical machine learning (QML, or atomistic ML), the ML representations used to encode the three-dimensional structure of molecules and evaluate their similarity cannot easily capture the subtle energy differences that govern enantioselectivity. Here, we present a general strategy for improving molecular representations within an atomistic machine learning model to predict the DFT-computed enantiomeric excess of asymmetric propargylation organocatalysts solely from the structure of catalytic cycle intermediates. Mean absolute errors as low as 0.25 kcal mol-1 were achieved in predictions of the activation energy with respect to DFT computations. By virtue of its design, this strategy is generalisable to other ML models, to experimental data and to any catalytic asymmetric reaction, enabling the rapid screening of structurally diverse organocatalysts from available structural information.