Nigel W. Moriarty

During photosynthetic water oxidation, the Mn₄Ca cluster in Photosystem II progresses through five intermediate S_i (i = 0–4) states. X-ray crystallography studies have reported the insertion of one new O ligand during the formation of the S₃ state, but recent studies question the presence of this additional ligand based on cryo-EM and earlier room-temperature crystallography data. There is also controversy about whether the O-O bond interaction already occurs in the S₃ state or in the subsequent S₃ to S₀ transition. Here we report conventional high-resolution data for the S₁, S₂, and S₃ states to a resolution of ~1.9 Å, and anomalous diffraction data at two energies (9.5 keV and 7 keV), that was used to model the Mn positions, followed by determination of oxygen positions using the high-resolution maps. We show that the new oxygen atom, O_X (or O6), in the S₃ state is observable as a distinct peak without any restraints, confirming its ligation to Mn1 and Ca. The O_X-O5 distance is ~2.1 Å, supporting no strong interaction between them in the S₃ state, suggesting that if this is the O-O bond formation site, it is formed during the S₃ to S₀ transition initiated by the final oxidation of the cluster.

Multipolar scattering models, such as the transferable aspherical atom model, account for atomic chemical interactions and provide a more accurate representation of experimental data. However, the simpler independent atom model (IAM), which assumes non-interacting atoms, is the only model available in the most widely used macromolecular refinement programs. This is primarily because IAM offers a hard-to-beat combination of computational efficiency and modelling power at typical macromolecular resolutions. By contrast, more accurate multipolar modelling has historically been limited due to its computational cost and the absence of an interface between software capable of calculating structure factors and gradients based on multipolar models and software designed for macromolecular refinement. This work introduces pyDiSCaMB , a Python software package designed to integrate between the computational crystallography toolbox ( cctbx ) and the quantum crystallography library DiSCaMB ( Densities in Structural Chemistry and Molecular Biology ), thus enabling multipolar scattering models in Phenix 's toolkit. The implementation, features and capabilities of pyDiSCaMB are presented, the runtimes for the calculation of structure factor and target gradients with respect to atomic parameters are explored, and Fourier images of electrostatic potential, electron density and deformation maps are computed as illustrative examples. The pyDiSCaMB library will make multipolar modelling widely available to the structural biology community, potentially transforming refinement and model-building for both crystallography and cryogenic electron microscopy (cryoEM).

In macromolecular structure refinement, the low observation-to-parameter ratio and the lack of high-resolution data are countered by using a priori information in the form of restraints. Having accurate geometries of the chemical entities in the sample is paramount for generating accurate chemical restraints and, therefore, accurate macromolecular structures. In particular, it is desirable to have accurate restraints for known and novel ligand entities. Quantum mechanics (QM) can minimize the energy of a ligand by adjusting its geometry, and these geometries can be used to generate restraints for macromolecular refinement. This article describes a library of approximately 37 000 small molecules extracted from the Chemical Component Dictionary in the Protein Data Bank and minimized by density-functional QM. The library includes restraint files for use in crystallography or cryo-EM refinement, along with files suitable for molecular-dynamics simulation. Because the geometries are validated using the Cambridge Structural Database, the restraints library provides users with both functional restraints and minimized geometries. This work also provides procedures for generating new and accurate restraints.

A Working Group consisting of the co-authors of this paper was established in 2020 to re-evaluate the standard valence geometry used for the validation of nucleic acid structure models in the Protein Data Bank (PDB). This Working Group re-examined the dependence of Cambridge Structural Database (CSD) derived targets on base and sugar type, sugar pucker, and phosphate and glycosidic conformation, before comparing those targets with the geometry of a quality-filtered reference set of nucleic acid crystal structural models held in the PDB. This revealed that the valence bond and angle mean values are close to the CSD targets, but many parameters have highly non-Gaussian or even multimodal distributions. One explanation is the inconsistency of restraints used over time and by different refinement programs. The Working Group recommends a new validation scheme for use by the PDB. For this purpose, we have developed a new three-tier scale for outlier detection—graded as Preferred, Allowed, and Of Concern intervals—based on a combination of quality-curated reference data from the CSD and the PDB. The proposed approach to validation should lead to improved nucleic acid models in (future) PDB-deposited macromolecular structures.

Cryo-EM and X-ray crystallography provide crucial experimental data for obtaining atomic-detail models of biomacromolecules. Refining these models relies on library-based stereochemical data, which, in addition to being limited to known chemical entities, do not include meaningful noncovalent interactions. Quantum mechanical (QM) calculations could alleviate these issues but are too expensive for large molecules. Here we present a novel AI-enabled Quantum Refinement (AQuaRef) based on AIMNet2 machine learned interatomic potential (MLIP) mimicking QM at substantially lower computational costs. By refining 41 cryo-EM and 30 X-ray structures, we show that this approach yields atomic models with superior geometric quality compared to standard techniques, while maintaining an equal or better fit to experimental data. Notably, AQuaRef aids in determining proton positions, as illustrated in the challenging case of short hydrogen bonds in the parkinsonism-associated human protein DJ-1 and its bacterial homolog YajL.

In macromolecular structure refinement the low observation-to-parameter ratio and the lack of high-resolution data is countered by using a priori information in the form of restraints. Having accurate geometries of the chemical entities in the sample is paramount for generating accurate chemical restraints and, therefore, accurate macromolecular structures. In particular, it is desirable to have accurate restraints for known and novel ligand entities. Quantum Mechanics (QM) can minimise the energy of a ligand by adjusting its geometry, and these geometries can be used to generate restraints macromolecular refinement. We describe here a library of 37,000 small molecules extracted from the Chemical Components Dictionary in the Protein Data Bank and minimized by density functional QM. The library includes restraint files for use in crystallography or cryo-EM refinement, along with files suitable for molecular dynamics simulation. Because the geometries are validated, the restraints library provides users with both functional restraints and minimised geometries. This work also provides procedures for generating new and accurate restraints.

Summary: Our work reveals the structure of the active state of Methyl-Coenzyme M Reductase (MCR), the key and rate-limiting enzyme in biological methane formation. We find large differences between the active Ni(I) and inactive Ni(II) proteins and provide insight into how nature makes and breaks the C-H bond of methane. The Ni(II)-F430 center in inactive MCR contains four planar nitrogen ligands, a lower axial glutamine oxo, and an upper axial thiolate. The Ni(I)-enzyme replaces the axial ligands with a single water. The one-electron redox change results in movement of the Ni ion and upward swing of the β-lactam ring in the tetrapyrrole coupled to a domino-like protein quake through second sphere residues, inter-subunit interactions, a substrate tunnel, affecting even the dimensions of the unit cell. These structural changes lead Ni(I)-MCR to release a charge clamp that, in the Ni(II) state, locks down substrate Coenzyme B. Determining the Ni(I)-MCR structure required development of rigorous anaerobic crystallographic techniques. Validation of the MCR redox state was accomplished by in-line and parallel spectroscopic and unit cell analyses. This structure has large implications for developing technologies to limit methane emissions and efficiently produce biofuels. Methodology described here will enhance structural biology for other oxygen-sensitive enzymes.

Histidine can be protonated on either or both of the two N atoms of the imidazole moiety. Each of the three possible forms occurs as a result of the stereochemical environment of the histidine side chain. In an atomic model, comparing the possible protonation states in situ, looking at possible hydrogen bonding and metal coordination, it is possible to predict which is most likely to be correct. A more direct method is described that uses quantum-mechanical methods to calculate, also in situ, the minimum geometry and energy for comparison, and therefore to more accurately identify the most likely protonation state.

Cryo-EM and X-ray crystallography provide crucial experimental data for obtaining atomic-detail models of biomacromolecules. Refining these models relies on library-based stereochemical restraints, which, in addition to being limited to known chemical entities, do not include meaningful noncovalent interactions relying solely on nonbonded repulsions. Quantum mechanical (QM) calculations could alleviate these issues but are too expensive for large molecules. We present a novel AI-enabled Quantum Refinement (AQuaRef) based on AIMNet2 neural network potential mimicking QM at substantially lower computational costs. By refining 41 cryo-EM and 30 X-ray structures, we show that this approach yields atomic models with superior geometric quality compared to standard techniques, while maintaining an equal or better fit to experimental data. Notably, AQuaRef aids in determining proton positions, as illustrated in the challenging case of short hydrogen bonds in the parkinsonism-associated human protein DJ-1 and its bacterial homolog YajL.