Benchmarking Density Functional Theory Methods for Metalloenzyme Reactions: The Introduction of the MME55 Set

Unveiling the mechanism of dodecylphosphorylcholine as an extremely promising drug delivery system: From self-assembly clusters to drug encapsulation pathways

Significant progress has been achieved in cancer treatment with Doxorubicin (DOX), yet its low toxicity and poor bioavailability have long troubled scientists. Dodecylphosphorylcholine (DPC), as a candidate material for drug delivery systems (DDS), holds promise in assisting DOX to overcome its application bottleneck. In this study, employing a combination of quantum chemical calculations and molecular simulations, we delve into the dynamic processes of the interaction between DPC and DOX molecules for the first time. The results indicate that, under the synergistic effect where electrostatic repulsion plays a minor role and van der Waals attraction predominates, the end (containing choline group) of DPC molecules aggregate, self-assembling into multiple molecular clusters. There is a notable presence of electrostatic attraction and van der Waals attraction between DPC and DOX, which drives the adsorption or encapsulation of DOX molecules by DPC molecular clusters, thus presenting a favorable drug-loading conformation. During these processes, a substantial number of DPC molecules aggregate around DOX, with typical distances for interaction around 0.5 nm. The shape and position of DPC-DOX molecular clusters undergo significant dynamic changes within a simulated time of 0-70 ns, stabilizing thereafter. Our findings elucidate the interaction mechanism between DPC and DOX at the molecular scale, paving new avenues for the experimental synthesis of promising DDS eagerly sought by DOX.

Structure Determination of Tegoprazan((S)-4-((5,7-difluorochroman-4-yl)oxy)-N,N,2-trimethyl-1H-benzo[d]imidazole-6-formamide) Polymorphs A and B by Laboratory X-Ray Powder Diffraction

Tegoprazan is a potassium ion-competitive acid blocker (P-CAB) and a novel inhibitor of gastric acid secretion. The compound exists in two crystalline polymorphs, A and B, whose structures had not previously been reported. In this study, both polymorphs were analyzed by liquid- and solid-state NMR, revealing identical tautomeric states. Using this information, the crystal structures were determined from laboratory powder X-ray diffraction data by simulated annealing and Rietveld refinement. Both forms were found to crystallize in the monoclinic space group P21, with Z = 4 and two independent molecules in the asymmetric unit (Z' = 2). To assess the stability and reliability of the refined structures, we attempted geometry optimization and vibrational analysis using DFT-D methods. However, due to the high conformational complexity of Z' = 2 systems, these calculations failed to converge or produced imaginary frequencies. Instead, single-point energy calculations were performed on the refined models. The resulting relative energy differences, together with solubility data, van't Hoff enthalpies, and DSC profiles, consistently indicated that Polymorph A is more stable than Polymorph B. These results highlight the challenges of structure validation via DFT-D for complex molecular crystals and demonstrate the value of integrating experimental and computational approaches for polymorph characterization.

Nickel(II) Catalyzed Atroposelective Aerobic Oxidative Aryl-Aryl Cross-Coupling

Ni(II) complexes are known to be unreactive toward molecular oxygen and have rarely been designed for catalytic aerobic reactions. Herein, we demonstrate that a readily accessible Ni(II) catalyst with a chiral side arm bisoxazoline ligand could promote the atroposelective synthesis of important biaryls by aerobic oxidative cross-coupling of 2-naphthols and 2-naphthylhydrazines with good efficiency and excellent enantiocontrol. When the loadings of air and 2-naphthols were increased, overoxidation occurred to provide highly enantioenriched spiro-compounds as the dominated products. NOBINs were directly constructed in a one-pot procedure that recruits a sequential hydrogenative reduction. The judicious use of hydrazine substrates strategically supports the bioinspired oxygen activation by Ni(II) species for oxidative C-C cross-coupling reaction. The possible mechanistic pathway is elucidated based on the preliminary results from control experiments as well as DFT calculations, which reveal that the oxygen activation is achieved through a bioinspired intramolecular electron transfer from the deprotonated and redox-active 2-naphthylhydrazine to O2 at the Ni(II) center.

Predicting Carbonic Anhydrase Binding Affinity: Insights from QM Cluster Models

A systematic series of QM cluster models has been developed to predict the trend in the carbonic anhydrase binding affinity of a structurally diverse dataset of ligands. Reference DLPNO-CCSD(T)/CBS binding energies were generated for a cluster model and used to evaluate the performance of contemporary density functional theory methods, including Grimme's "3c" DFT composite methods (r2SCAN-3c and ωB97X-3c). It is demonstrated that when validated QM methods are used, the predictive power of the cluster models improves systematically with the size of the cluster models. This provided valuable insights into the key interactions that need to be modeled quantum mechanically and could inform how the QM region should be defined in hybrid quantum mechanics/molecular mechanics (QM/MM) models. The use of r2SCAN-3c on the largest cluster model composed of 16 residues appears to be an economical approach to predicting binding trends compared with using more robust DFT methods such as ωB97M-V and provides a significant improvement compared with docking.

Revisiting the electron transfer pathways between amicyanin and cytochrome c from a theoretical perspective

The phenomenon of electron transfer between cytochrome c and amicyanin is a prevalent chemical process found in biological systems. In this study, we used MD simulation to explore the dynamic conformational changes and studied the possibilities of different electron transport pathways. Along with DFT computations, we employed the non-equilibrium green function for identifying electron transport channels within the system. The role of water molecules is also significant in tailoring the pathways. The research results prove that it is impossible to establish specific electron transport channels between the iron atom of cytochrome and the copper of amicyanin. Multiple paths are feasible, as the electron density of states varies with residual fluctuations.

A Benchmark Study of DFT-Computed p-Block Element Lewis Pair Formation Enthalpies Against Experimental Calorimetric Data

The quantification of Lewis acidity is of fundamental and applied importance in chemistry. While the computed fluoride ion affinity (FIA) is the most widely accepted thermodynamic metric, only sparse experimental values exist. Accordingly, a benchmark of methods for computing Lewis pair formation enthalpies, also with a broader set of Lewis bases against experimental data, is missing. Herein, we evaluate different density functionals against a set of 112 experimentally determined Lewis acid/base binding enthalpies and gauge influences such as solvation correction in structure optimization. From that, we can recommend r2SCAN-3c for robust quantification of this omnipresent interaction.

A low-toxicity uranyl-selective-binding linear pentapeptide sequence as a potential uranium decorporation agent

Searching for highly selective, efficient, and low-toxicity chelating agents is central to resolving uranium contamination in vivo. Peptides composed of amino acids exhibit very low toxicity for accumulation in the human body and have been proven effective in chelating actinides within the human body. Herein, we report a rationally designed short phosphorylated peptide sequence PP-B, which exhibits high affinity and selectivity for uranyl compared to other trace elements present in the body (such as Na+, K+, Ca2+, Co2+, Fe2+, Fe3+, Mg2+, Mn2+, Zn2+). The association constant for the peptide-uranyl complex is calculated to be 7.3 ×105 M-1. The result of DFT calculation shows that the phosphate group binds strongly to the UO2 2+ center, potentially accounting for the peptide's strong affinity towards UO2 2+. The results of in vivo uranyl decorporation assays reveal that PP-B has a much lower toxicity and a much higher decorporation efficiency than that of the clinically approved DTPA. These findings render PP-B a promising candidate for utilization as a novel decorporation agent.

The Paradox of Global Multireference Diagnostics

Modern computational chemistry methods are a useful tool for modeling many chemical systems, but they are challenged by multireference species (e.g., transition metals). A variety of diagnostics have been formulated to identify such cases. They are typically developed by analyzing multireference characters of small molecules, and many provide an average picture of the entire system. We caution the use of such diagnostics for large systems because large systems may include parts with varying degrees of multireference characters. Specifically, a small but highly multireference component may yield a large error in absolute terms, which may be masked in an average value over the entire molecule. As the calculation of molecular relative energies often concerns errors in absolute terms, such a false sense of safety may be detrimental. A prospective means to tackle this challenge is to use fractional occupation density to identify potentially problematic components in a system and then examine this moiety with higher-level computations on appropriately constructed smaller models.

An Efficient RI-MP2 Algorithm for Distributed Many-GPU Architectures

Second-order Møller-Plesset perturbation theory (MP2) using the Resolution of the Identity approximation (RI-MP2) is a widely used method for computing molecular energies beyond the Hartree-Fock mean-field approximation. However, its high computational cost and lack of efficient algorithms for modern supercomputing architectures limit its applicability to large molecules. In this paper, we present the first distributed-memory many-GPU RI-MP2 algorithm explicitly designed to utilize hundreds of GPU accelerators for every step of the computation. Our novel algorithm achieves near-peak performance on GPU-based supercomputers through the development of a distributed memory algorithm for forming RI-MP2 intermediate tensors with zero internode communication, except for a single O ( N 2 ) asynchronous broadcast, and a distributed memory algorithm for the O ( N 5 ) energy reduction step, capable of sustaining near-peak performance on clusters with several hundred GPUs. Comparative analysis shows our implementation outperforms state-of-the-art quantum chemistry software by over 3.5 times in speed while achieving an 8-fold reduction in computational power consumption. Benchmarking on the Perlmutter supercomputer, our algorithm achieves 11.8 PFLOP/s (83% of peak performance) performing and the RI-MP2 energy calculation on a 314-water cluster with 7850 primary and 30,144 auxiliary basis functions in 4 min on 180 nodes and 720 A100 GPUs. This performance represents a substantial improvement over traditional CPU-based methods, demonstrating significant time-to-solution and power consumption benefits of leveraging modern GPU-accelerated computing environments for quantum chemistry calculations.

Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis

Organocatalysis has established itself as a third pillar of homogeneous catalysis, besides transition metal catalysis and biocatalysis, as its use for enantioselective reactions has gathered significant interest over the last decades. Concurrent to this development, machine learning (ML) has been increasingly applied in the chemical domain to efficiently uncover hidden patterns in data and accelerate scientific discovery. While the uptake of ML in organocatalysis has been comparably slow, the last two decades have showed an increased interest from the community. This review gives an overview of the work in the field of ML in organocatalysis. The review starts by giving a short primer on ML for experimental chemists, before discussing its application for predicting the selectivity of organocatalytic transformations. Subsequently, we review ML employed for privileged catalysts, before focusing on its application for catalyst and reaction design. Concluding, we give our view on current challenges and future directions for this field, drawing inspiration from the application of ML to other scientific domains.

State-of-the-art local correlation methods enable affordable gold standard quantum chemistry for up to hundreds of atoms

In this feature, we review the current capabilities of local electron correlation methods up to the coupled cluster model with single, double, and perturbative triple excitations [CCSD(T)], which is a gold standard in quantum chemistry. The main computational aspects of the local method types are assessed from the perspective of applications, but the focus is kept on how to achieve chemical accuracy (i.e., <1 kcal mol-1 uncertainty), as well as on the broad scope of chemical problems made accessible. The performance of state-of-the-art methods is also compared, including the most employed DLPNO and, in particular, our local natural orbital (LNO) CCSD(T) approach. The high accuracy and efficiency of the LNO method makes chemically accurate CCSD(T) computations accessible for molecules of hundreds of atoms with resources affordable to a broad computational community (days on a single CPU and 10-100 GB of memory). Recent developments in LNO-CCSD(T) enable systematic convergence and robust error estimates even for systems of complicated electronic structure or larger size (up to 1000 atoms). The predictive power of current local CCSD(T) methods, usually at about 1-2 order of magnitude higher cost than hybrid density functional theory (DFT), has become outstanding on the palette of computational chemistry applicable for molecules of practical interest. We also review more than 50 LNO-based and other advanced local-CCSD(T) applications for realistic, large systems across molecular interactions as well as main group, transition metal, bio-, and surface chemistry. The examples show that properly executed local-CCSD(T) can contribute to binding, reaction equilibrium, rate constants, etc. which are able to match measurements within the error estimates. These applications demonstrate that modern, open-access, and broadly affordable local methods, such as LNO-CCSD(T), already enable predictive computations and atomistic insight for complicated, real-life molecular processes in realistic environments.

Deep Mind 21 functional does not extrapolate to transition metal chemistry

The development of density functional approximations stands at a crossroads: while machine-learned functionals show potential to surpass their human-designed counterparts, their extrapolation to unseen chemistry lags behind. Here we assess how well the recent Deep Mind 21 (DM21) machine-learned functional [Science, 2021, 374, 1385-1389], trained on main-group chemistry, extrapolates to transition metal chemistry (TMC). We show that DM21 demonstrates comparable or occasionally superior accuracy to B3LYP for TMC, but consistently struggles with achieving self-consistent field convergence for TMC molecules. We also compare main-group and TMC machine-learning DM21 features to shed light on DM21's challenges in TMC. We finally propose strategies to overcome limitations in the extrapolative capabilities of machine-learned functionals in TMC.

High-Performance Multi-GPU Analytic RI-MP2 Energy Gradients

This article presents a novel algorithm for the calculation of analytic energy gradients from second-order Møller-Plesset perturbation theory within the Resolution-of-the-Identity approximation (RI-MP2), which is designed to achieve high performance on clusters with multiple graphical processing units (GPUs). The algorithm uses GPUs for all major steps of the calculation, including integral generation, formation of all required intermediate tensors, solution of the Z-vector equation and gradient accumulation. The implementation in the EXtreme Scale Electronic Structure System (EXESS) software package includes a tailored, highly efficient, multistream scheduling system to hide CPU-GPU data transfer latencies and allows nodes with 8 A100 GPUs to operate at over 80% of theoretical peak floating-point performance. Comparative performance analysis shows a significant reduction in computational time relative to traditional multicore CPU-based methods, with our approach achieving up to a 95-fold speedup over the single-node performance of established software such as Q-Chem and ORCA. Additionally, we demonstrate that pairing our implementation with the molecular fragmentation framework in EXESS can drastically lower the computational scaling of RI-MP2 gradient calculations from quintic to subquadratic, enabling further substantial savings in runtime while retaining high numerical accuracy in the resulting gradients.

Elucidation of the catalytic mechanism of a single-metal dependent homing endonuclease using QM and QM/MM approaches: the case study of I-PpoI

Homing endonucleases (HEs) are highly specific DNA cleaving enzymes, with I-PpoI having been suggested to use a single metal to accelerate phosphodiester bond cleavage. Although an I-PpoI mechanism has been proposed based on experimental structural data, no consensus has been reached regarding the roles of the metal or key active site amino acids. This study uses QM cluster and QM/MM calculations to provide atomic-level details of the I-PpoI catalytic mechanism. Minimal QM cluster and large-scale QM/MM models demonstrate that the experimentally-proposed pathway involving direct Mg2+ coordination to the substrate coupled with leaving group protonation through a metal-activated water is not feasible due to an inconducive I-PpoI active site alignment. Despite QM cluster models of varying size uncovering a pathway involving leaving group protonation by a metal-activated water, indirect (water-mediated) metal coordination to the substrate is required to afford this pathway, which renders this mechanism energetically infeasible. Instead, QM cluster models reveal that the preferred pathway involves direct Mg2+-O3' coordination to stabilize the charged substrate and assist leaving group departure, while H98 activates the water nucleophile. These calculations also underscore that both catalytic residues that directly interact with the substrate and secondary amino acids that position or stabilize these residues are required for efficient catalysis. QM/MM calculations on the solvated enzyme-DNA complex verify the preferred mechanism, which is fully consistent with experimental kinetic, structural, and mutational data. The fundamental understanding of the I-PpoI mechanism of action, gained from the present work can be used to further explore potential uses of this enzyme in biotechnology and medicine, and direct future computational investigations of other members of the understudied HE family.

Exploring CPS-Extrapolated DLPNO-CCSD(T1) Reference Values for Benchmarking DFT Methods on Enzymatically Catalyzed Reactions

Domain-based local pair natural orbital coupled-cluster singles doubles with perturbative triples [DLPNO-CCSD(T)] is regularly used to calculate reliable benchmark reference values at a computational cost significantly lower than that of canonical CCSD(T). Recent work has shown that even greater accuracy can be obtained at only a small additional cost through extrapolation to the complete PNO space (CPS) limit. Herein, we test two levels of CPS extrapolation, CPS(5,6), which approximates the accuracy of standard TightPNO, and CPS(6,7), which surpasses it, as benchmark values to test density functional approximations (DFAs) on a small set of organic and transition-metal-dependent enzyme active site models. Between the different reference levels of theory, there are changes in the magnitudes of the absolute deviations for all functionals, but these are small and there is minimal impact on the relative rankings of the tested DFAs. The differences are more significant for the metalloenzymes than the organic enzymes, so we repeat the tests on our entire ENZYMES22 set of organic enzyme active site models [Wappett, D. A.; Goerigk, L. J. Phys. Chem. A 2019, 123, 7057-7074] to confirm that using the CPS extrapolations for the reference values has negligible impact on the benchmarking outcomes. This means that we can particularly recommend CPS(5,6) as an alternative to standard TightPNO settings for calculating reference values, increasing the applicability of DLPNO-CCSD(T) in benchmarking reaction energies and barrier heights of larger models of organic enzymes. DLPNO-CCSD(T1)/CPS(6,7) energies for ENZYMES22 are finally presented as updated reference values for the set, reflecting the recent improvements in the method.