Breaking a Molecular Scaling Relationship Using an Iron-Iron Fused Porphyrin Electrocatalyst for Oxygen Reduction.

The design of efficient electrocatalysts is limited by scaling relationships governing trade-offs between thermodynamic and kinetic performance metrics. This ″iron law″ of electrocatalysis arises from synthetic design strategies, where structural alterations to a catalyst must balance nucleophilic versus electrophilic character. Efforts to circumvent this fundamental impasse have focused on bioinspired applications of extended coordination spheres and charged sites proximal to a catalytic center. Herein, we report evidence for breaking a molecular scaling relationship involving electrocatalysis of the oxygen reduction reaction (ORR) by leveraging ligand design. We achieve this using a binuclear catalyst (a diiron porphyrin), featuring a macrocyclic ligand with extended electronic conjugation. This ligand motif delocalizes electrons across the molecular scaffold, improving the catalyst's nucleophilic and electrophilic character. As a result, our binuclear catalyst exhibits low overpotential and high catalytic turnover frequency, breaking the traditional trade-off between these two metrics.

A Resilient Platform for the Discrete Functionalization of Gold Surfaces Based on N-Heterocyclic Carbene Self-Assembled Monolayers.

We describe the synthesis and characterization of a versatile platform for gold functionalization, based on self-assembled monolayers (SAMs) of distal-pyridine-functionalized N-heterocyclic carbenes (NHC) derived from bis(NHC) Au(I) complexes. The SAMs are characterized using polarization-modulation infrared reflectance-absorption spectroscopy, surface-enhanced Raman spectroscopy, and X-ray photoelectron spectroscopy. The binding mode is examined computationally using density functional theory, including calculations of vibrational spectra and direct comparisons to the experimental spectroscopic signatures of the monolayers. Our joint computational and experimental analyses provide structural information about the SAM binding geometries under ambient conditions. Additionally, we examine the reactivity of the pyridine-functionalized SAMs toward H2SO4 and W(CO)5(THF) and verify the preservation of the introduced functionality at the interface. Our results demonstrate the versatility of N-heterocyclic carbenes as robust platforms for on-surface acid-base and ligand exchange reactions.

Dehydrogenation and Transfer Hydrogenation of Alkenones to Phenols and Ketones on Carbon-Supported Noble Metals.

The catalytic dehydrogenation of substituted alkenones on noble metal catalysts supported on carbon (Pt/C, Pd/C, Rh/C, and Ru/C) was investigated in an organic phase under inert conditions. The dehydrogenation and semihydrogenation of the enone starting materials resulted in aromatic compounds (primary products), saturated cyclic ketones (secondary products), and cyclic alcohols (minor products). Pd/C exhibits the highest catalytic activity, followed by Pt/C and Rh/C. Aromatic compounds remain the primary products, even in the presence of hydrogen donors. Joint experimental and theoretical analyses showed that the four catalytic materials stabilize a common dienol intermediate on the metal surfaces, formed by keto-enol tautomerization. This intermediate subsequently forms aromatic products upon dehydrogenation. The binding orientation of the enone reactants on the catalytic surface is strongly metal-dependent, as the M-O bond distance changes substantially according to the metal. The longer M-O bonds (Pt: 2.84 Å > Pd: 2.23 Å > Rh: 2.17 Å > Ru: 2.07 Å) correlate with faster reaction rates and more favorable keto-enol tautomerization, as shorter distances correspond to a more stabilized starting material. Tautomerization is shown to occur via a stepwise surface-assisted pathway. Overall, each of the studied metals exhibits a distinct balance of enthalpy and entropy of activation (ΔH°‡, ΔS°‡), offering unique possibilities in the realm of enone dehydrogenation reactions that can be achieved by suitable selection of catalytic materials.

BODIPY Chemisorbed on SnO2 and TiO2 Surfaces for Photoelectrochemical Applications.

Advancement toward dye-sensitized photoelectrochemical cells to produce solar fuels by solar-driven water splitting requires a photosensitizer that is firmly attached to the semiconducting photoelectrodes. Covalent binding enhances the efficiency of electron injection from the photoexcited dye into the metal oxide. Optimization of charge transfer, efficient electron injection, and minimal electron-hole recombination are mandatory for achieving high efficiencies. Here, a BODIPY-based dye exploiting a novel surface-anchoring mode via boron is compared to a similar dye bound by a traditional carboxylic acid anchoring group. Through terahertz and transient absorption spectroscopic studies, along with interfacial electron transfer simulations, we find that, when compared to the traditional carboxylic acid anchoring group, electron injection of boron-bound BODIPY is faster into both TiO2 and SnO2. Although the surface coverage is low compared with carboxylic acids, the binding stability is improved over a wide range of pH. Subsequent photoelectrochemical studies using a sacrificial electron donor showed that this combined dye and anchoring group maintained photocurrent with good stability over long-time irradiation. This recently discovered binding mode of BODIPY shows excellent electron injection and good stability over time, making it promising for future investigations.

Occupancy Analysis of Water Molecules inside Channels within 25 Å Radius of the Oxygen-Evolving Center of Photosystem II in Molecular Dynamics Simulations.

At room temperature and neutral pH, the oxygen-evolving center (OEC) of photosystem II (PSII) catalyzes water oxidation. During this process, oxygen is released from the OEC, while substrate waters are delivered to the OEC and protons are passed from the OEC to the lumen through water channels known as the narrow or the O4 channel, broad or the Cl1 channel, and large or the O1 channel. Protein residues lining the surfaces of these channels play a critical role in stabilizing the hydrogen-bonding networks that assist in the process. We carried out an occupancy analysis to better understand the structural and possible substrate water dynamics in full PSII monomer molecular dynamics (MD) trajectories in both the S1 and S2 states. We find that the equilibrated positions of water molecules derived from MD-derived electron density maps largely match the experimentally observed positions in crystallography. Furthermore, the occupancy reduction in MD simulations of some water molecules inside the single-filed narrow channel also correlates well with the crystallographic data during a structural transition when the S1 state of the OEC advances to the S2 state. The overall reduced occupancies of water molecules are the source of their "vacancy-hopping" dynamic nature inside these channels, unlike water molecules inside an ice lattice where all water molecules have a fixed unit occupancy. We propose on the basis of findings in our structural and molecular dynamics analysis that the water molecule occupying a pocket formed by D1-D61, D1-S169, and O4 of the OEC could be the last steppingstone to enter into the OEC and that the broad channel may be favored for proton transfer.

ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation.

The incredible capabilities of generative artificial intelligence models have inevitably led to their application in the domain of drug discovery. Within this domain, the vastness of chemical space motivates the development of more efficient methods for identifying regions with molecules that exhibit desired characteristics. In this work, we present a computationally efficient active learning methodology and demonstrate its applicability to targeted molecular generation. When applied to c-Abl kinase, a protein with FDA-approved small-molecule inhibitors, the model learns to generate molecules similar to the inhibitors without prior knowledge of their existence and even reproduces two of them exactly. We also show that the methodology is effective for a protein without any commercially available small-molecule inhibitors, the HNH domain of the CRISPR-associated protein 9 (Cas9) enzyme. To facilitate implementation and reproducibility, we made all of our software available through the open-source ChemSpaceAL Python package.

Substrate-independent activation pathways of the CRISPR-Cas9 HNH nuclease.

A hallmark of tightly regulated high-fidelity enzymes is that they become activated only after encountering cognate substrates, often by an induced-fit mechanism rather than conformational selection. Upon analysis of molecular dynamics trajectories, we recently discovered that the Cas9 HNH domain exists in three conformations: 1) Y836 (which is two residues away from the catalytic D839 and H840 residues) is hydrogen bonded to the D829 backbone amide, 2) Y836 is hydrogen bonded to the backbone amide of D861 (which is one residue away from the third catalytic residue N863), and 3) Y836 is not hydrogen bonded to either residue. Each of the three conformers differs from the active state of HNH. The conversion between the inactive and active states involves a local unfolding-refolding process that displaces the Cα and side chain of the catalytic N863 residue by ∼5 Å and ∼10 Å, respectively. In this study, we report the two largest principal components of coordinate variance of the HNH domain throughout molecular dynamics trajectories to establish the interconversion pathways of these conformations. We show that conformation 2 is an obligate step between conformations 1 and 3, which are not directly interconvertible without conformation 2. The loss of hydrogen bonding of the Y836 side chain in conformation 3 likely plays an essential role in activation during local unfolding-refolding of an α-helix containing the catalytic N863. Three single Lys-to-Ala mutants appear to eliminate this substrate-independent activation pathway of the wild-type HNH nuclease, thereby enhancing the fidelity of HNH cleavage.

Triple-action inhibitory mechanism of allosteric TYK2-specific inhibitors.

Deucravacitinib, 6-(cyclopropanecarbonylamido)-4-[2-methoxy-3-(1-methyl-1,2,4-triazol-3-yl)anilino]-N-(trideuteriomethyl)pyridazine-3-carboxamide, is a highly selective inhibitor of protein tyrosine kinase 2 (TYK2) that targets the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway. The structural basis for its selectivity and allosteric inhibition remains poorly understood. Here, we investigate the inhibition mechanism through analysis of available structures relevant to the STAT pathway, including crystal structures of the truncated TYK2 FERM-SH2 domain bound to the IFNα type I receptor (IFNαR1) and the truncated TYK2 JH2-JH1 domain. Our computational analysis provides a mechanistic hypothesis for the relatively rapid interferon-induced gene expression mediated by TYK2 relative to other cytokines. We find that deucravacitinib inhibits TYK2 kinase in three distinct states: the autoinhibited state and two activated states for autophosphorylation and phosphorylation of downstream protein substrates. Its binding to the TYK2 pseudokinase domain in the autoinhibited state restricts the essential dynamics of the TYK2 kinase domain required for kinase activity. Furthermore, it binds competitively with ATP in the pseudokinase domain, and also directly prevents formation of the active state of TYK2 through steric clashes.

Feed-Forward Neural Network for Predicting Enantioselectivity of the Asymmetric Negishi Reaction.

Density functional theory (DFT) is a powerful tool to model transition state (TS) energies to predict selectivity in chemical synthesis. However, a successful multistep synthesis campaign must navigate energetically narrow differences in pathways that create some limits to rapid and unambiguous application of DFT to these problems. While powerful data science techniques may provide a complementary approach to overcome this problem, doing so with the relatively small data sets that are widespread in organic synthesis presents a significant challenge. Herein, we show that a small data set can be labeled with features from DFT TS calculations to train a feed-forward neural network for predicting enantioselectivity of a Negishi cross-coupling reaction with P-chiral hindered phosphines. This approach to modeling enantioselectivity is compared with conventional approaches, including exclusive use of DFT energies and data science approaches, using features from ligands or ground states with neural network architectures.

Phenolic chalcones as agents against Trichomonas vaginalis.

Trichomonas vaginalis, a flagellated and anaerobic protozoan, is a causative agent of trichomoniasis. This disease is among the world's most common non-viral sexually transmitted infection. A single class drug, nitroimidazoles, is currently available for the trichomoniasis treatment. However, resistant isolates have been identified from unsuccessfully treated patients. Thus, there is a great challenge for a discovery of innovative anti-T. vaginalis agents. As part of our ongoing search for antiprotozoal chalcones, we designed and synthesized a series of 21 phenolic chalcones, which were evaluated against T. vaginalis trophozoites. Structure-activity relationship indicated hydroxyl group plays a role key in antiprotozoal activity. 4'-Hydroxychalcone (4HC) was the most active compound (IC50 = 27.5 µM) and selected for detailed bioassays. In vitro and in vivo evaluations demonstrated 4HC was not toxic against human erythrocytes and Galleria mellonella larvae. Trophozoites of T. vaginalis were treated with 4HC and did not present significant reactive oxygen species (ROS) accumulation. However, compound 4HC was able to increase ROS accumulation in neutrophils coincubated with T. vaginalis. qRT-PCR Experiments indicated that 4HC did not affect the expression of pyruvate:ferredoxin oxidoreductase (PFOR) and ß-tubulin genes. In silico simulations, using purine nucleoside phosphorylase of T. vaginalis (TvPNP), corroborated 4HC as a promising ligand. Compound 4HC was able to establish interactions with residues D21, G20, M180, R28, R87 and T90 through hydrophobic interactions, π-donor hydrogen bond and hydrogen bonds. Altogether, these results open new avenues for phenolic chalcones to combat trichomoniasis, a parasitic neglected infection.

A quantitative assessment of (bacterio)chlorophyll assignments in the cryo-EM structure of the Chloracidobacterium thermophilum reaction center.

Chlorophylls and bacteriochlorophylls are the primary pigments used by photosynthetic organisms for light harvesting, energy transfer, and electron transfer. Many molecular structures of (bacterio)chlorophyll-containing protein complexes are available, some of which contain mixtures of different (bacterio)chlorophyll types. Differentiating these, which sometimes are structurally similar, is challenging but is required for leveraging structural data to gain functional insight. The reaction center complex from Chloroacidobacterium thermophilum has a hybrid (bacterio)chlorophyll antenna system containing both chlorophyll a and bacteriochlorophyll a molecules. The recent availability of its cryogenic electron microscopy (cryo-EM) structure provides an opportunity for a quantitative analysis of their identities and chemical environments. Here, we describe a theoretical basis for differentiating chlorophyll a and bacteriochlorophyll a in a cryo-EM map, and apply the approach to the experimental cryo-EM maps of the (bacterio)chlorophyll sites of the chloroacidobacterial reaction center. The comparison reveals that at ~ 2.2-Å resolution, chlorophyll a and bacteriochlorophyll a are easily distinguishable, but the orientation of the bacteriochlorophyll a acetyl moiety is not; however, the latter can confidently be assigned by identifying a hydrogen bond donor from the protein environment. This study reveals the opportunities and challenges in assigning (bacterio)chlorophyll types in structural biology, the accuracy of which is vital for downstream investigations.

Mapping Molecular Hamiltonians into Hamiltonians of Modular cQED Processors.

We introduce a general method based on the operators of the Dyson-Masleev transformation to map the Hamiltonian of an arbitrary model system into the Hamiltonian of a circuit Quantum Electrodynamics (cQED) processor. Furthermore, we introduce a modular approach to programming a cQED processor with components corresponding to the mapping Hamiltonian. The method is illustrated as applied to quantum dynamics simulations of the Fenna-Matthews-Olson (FMO) complex and the spin-boson model of charge transfer. Beyond applications to molecular Hamiltonians, the mapping provides a general approach to implement any unitary operator in terms of a sequence of unitary transformations corresponding to powers of creation and annihilation operators of a single bosonic mode in a cQED processor.

ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation.

The incredible capabilities of generative artificial intelligence models have inevitably led to their application in the domain of drug discovery. Within this domain, the vastness of chemical space motivates the development of more efficient methods for identifying regions with molecules that exhibit desired characteristics. In this work, we present a computationally efficient active learning methodology that requires evaluation of only a subset of the generated data in the constructed sample space to successfully align a generative model with respect to a specified objective. We demonstrate the applicability of this methodology to targeted molecular generation by fine-tuning a GPT-based molecular generator toward a protein with FDA-approved small-molecule inhibitors, c-Abl kinase. Remarkably, the model learns to generate molecules similar to the inhibitors without prior knowledge of their existence, and even reproduces two of them exactly. We also show that the methodology is effective for a protein without any commercially available small-molecule inhibitors, the HNH domain of the CRISPR-associated protein 9 (Cas9) enzyme. We believe that the inherent generality of this method ensures that it will remain applicable as the exciting field of in silico molecular generation evolves. To facilitate implementation and reproducibility, we have made all of our software available through the open-source ChemSpaceAL Python package.

Connected Component Analysis of Dynamical Perturbation Contact Networks.

Describing protein dynamical networks through amino acid contacts is a powerful way to analyze complex biomolecular systems. However, due to the size of the systems, identifying the relevant features of protein-weighted graphs can be a difficult task. To address this issue, we present the connected component analysis (CCA) approach that allows for fast, robust, and unbiased analysis of dynamical perturbation contact networks (DPCNs). We first illustrate the CCA method as applied to a prototypical allosteric enzyme, the imidazoleglycerol phosphate synthase (IGPS) enzyme from Thermotoga maritima bacteria. This approach was shown to outperform the clustering methods applied to DPCNs, which could not capture the propagation of the allosteric signal within the protein graph. On the other hand, CCA reduced the DPCN size, providing connected components that nicely describe the allosteric propagation of the signal from the effector to the active sites of the protein. By applying the CCA to the IGPS enzyme in different conditions, i.e., at high temperature and from another organism (yeast IGPS), and to a different enzyme, i.e., a protein kinase, we demonstrated how CCA of DPCNs is an effective and transferable tool that facilitates the analysis of protein-weighted networks.

Development of a new quantum trajectory molecular dynamics framework.

An extension to the wave packet description of quantum plasmas is presented, where the wave packet can be elongated in arbitrary directions. A generalized Ewald summation is constructed for the wave packet models accounting for long-range Coulomb interactions and fermionic effects are approximated by purpose-built Pauli potentials, self-consistent with the wave packets used. We demonstrate its numerical implementation with good parallel support and close to linear scaling in particle number, used for comparisons with the more common wave packet employing isotropic states. Ground state and thermal properties are compared between the models with differences occurring primarily in the electronic subsystem. Especially, the electrical conductivity of dense hydrogen is investigated where a 15% increase in DC conductivity can be seen in our wave packet model compared with other models. This article is part of the theme issue 'Dynamic and transient processes in warm dense matter'.

Proton-Tunneling Dynamics along Low-Barrier Hydrogen Bonds: A Full-Dimensional Instanton Study of 6-Hydroxy-2-formylfulvene.

Understanding the dynamics of proton transfer along low-barrier hydrogen bonds remains an outstanding challenge of great fundamental and practical interest, reflecting the central role of quantum effects in reactions of chemical and biological importance. Here, we combine ab initio calculations with the semiclassical ring-polymer instanton method to investigate tunneling processes on the ground electronic state of 6-hydroxy-2-formylfulvene (HFF), a prototypical neutral molecule supporting low-barrier hydrogen-bonding. The results emerging from a full-dimensional ab initio instanton analysis reveal that the tunneling path does not pass through the instantaneous transition-state geometry. Instead, the tunneling process involves a multidimensional reaction coordinate with concerted reorganization of the heavy-atom skeletal framework to substantially reduce the donor-acceptor distance and drive the ensuing intramolecular proton-transfer event. The predicted tunneling-induced splittings for HFF isotopologues are in good agreement with experimental findings, leading to percentage deviations of only 20-40%. Our full-dimensional results allow us to characterize vibrational contributions along the tunneling path, highlighting the intrinsically multidimensional nature of the attendant hydron-migration dynamics.

MDiGest: A Python package for describing allostery from molecular dynamics simulations.

Many biological processes are regulated by allosteric mechanisms that communicate with distant sites in the protein responsible for functionality. The binding of a small molecule at an allosteric site typically induces conformational changes that propagate through the protein along allosteric pathways regulating enzymatic activity. Elucidating those communication pathways from allosteric sites to orthosteric sites is, therefore, essential to gain insights into biochemical processes. Targeting the allosteric pathways by mutagenesis can allow the engineering of proteins with desired functions. Furthermore, binding small molecule modulators along the allosteric pathways is a viable approach to target reactions using allosteric inhibitors/activators with temporal and spatial selectivity. Methods based on network theory can elucidate protein communication networks through the analysis of pairwise correlations observed in molecular dynamics (MD) simulations using molecular descriptors that serve as proxies for allosteric information. Typically, single atomic descriptors such as α-carbon displacements are used as proxies for allosteric information. Therefore, allosteric networks are based on correlations revealed by that descriptor. Here, we introduce a Python software package that provides a comprehensive toolkit for studying allostery from MD simulations of biochemical systems. MDiGest offers the ability to describe protein dynamics by combining different approaches, such as correlations of atomic displacements or dihedral angles, as well as a novel approach based on the correlation of Kabsch-Sander electrostatic couplings. MDiGest allows for comparisons of networks and community structures that capture physical information relevant to allostery. Multiple complementary tools for studying essential dynamics include principal component analysis, root mean square fluctuation, as well as secondary structure-based analyses.

Matsubara dynamics approximation for generalized multi-time correlation functions.

We introduce a semi-classical approximation for calculating generalized multi-time correlation functions based on Matsubara dynamics, a classical dynamics approach that conserves the quantum Boltzmann distribution. This method is exact for the zero time and harmonic limits and reduces to classical dynamics when only one Matsubara mode is considered (i.e., the centroid). Generalized multi-time correlation functions can be expressed as canonical phase-space integrals, involving classically evolved observables coupled through Poisson brackets in a smooth Matsubara space. Numerical tests on a simple potential show that the Matsubara approximation exhibits better agreement with exact results than classical dynamics, providing a bridge between the purely quantum and classical descriptions of multi-time correlation functions. Despite the phase problem that prevents practical applications of Matsubara dynamics, the reported work provides a benchmark theory for the future development of quantum-Boltzmann-preserving semi-classical approximations for studies of chemical dynamics in condensed phase systems.