Holographic Gaussian Boson Sampling with Matrix Product States on 3D cQED Processors.

We introduce quantum circuits for simulations of multimode state vectors on 3D circuit quantum electrodynamics (cQED) processors using matrix product state representations. The circuits are demonstrated as applied to simulations of molecular docking based on holographic Gaussian boson sampling (GBS), as illustrated for the binding of a thiol-containing aryl sulfonamide ligand to the tumor necrosis factor-α converting enzyme receptor. We show that cQED devices with a modest number of modes could be employed to simulate multimode systems by repurposing working modes through measurement and reinitialization. We anticipate that a wide range of GBS applications could be implemented on compact 3D cQED processors analogously using the holographic approach. Simulations on qubit-based quantum computers could be implemented analogously using circuits that represent continuous variables in terms of truncated expansions of Fock states.

Benchmarking various nonadiabatic semiclassical mapping dynamics methods with tensor-train thermo-field dynamics.

Accurate quantum dynamics simulations of nonadiabatic processes are important for studies of electron transfer, energy transfer, and photochemical reactions in complex systems. In this comparative study, we benchmark various approximate nonadiabatic dynamics methods with mapping variables against numerically exact calculations based on the tensor-train (TT) representation of high-dimensional arrays, including TT-KSL for zero-temperature dynamics and TT-thermofield dynamics for finite-temperature dynamics. The approximate nonadiabatic dynamics methods investigated include mixed quantum-classical Ehrenfest mean-field and fewest-switches surface hopping, linearized semiclassical mapping dynamics, symmetrized quasiclassical dynamics, the spin-mapping method, and extended classical mapping models. Different model systems were evaluated, including the spin-boson model for nonadiabatic dynamics in the condensed phase, the linear vibronic coupling model for electronic transition through conical intersections, the photoisomerization model of retinal, and Tully's one-dimensional scattering models. Our calculations show that the optimal choice of approximate dynamical method is system-specific, and the accuracy is sensitively dependent on the zero-point-energy parameter and the initial sampling strategy for the mapping variables.

Mutation-induced shift of the photosystem II active site reveals insight into conserved water channels.

Photosystem II (PSII) is the water-plastoquinone photo-oxidoreductase central to oxygenic photosynthesis. PSII has been extensively studied for its ability to catalyze light-driven water oxidation at a Mn4CaO5 cluster called the oxygen-evolving complex (OEC). Despite these efforts, the complete reaction mechanism for water oxidation by PSII is still heavily debated. Previous mutagenesis studies have investigated the roles of conserved amino acids, but these studies have lacked a direct structural basis that would allow for a more meaningful interpretation. Here, we report a 2.14-Å resolution cryo-EM structure of a PSII complex containing the substitution Asp170Glu on the D1 subunit. This mutation directly perturbs a bridging carboxylate ligand of the OEC, which alters the spectroscopic properties of the OEC without fully abolishing water oxidation. The structure reveals that the mutation shifts the position of the OEC within the active site without markedly distorting the Mn4CaO5 cluster metal-metal geometry, instead shifting the OEC as a rigid body. This shift disturbs the hydrogen-bonding network of structured waters near the OEC, causing disorder in the conserved water channels. This mutation-induced disorder appears consistent with previous FTIR spectroscopic data. We further show using quantum mechanics/molecular mechanics methods that the mutation-induced structural changes can affect the magnetic properties of the OEC by altering the axes of the Jahn-Teller distortion of the Mn(III) ion coordinated to D1-170. These results offer new perspectives on the conserved water channels, the rigid body property of the OEC, and the role of D1-Asp170 in the enzymatic water oxidation mechanism.

Water Ligands Regulate the Redox Leveling Mechanism of the Oxygen-Evolving Complex of the Photosystem II.

Understanding how water ligands regulate the conformational changes and functionality of the oxygen-evolving complex (OEC) in photosystem II (PSII) throughout the catalytic cycle of oxygen evolution remains a highly intriguing and unresolved challenge. In this study, we investigate the effect of water insertion (WI) on the redox state of the OEC by using the molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) hybrid methods. We find that water binding significantly reduces the free energy change for proton-coupled electron transfer (PCET) from Mn to YZ•, underscoring the important regulatory role of water binding, which is essential for enabling the OEC redox-leveling mechanism along the catalytic cycle. We propose a water binding mechanism in which WI is thermodynamically favored by the closed-cubane form of the OEC, with water delivery mediated by Ca2+ ligand exchange. Isomerization from the closed- to open-cubane conformation at three post-WI states highlights the importance of the location of the MnIII center in the OEC and the orientation of its Jahn-Teller axis to conformational changes of the OEC, which might be critical for the formation of the O-O bond. These findings reveal a complex interplay between conformational changes in the OEC and the ligand environment during the activation of the OEC by YZ•. Analogous regulatory effects due to water ligand binding are expected to be important for a wide range of catalysts activated by redox state transitions in aqueous environments.

Mechanism of Action of KL-50, a Candidate Imidazotetrazine for the Treatment of Drug-Resistant Brain Cancers.

Aberrant DNA repair is a hallmark of cancer, and many tumors display reduced DNA repair capacities that sensitize them to genotoxins. Here, we demonstrate that the differential DNA repair capacities of healthy and transformed tissue may be exploited to obtain highly selective chemotherapies. We show that the novel N3-(2-fluoroethyl)imidazotetrazine "KL-50" is a selective toxin toward tumors that lack the DNA repair protein O6-methylguanine-DNA-methyltransferase (MGMT), which reverses the formation of O6-alkylguanine lesions. We establish that KL-50 generates DNA interstrand cross-links (ICLs) by a multistep process comprising DNA alkylation to generate an O6-(2-fluoroethyl)guanine (O6FEtG) lesion, slow unimolecular displacement of fluoride to form an N1,O6-ethanoguanine (N1,O6EtG) intermediate, and ring-opening by the adjacent cytidine. The slow rate of N1,O6EtG formation allows healthy cells expressing MGMT to reverse the initial O6FEtG lesion before it evolves to N1,O6EtG, thereby suppressing the formation of toxic DNA-MGMT cross-links and reducing the amount of DNA ICLs generated in healthy cells. In contrast, O6-(2-chloroethyl)guanine lesions produced by agents such as lomustine and the N3-(2-chloroethyl)imidazotetrazine mitozolomide rapidly evolve to N1,O6EtG, resulting in the formation of DNA-MGMT cross-links and DNA ICLs in healthy tissue. These studies suggest that careful consideration of the rates of chemical DNA modification and biochemical DNA repair may lead to the identification of other tumor-specific genotoxic agents.

Quantum-to-Classical Neural Network Transfer Learning Applied to Drug Toxicity Prediction.

Toxicity is a roadblock that prevents an inordinate number of drugs from being used in potentially life-saving applications. Deep learning provides a promising solution to finding ideal drug candidates; however, the vastness of chemical space coupled with the underlying O(n3) matrix multiplication means these efforts quickly become computationally demanding. To remedy this, we present a hybrid quantum-classical neural network for predicting drug toxicity utilizing a quantum circuit design that mimics classical neural behavior by explicitly calculating matrix products with complexity O(n2). Leveraging the Hadamard test for efficient inner product estimation rather than the conventionally used swap test, we reduce the number of qubits by half and remove the need for quantum phase estimation. Directly computing matrix products quantum mechanically allows for learnable weights to be transferred from a quantum to a classical device for further training. We apply our framework to the Tox21 data set and show that it achieves commensurate predictive accuracy to the model's fully classical O(n3) analogue. Additionally, we demonstrate that the model continues to learn, without disruption, once transferred to a fully classical architecture. We believe that combining the quantum advantage of reduced complexity and the classical advantage of noise-free calculation will pave the way for more scalable machine learning models.

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.

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.

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.

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.