Origins of enhanced oxygen reduction activity of transition metal nitrides.

Transition metal nitride (TMN-) based materials have recently emerged as promising non-precious-metal-containing electrocatalysts for the oxygen reduction reaction (ORR) in alkaline media. However, the lack of fundamental understanding of the oxide surface has limited insights into structure-(re)activity relationships and rational catalyst design. Here we demonstrate how a well-defined TMN can dictate/control the as-formed oxide surface and the resulting ORR electrocatalytic activity. Structural characterization of MnN nanocuboids revealed that an electrocatalytically active Mn3O4 shell grew epitaxially on the MnN core, with an expansive strain along the [010] direction to the surface Mn3O4. The strained Mn3O4 shell on the MnN core exhibited an intrinsic activity that was over 300% higher than that of pure Mn3O4. A combined electrochemical and computational investigation indicated/suggested that the enhancement probably originates from a more hydroxylated oxide surface resulting from the expansive strain. This work establishes a clear and definitive atomistic picture of the nitride/oxide interface and provides a comprehensive mechanistic understanding of the structure-reactivity relationship in TMNs, critical for other catalytic interfaces for different electrochemical processes.

Development of a machine learning-based target-specific scoring function for structure-based binding affinity prediction for human dihydroorotate dehydrogenase inhibitors.

Human dihydroorotate dehydrogenase (hDHODH) is a flavin mononucleotide-dependent enzyme that can limit de novo pyrimidine synthesis, making it a therapeutic target for diseases such as autoimmune disorders and cancer. In this study, using the docking structures of complexes generated by AutoDock Vina, we integrate interaction features and ligand features, and employ support vector regression to develop a target-specific scoring function for hDHODH (TSSF-hDHODH). The Pearson correlation coefficient values of TSSF-hDHODH in the cross-validation and external validation are 0.86 and 0.74, respectively, both of which are far superior to those of classic scoring function AutoDock Vina and random forest (RF) based generic scoring function RF-Score. TSSF-hDHODH is further used for the virtual screening of potential inhibitors in the FDA-Approved & Pharmacopeia Drug Library. In conjunction with the results from molecular dynamics simulations, crizotinib is identified as a candidate for subsequent structural optimization. This study can be useful for the discovery of hDHODH inhibitors and the development of scoring functions for additional targets.

Electro-inductive Effects and Molecular Polarizability for Vibrational Probes on Electrode Surfaces.

A microscopic understanding of electric fields and molecular polarization at interfaces will aid in the design of electrocatalytic systems. Herein, variants of 4-mercaptobenzonitrile are designed to test different schemes for breaking the continuous conjugation between a gold electrode surface and a nitrile group. Periodic density functional theory calculations predict applied potential dependencies of the CN vibrational frequencies similar to those observed experimentally. The CN frequency response decreased more when the conjugation was broken between the benzene ring and the nitrile group than between the electrode and the benzene ring, highlighting molecular polarizability effects. The systems with continuous or broken conjugation are dominated by electro-inductive effects or through-space electrostatic effects, respectively. Analysis of the fractional charge transfer between the electrode and the molecule as well as the occupancy of the CN antibonding orbital provides further insights. Balancing the effects of molecular polarizability, electro-induction, and through-space electrostatics has broad implications for electrocatalyst design.

Construction of highly active and stable recombinant nattokinase by engineered bacteria and computational design.

Nattokinase (NK) is an enzyme that has been recognized as a new potential thrombolytic drug due to its strong thrombolytic activity. However, it is difficult to maintain the enzyme activity of NK during high temperature environment of industrial production. In this study, we constructed six NK mutants with potential for higher thermostability using a rational protein engineering strategy integrating free energy-based methods and molecular dynamics (MD) simulation. Then, wild-type NK and NK mutants were expressed in Escherichia coli (E. coli), and their thermostability and thrombolytic activity were tested. The results showed that, compared with wild-type NK, the mutants Y256P, Q206L and E156F all had improved thermostability. The optimal mutant Y256P showed a higher melting temperature (Tm) of 77.4 °C, an increase of 4 °C in maximum heat-resistant temperature and an increase of 51.8 % in activity at 37 °C compared with wild-type NK. Moreover, we also explored the mechanism of the increased thermostability of these mutants by analysing the MD trajectories under different simulation temperatures.

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.

Electro-inductive Effect Dominates Vibrational Frequency Shifts of Conjugated Probes on Gold Electrodes.

Interfacial electric fields play a critical role in electrocatalysis and are often characterized by using vibrational probes attached to an electrode surface. Understanding the physical principles dictating the impact of the applied electrode potential on the vibrational probe frequency is important. Herein, a comparative study is performed for two molecular probes attached to a gold electrode. Both probes contain a nitrile (CN) group, but 4-mercaptobenzonitrile (4-MBN) exhibits continuous conjugation from the electrode through the nitrile group, whereas this conjugation is interrupted for 2-(4-mercaptophenyl)acetonitrile (4-MPCN). Periodic density functional theory calculations predict that the CN vibrational frequency shift of the 4-MBN system is dominated by induction, which is a through-bond polarization effect, leading to a strong potential dependence that does not depend significantly on the orientation of the CN bond relative to the surface. In contrast, the CN vibrational frequency shift of the 4-MPCN system is influenced less by induction and more by through-space electric field effects, leading to a weaker potential dependence and a greater orientation dependence. These theoretical predictions were confirmed by surface-enhanced Raman spectroscopy experiments. Balancing through-bond and through-space electrostatic effects may assist in the fundamental understanding and design of electrocatalytic systems.

Direct Vibrational Stark Shift Probe of Quasi-Fermi Level Alignment in Metal Nanoparticle Catalyst-Based Metal-Insulator-Semiconductor Junction Photoelectrodes.

Photoelectrodes consisting of metal-insulator-semiconductor (MIS) junctions are a promising candidate architecture for water splitting and for the CO2 reduction reaction (CO2RR). The photovoltage is an essential indicator of the driving force that a photoelectrode can provide for surface catalytic reactions. However, for MIS photoelectrodes that contain metal nanoparticles, direct photovoltage measurements at the metal sites under operational conditions remain challenging. Herein, we report a new in situ spectroscopic approach to probe the quasi-Fermi level of metal catalyst sites in heterogeneous MIS photoelectrodes via surface-enhanced Raman spectroscopy. Using a CO2RR photocathode, nanoporous p-type Si modified with Ag nanoparticles, as a prototype, we demonstrate a selective probe of the photovoltage of ∼0.59 V generated at the Si/SiOx/Ag junctions. Because it can directly probe the photovoltage of MIS heterogeneous junctions, this vibrational Stark probing approach paves the way for the thermodynamic evaluation of MIS photoelectrodes with varied architectural designs.

Unconventional interfacial water structure of highly concentrated aqueous electrolytes at negative electrode polarizations.

Water-in-salt electrolytes are an appealing option for future electrochemical energy storage devices due to their safety and low toxicity. However, the physicochemical interactions occurring at the interface between the electrode and the water-in-salt electrolyte are not yet fully understood. Here, via in situ Raman spectroscopy and molecular dynamics simulations, we investigate the electrical double-layer structure occurring at the interface between a water-in-salt electrolyte and an Au(111) electrode. We demonstrate that most interfacial water molecules are bound with lithium ions and have zero, one, or two hydrogen bonds to feature three hydroxyl stretching bands. Moreover, the accumulation of lithium ions on the electrode surface at large negative polarizations reduces the interfacial field to induce an unusual "hydrogen-up" structure of interfacial water and blue shift of the hydroxyl stretching frequencies. These physicochemical behaviours are quantitatively different from aqueous electrolyte solutions with lower concentrations. This atomistic understanding of the double-layer structure provides key insights for designing future aqueous electrolytes for electrochemical energy storage devices.

Sub-Nanometer Mapping of the Interfacial Electric Field Profile Using a Vibrational Stark Shift Ruler.

The characterization of electrical double layers is important since the interfacial electric field and electrolyte environment directly affect the reaction mechanisms and catalytic rates of electrochemical processes. In this work, we introduce a spectroscopic method based on a Stark shift ruler that enables mapping the electric field strength across the electric double layer of electrode/electrolyte interfaces. We use the tungsten-pentacarbonyl(1,4-phenelenediisocyanide) complex attached to the gold surface as a molecular ruler. The carbonyl (CO) and isocyanide (NC) groups of the self-assembled monolayer (SAM) provide multiple vibrational reporters situated at different distances from the electrode. Measurements of Stark shifts under operando electrochemical conditions and direct comparisons to density functional theory (DFT) simulations reveal distance-dependent electric field strength from the electrode surface. This electric field profile can be described by the Gouy-Chapman-Stern model with Stern layer thickness of ∼4.5 Å, indicating substantial solvent and electrolyte penetration within the SAM. Significant electro-induction effect is observed on the W center that is ∼1.2 nm away from the surface despite rapid decay of the electric field (∼90%) within 1 nm. The applied methodology and reported findings should be particularly valuable for the characterization of a wide range of microenvironments surrounding molecular electrocatalysts at electrode interfaces and the positioning of electrocatalysts at specific distances from the electrode surface for optimal functionality.

De Novo design of potential inhibitors against SARS-CoV-2 Mpro.

The impact of the ravages of COVID-19 on people's lives is obvious, and the development of novel potential inhibitors against SARS-CoV-2 main protease (Mpro), which has been validated as a potential target for drug design, is urgently needed. This study developed a model named MproI-GEN, which can be used for the de novo design of potential Mpro inhibitors (MproIs) based on deep learning. The model was mainly composed of long-short term memory modules, and the last layer was re-trained with transfer learning. The validity (0.9248), novelty (0.9668), and uniqueness (0.0652) of the designed potential MproI library (PMproIL) were evaluated, and the results showed that MproI-GEN could be used to design structurally novel and reasonable molecules. Additionally, PMproIL was filtered based on machine learning models and molecular docking. After filtering, the potential MproIs were verified with molecular dynamics simulations to evaluate the binding stability levels of these MproIs and SARS-CoV-2 Mpro, thereby illustrating the inhibitory effects of the potential MproIs against Mpro. Two potential MproIs were proposed in this study. This study provides not only new possibilities for the development of COVID-19 drugs but also a complete pipeline for the discovery of novel lead compounds.

TSSF-hERG: A machine-learning-based hERG potassium channel-specific scoring function for chemical cardiotoxicity prediction.

The human ether-à-go-go-related gene (hERG) encodes the Kv11.1 voltage-gated potassium ion (K+) channel that conducts the rapidly activating delayed rectifier current (IKr) in cardiomyocytes to regulate the repolarization process. Some drugs, as blockers of hERG potassium channels, cannot be marketed due to prolonged QT intervals, as well known as cardiotoxicity. Predetermining the binding affinity values between drugs and hERG through in silico methods can greatly reduce the time and cost required for experimental verification. In this study, we collected 9,215 compounds with AutoDock Vina's docking structures as training set, and collected compounds from four references as test sets. A series of models for predicting the binding affinities of hERG blockers were built based on five machine learning algorithms and combinations of interaction features and ligand features. The model built by support vector regression (SVR) using the combination of all features achieved the best performance on both tenfold cross-validation and external verification, which was selected and named as TSSF-hERG (target-specific scoring function for hERG). TSSF-hERG is more accurate than the classic scoring function of AutoDock Vina and the machine-learning-based generic scoring function RF-Score, with a Pearson's correlation coefficient (Rp) of 0.765, a Spearman's rank correlation coefficient (Rs) of 0.757, a root-mean-square error (RMSE) of 0.585 in a tenfold cross-validation study. All results demonstrated that TSSF-hERG would be useful for improving the power of binding affinity prediction between hERG and compounds, which can be further used for prediction or virtual screening of the hERG-related cardiotoxicity of drug candidates.

MutagenPred-GCNNs: A Graph Convolutional Neural Network-Based Classification Model for Mutagenicity Prediction with Data-Driven Molecular Fingerprints.

An important task in the early stage of drug discovery is the identification of mutagenic compounds. Mutagenicity prediction models that can interpret relationships between toxicological endpoints and compound structures are especially favorable. In this research, we used an advanced graph convolutional neural network (GCNN) architecture to identify the molecular representation and develop predictive models based on these representations. The predictive model based on features extracted by GCNNs can not only predict the mutagenicity of compounds but also identify the structure alerts in compounds. In fivefold cross-validation and external validation, the highest area under the curve was 0.8782 and 0.8382, respectively; the highest accuracy (Q) was 80.98% and 76.63%, respectively; the highest sensitivity was 83.27% and 78.92%, respectively; and the highest specificity was 78.83% and 76.32%, respectively. Additionally, our model also identified some toxicophores, such as aromatic nitro, three-membered heterocycles, quinones, and nitrogen and sulfur mustard. These results indicate that GCNNs could learn the features of mutagens effectively. In summary, we developed a mutagenicity classification model with high predictive performance and interpretability based on a data-driven molecular representation trained through GCNNs.

On the Coupling of Electron Transfer to Proton Transfer at Electrified Interfaces.

Many electrochemical processes are governed by the transfer of protons to the surface, which can be coupled with electron transfer; this electron transfer is in general non-integer and unknown a priori, but is required to hold the potential constant. In this study, we employ a combination of surface spectroscopic techniques and grand-canonical electronic-structure calculations in order to rigorously understand the thermodynamics of this process. Specifically, we explore the protonation/deprotonation of 4-mercaptobenzoic acid as a function of the applied potential. Using grand-canonical electronic-structure calculations, we directly infer the coupled electron transfer, which we find to be on the order of 0.1 electron per proton; experimentally, we also access this quantity via the potential-dependence of the pKa. We show a striking agreement between the potential-dependence of the measured pKa and that calculated with electronic-structure calculations. We further employ a simple electrostatics-based model to show that this slope can equivalently be interpreted to provide information on the degree of coupled electron transfer or the potential change at the point of the charged species.