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.

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.

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.

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.

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.

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 CO reduction reaction (CORR). 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.

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.

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.

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.

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 (I) 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.

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.

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 , 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.