Engineering the biomimetic cofactors of NMNH for cytochrome P450 BM3 based on binding conformation refinement.

Cytochrome P450 BM3 (BM3) is an important oxidoreductase that is widely used in drug synthesis, chemical synthesis, and other industries. However, as BM3 unquestionably increases costs by consuming a natural cofactor that unstably provides electrons, an alternative biomimetic cofactor with simpler structures represented by nicotinamide mononucleotide (NMNH) has been utilized. Currently, few reports exist on artificially modified BM3 enzymes using NMNH, especially regarding theoretical simulation and calculation.

Accurate Prediction of Aqueous Free Solvation Energies Using 3D Atomic Feature-Based Graph Neural Network with Transfer Learning.

Graph neural network (GNN)-based deep learning (DL) models have been widely implemented to predict the experimental aqueous solvation free energy, while its prediction accuracy has reached a plateau partly due to the scarcity of available experimental data. In order to tackle this challenge, we first build a large and diverse calculated data set Frag20-Aqsol-100K of aqueous solvation free energy with reasonable computational cost and accuracy via electronic structure calculations with continuum solvent models. Then, we develop a novel 3D atomic feature-based GNN model with the principal neighborhood aggregation (PNAConv) and demonstrate that 3D atomic features obtained from molecular mechanics-optimized geometries can significantly improve the learning power of GNN models in predicting calculated solvation free energies.

Conformations and binding pockets of HRas and its guanine nucleotide exchange factors complexes in the guanosine triphosphate exchange process.

The human Son of Sevenless (SOS) activates the signal-transduction protein Ras by forming the complex SOS·Ras and accelerating the guanosine triphosphate (GTP) exchange in Ras. Inhibition of SOS·Ras could regulate the function of Ras in cells and has emerged as an effective strategy for battling Ras related cancers. A key factor to the success of this approach is to understand the conformational change of Ras during the GTP exchange process.

Identification of functional substates of KRas during GTP hydrolysis with enhanced sampling simulations.

As the hub of major signaling pathways, Ras proteins are implicated in 19% of tumor-caused cancers due to perturbations in their conformational and/or catalytic properties. Despite numerous studies, the functions of the conformational substates for the most important isoform, KRas, remain elusive. In this work, we perform an extensive simulation analysis on the conformational landscape of KRas in its various chemical states during the GTP hydrolysis cycle: the reactant state KRasGTP·Mg, the intermediate state KRasGDP·Pi·Mg and the product state KRasGDP·Mg.

Unified Deep Learning Model for Multitask Reaction Predictions with Explanation.

There is significant interest and importance to develop robust machine learning models to assist organic chemistry synthesis. Typically, task-specific machine learning models for distinct reaction prediction tasks have been developed. In this work, we develop a unified deep learning model, T5Chem, for a variety of chemical reaction predictions tasks by adapting the "Text-to-Text Transfer Transformer" (T5) framework in natural language processing (NLP).

Discovery of novel inhibitors of CDK2 using docking and physics-based binding free energy calculation.

Cyclin-dependent kinase (CDK) is a serine/threonine protein kinase family that cooperates with cyclin and plays an important role in the regulation of cell cycle. Cyclin-dependent kinase 2 is an important member of the CDK family and holds great promise as an anti-cancer drug target. In this study, we used molecular docking and physics-based binding free energy calculation method AS-IE that explicitly calculated protein-ligand binding entropy to discover novel inhibitors of CDK2.

Cholesterylation of Smoothened is a calcium-accelerated autoreaction involving an intramolecular ester intermediate.

Hedgehog (Hh) is a morphogen that binds to its receptor Patched 1 and activates Smoothened (SMO), thereby governing embryonic development and postnatal tissue homeostasis. Cholesterol can bind and covalently conjugate to the luminal cysteine-rich domain (CRD) of human SMO at the D95 residue (D99 in mouse). The reaction mechanism and biological function of SMO cholesterylation have not been elucidated.

Correlating Interfacial Charge Transfer Rates with Interfacial Molecular Structure in the Tetraphenyldibenzoperiflanthene/C Organic Photovoltaic System.

Organic photovoltaics (OPV) is an emerging solar cell technology that offers vast advantages such as low-cost manufacturing, transparency, and solution processability. However, because the performance of OPV devices is still disappointing compared to their inorganic counterparts, better understanding of how controlling the molecular-level morphology can impact performance is needed. To this end, one has to overcome significant challenges that stem from the complexity and heterogeneity of the underlying electronic structure and molecular morphology.

Evolution of microscopic heterogeneity and dynamics in choline chloride-based deep eutectic solvents.

Deep eutectic solvents (DESs) are an emerging class of non-aqueous solvents that are potentially scalable, easy to prepare and functionalize for many applications ranging from biomass processing to energy storage technologies. Predictive understanding of the fundamental correlations between local structure and macroscopic properties is needed to exploit the large design space and tunability of DESs for specific applications. Here, we employ a range of computational and experimental techniques that span length-scales from molecular to macroscopic and timescales from picoseconds to seconds to study the evolution of structure and dynamics in model DESs, namely Glyceline and Ethaline, starting from the parent compounds.

HobPre: accurate prediction of human oral bioavailability for small molecules.

Human oral bioavailability (HOB) is a key factor in determining the fate of new drugs in clinical trials. HOB is conventionally measured using expensive and time-consuming experimental tests. The use of computational models to evaluate HOB before the synthesis of new drugs will be beneficial to the drug development process.

Dynamic Coupling of Tyrosine 185 with the Bacteriorhodopsin Photocycle, as Revealed by Chemical Shifts, Assisted AF-QM/MM Calculations and Molecular Dynamic Simulations.

Aromatic residues are highly conserved in microbial photoreceptors and play crucial roles in the dynamic regulation of receptor functions. However, little is known about the dynamic mechanism of the functional role of those highly conserved aromatic residues during the receptor photocycle. Tyrosine 185 (Y185) is a highly conserved aromatic residue within the retinal binding pocket of bacteriorhodopsin (bR).

Near Infrared Light-Driven Photothermal Effect on Homochiral Au/TiO Nanotube Arrays for Enantioselective Desorption.

Chiral enantiomers have different effects on biological processes. Enantiomer separation is significant and necessary. Herein, a photothermal (PT) effect-derived enantioselective desorption strategy based on homochiral Au/TiO nanotubes (NTs) is developed.

Investigating mechanism of sweetness intensity differences through dynamic analysis of sweetener-T1R2-membrane systems.

Knowing the mechanism of action of sweet taste receptors is important for the design of new, healthy sweeteners. However, little is known about the structures and recognition mechanisms of these receptors. 28 sweeteners were assessed by molecular docking, and 8 typical sweeteners were chosen to construct sweetener-T1R2-membrane systems to analyze interactions between receptor and sweeteners.

Affordable Path Integral for Thermodynamic Properties via Molecular Dynamics Simulations Using Semiempirical Reference Potential.

Path integral molecular dynamics (PIMD) is becoming a routinely applied method for incorporating the nuclear quantum effect in computer simulations. However, direct PIMD simulations at an level of theory are formidably expensive. Using the protonated 1,8-bis(dimethylamino)naphthalene molecule as an example, we show in this work that the computational expense for the intramolecular proton transfer between the two nitrogen atoms can be remarkably reduced by implementing the idea of reference-potential methods.

Computational Insights into the Binding Mechanism of OxyS sRNA with Chaperone Protein Hfq.

Under the oxidative stress condition, the small RNA (sRNA) OxyS that acts as essential post-transcriptional regulators of gene expression is produced and plays a regulatory function with the assistance of the RNA chaperone Hfq protein. Interestingly, experimental studies found that the N48A mutation of Hfq protein could enhance the binding affinity with OxyS while resulting in the defection of gene regulation. However, how the Hfq protein interacts with sRNA OxyS and the origin of the stronger affinity of N48A mutation are both unclear.

Charge-Transfer Landscape Manifesting the Structure-Rate Relationship in the Condensed Phase Machine Learning.

In this work, we develop a machine learning (ML) strategy to map the molecular structure to condensed phase charge-transfer (CT) properties including CT rate constants, energy levels, electronic couplings, energy gaps, reorganization energies, and reaction free energies which are called CT fingerprints. The CT fingerprints of selected landmark structures covering the conformation space of an organic photovoltaic molecule dissolved in an explicit solvent are computed and used to train ML models using kernel ridge regression. The ML models show high predictive power with > 0.

Room-temperature dynamic nuclear polarization enhanced NMR spectroscopy of small biological molecules in water.

Nuclear magnetic resonance (NMR) spectroscopy is a powerful and popular technique for probing the molecular structures, dynamics and chemical properties. However the conventional NMR spectroscopy is bottlenecked by its low sensitivity. Dynamic nuclear polarization (DNP) boosts NMR sensitivity by orders of magnitude and resolves this limitation.

Targeting mechanism for SARS-CoV-2 : interaction and key groups of TMPRSS2 toward four potential drugs.

The global dissemination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has seriously endangered human health. The number of confirmed cases is still increasing; however, treatment options are limited. Transmembrane protease serine 2 (TMPRSS2), as a key protease that primes the binding of SARS-CoV-2 spike protein and angiotensin-converting enzyme 2 (ACE2), has become an attractive target and received widespread attention.

Automated Construction of Neural Network Potential Energy Surface: The Enhanced Self-Organizing Incremental Neural Network Deep Potential Method.

In recent years, the use of deep learning (neural network) potential energy surface (NNPES) in molecular dynamics simulation has experienced explosive growth as it can be as accurate as quantum chemistry methods while being as efficient as classical mechanic methods. However, the development of NNPES is highly nontrivial. In particular, it has been troubling to construct a dataset that is as small as possible yet can cover the target chemical space.

Structural Reconstruction of Optically Invisible State in a Single Molecule via Scanning Tunneling Microscope.

Molecular dark states, participating in various energy- and electron-transfer processes, are typically beyond direct optical-spectroscopic measurements because of the forbidden transition dictated by the selection rule. In this work, we demonstrate a direct profile of the dark-state transition density of a single molecule on the subnanometer scale by using a scanning tunneling microscope. Our method allows one to resolve the four-lobe configuration in a 1 nm region for the example molecule.

A fixed multi-site interaction charge model for an accurate prediction of the QM/MM interactions.

A fixed multi-site interaction charge (FMIC) model was proposed for the accurate prediction of intermolecular electrostatic interactions based on the quantum mechanical linear response of a molecule to an external electric field. In such a model, some additional off-center interaction sites were added for capturing multipole interactions for a given molecule. By multivariate least-square fitting analysis of the calculated QM/MM interactions of a given molecule with the electrostatic environment and the electrostatic potentials of the environment at the pre-defined distributed interaction sites, the FMIC of the molecule was obtained.

Insights into small molecule inhibitor bindings to PD-L1 with residue-specific binding free energy calculation.

Targeting the immunological checkpoint PD-1/PD-L1 with antibodies has shown opportunities to improve cancer treatment in recent years. However, antibody therapy is a double-edged sword with high cost, low patient tolerance, lack of oral bioavailability, and a reaction to most solid tumors that prevents the adoption of antibodies. Advancement of small-molecule PD-1/PD-L1 inhibitors that could overwhelm these drawbacks is sluggish because of the poor pharmacodynamic properties and shallow pocket of the PD-1/PD-L1 binding interface.

Characterizing and Contrasting Structural Proton Transport Mechanisms in Azole Hydrogen Bond Networks Using Molecular Dynamics.

Imidazole and 1,2,3-triazole are promising hydrogen-bonded heterocycles that conduct protons via a structural mechanism and whose derivatives are present in systems ranging from biological proton channels to proton exchange membrane fuel cells. Here, we leverage multiple time-stepping to perform molecular dynamics of imidazole and 1,2,3-triazole at the nanosecond time scale. We show that despite the close structural similarities of these compounds, their proton diffusion constants vary by over an order of magnitude.