Molecular Insights into the Variability in Infection and Immune Evasion Capabilities of SARS-CoV-2 Variants: A Sequence and Structural Investigation of the RBD Domain.

As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continuously emerge, an increasing number of mutations are accumulating in the Spike protein receptor-binding domain (RBD) region. Through sequence analysis of various Variants of Concern (VOC), we identified that they predominantly fall within the ο lineage although recent variants introduce any novel mutations in the RBD. Molecular dynamics simulations were employed to compute the binding free energy of these variants with human Angiotensin-converting enzyme 2 (ACE2). Structurally, the binding interface of the ο RBD displays a strong positive charge, complementing the negatively charged binding interface of ACE2, resulting in a significant enhancement in the electrostatic potential energy for the ο variants. Although the increased potential energy is partially offset by the rise in polar solvation free energy, enhanced electrostatic potential contributes to the long-range recognition between the ο variant's RBD and ACE2. We also conducted simulations of glycosylated ACE2-RBD proteins. The newly emerged ο (JN.1) variant has added a glycosylation site at N-354@RBD, which significantly weakened its binding affinity with ACE2. Further, our interaction studies with three monoclonal antibodies across multiple SARS-CoV-2 strains revealed a diminished neutralization efficacy against the ο variants, primarily attributed to the electrostatic repulsion between the antibodies and RBD interface. Considering the characteristics of the ο variant and the trajectory of emerging strains, we propose that newly developed antibodies against SARS-CoV-2 RBD should have surfaces rich in negative potential and, postbinding, exhibit strong van der Waals interactions. These findings provide invaluable guidance for the formulation of future therapeutic strategies.

Inhibitory mechanism of n-MTAB AuNPs for α-synuclein aggregation.

OBJECTIVE: The aggregation of alpha-synuclein (α-syn) is closely related to the pathogenesis and dysfunction of Parkinson's disease. METHODS: To investigate the potential of nanoparticlemediated therapy, the interactive mechanism between α-syn and n-myristyltrimethylammonium bromide (MTAB) Gold nanoparticles (AuNPs) with different diameters was explored by molecular dynamics simulations. RESULTS: The results indicated that there was a directional interaction between α-syn and n-MTAB AuNPs, in which the driving force for the binding of the C-terminus in α-syn came from electrostatic interactions and the nonamyloid ß component (NAC) domain exhibited weak hydrophobic interactions as well as electrostatic interaction, thereby preventing α-syn aggregation. Energy statistics and analysis showed that for 5-MTAB AuNPs, acidic amino acids such as Glu and Asp played a very important role. CONCLUSIONS: This study not only demonstrated a theoretical foundation for the behavior of biomolecules directionally adsorbed on the surface of biofunctional nanoparticles but also indicated that 5-MTAB AuNPs may be a potential inhibitor against α-syn protein aggregation.

Discovery of inhibitors against SARS-CoV-2 main protease using fragment-based drug design.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiological agent of coronavirus disease 2019 (COVID-19), in which the main protease (Mpro) plays an important role in the virus's life cycle. In this work, two representative peptide inhibitors (11a and PF-07321332) were selected, and their interaction mechanisms of non-covalently bound with Mpro were firstly investigated by means of molecular dynamical simulation. Then, using the fragment-based drug design method, some fragments from the existing SARS-CoV and SARS-CoV-2 inhibitors were selected to replace the original P2 and P3 fragments, resulting in some new molecules. Among them, two molecules (O-74 and N-98) were confirmed by molecular docking and molecular dynamics simulation, and ADMET properties prediction was employed for further verification. The results shown that they presented excellent activity and physicochemical properties, and had the potential to be new inhibitors for SARS-CoV-2 main protease.

Biological effect of black phosphorus nanosheets on the interaction between SARS-CoV-2 S protein and ACE2.

The binding of the spike glycoprotein (S protein) in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to angiotensin-converting enzyme 2 (ACE2) is the main pathway that leads to serious coronavirus disease 2019 (COVID-19) infection. In the biomedical applications of various nanomaterials, black phosphorus nanosheets (BP) have been receiving increasing attention owing to their excellent characteristics. In this study, the biological effect of BP on the interaction between the S protein and ACE2 was investigated by molecular dynamics simulations. The results indicated that the ACE2 could be quickly and stably adsorbed on the BP surface by non-specific binding and retain its structural integrity. Compared with the case without BP, the interaction of the S protein bound to ACE2 adsorbed on the BP surface was greatly weakened, including hydrogen bonds, salt bridges, and van der Waals forces. This study not only reveals that BP could effectively obstruct the binding of S protein and ACE2, which may provide a potential and reasonable drug carrier to further enhance the curative effect of inhibitors against SARS-CoV-2 infection, but also presents a novel interference mechanism for protein-protein interactions caused by nanomaterials.

Rapid determination of trace homogeneous catalyst in chemical production.

As one of the important factors in chemical production, catalyst content directly affects the process of reaction and the quality of products. The quantitative analysis of trace catalyst in homogeneous reaction system is still faced with great challenges. In this work, a simple and effective approach to the rapid determination of trace homogeneous catalyst (THC) was proposed based on UV-vis spectrophotometry. Wavelet transform and Tchebichef curve moment methods were combined with gray wolf algorithm to extract the feature information from the original UV-vis spectra of samples. Then the partial least-squares model was established. The predictive correlation coefficient (Rp2) was 0.9842, and the limit of quantification was 0.07 ‰. The intra-day and inter-day precision were 3.97 % and 4.36 %, respectively. The spiked recoveries of three different concentrations in actual samples were between 97.6 and 101.9 %. The results indicated that the obtained model was satisfactory and could be used in practical measurement. Compared with the conventional modeling methods, the proposed approach was more accurate and reliable, which provided a feasible new pathway for enterprise product quality control.