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.

A simple approach to the prediction of soil sorption of organophosphorus pesticides.

Organophosphorus pesticides (OP) affect the crops and environments, and the reliable approach to the prediction of soil sorption of pesticides is required. In this respect, we proposed a simple Chemometrics approach, in which the Tchebichef image moment (TM) method was used to extract useful information from the greyscale images of molecular structures and the quantitative model was established by stepwise regression to predict the soil sorption of OPs. Different squared correlation coefficients including the leave-one-out cross-validation (LOO-CV) (Q2) that concerns the training set and the (R2test) which concerns the external independent test set are more than 0.96. This reflects that the established model has considerably high accuracy and reliability. Compared with the literature on the strategies of quantitative structure-property relationship (QSPR), the proposed method is more suitable, in which the established model shows a high predictive ability. Our study provides another effective approach to predict the soil sorption of OPs and also extends the innovative pathway of QSPR modelling.

A simple approach to the determination of three curcuminoids with similar chemical structures in different substrates.

The determination of curcuminoids in mixtures is more difficult due to their similar chemical structures as well as serious interferences, thus the complex pretreatments of samples and the optimization of experimental conditions are often required. Here, owing to the mathematical separation of chemical signals by Tchebichef image moments, a simple and effective approach to the simultaneous quantitative analysis was proposed, and applied to the determination of the three curcuminoids in turmeric and curry based on their raw fluorescence 3D spectra. For the established linear models, the leave-one-out correlation coefficients (R loo-cv) were more than 0.9816 within the linear ranges, and the predictive correlation coefficients (R p) for the external independent samples were more than 0.9897. The intra- and inter-day precision (less than 6.82%, RSD), average spiked recovery (89.9% ~ 100.8%), LOD (less than 0.07 µg/mL) and LOQ (less than 0.23 µg/mL) suggest that the proposed approach is accurate and reliable. Compared with N-PLS and MCR-ALS methods, our method can obtain more satisfactory results. This study provides a convenient pathway for the rapid analysis of multi-target components with similar chemical structures in mixture of different substrates.

Selective inhibition mechanism of nitroxoline to the BET family: Insight from molecular simulations.

Although the proteins in bromodomain and extra-terminal domain (BET) family are promising therapy drug targets for numerous human diseases, the binding effectiveness is interfered by the competition from non-BET protein BRD9. In this study, molecular docking, molecular dynamics simulations, binding free energy calculations and per-residue energy decomposition methods were employed to clarify the selective inhibition mechanism of nitroxoline. The results showed that the different cavity volume of effective embedding inhibitor and the changes in conserved residues were associated with the significant higher selectivity of inhibitor nitroxoline for BET family than non-BET protein (BRD9). In addition, the non-polar interactions occurred in Phe83, Val87 at ZA loop, and the polar interaction appeared in Met132, Asn135 at BC loop. Therefore, when designing a new inhibitor, it could better improve the inhibitor activity by introducing the heteroatom conjugated pyridine-like moiety and the strong electron-withdrawing nitro-like moiety. Overall, this study not only clarified the molecular mechanism of the selective inhibition of nitroxoline, but also provided insight into designing more effective BET inhibitors in next step.

Chemometrics-assisted simultaneous voltammetric determination of multiple neurotransmitters in human serum.

An electrochemical method combining chemometrics was developed for simultaneous quantification of multiple neurotransmitters including Dopamine (DA), Epinephrine (EP), Norepinephrine (NE) and serotonin (5-hydroxytryptamine, 5-HT) in human blood serum. A reduced graphene oxide modified glassy carbon electrode (RGO/GCE) was prepared via electrodeposition method. Differential pulse voltammetry (DPV) measurement of the four neurotransmitters showed that the voltammetric signals of the four targets overlapped significantly. To facilitate the simultaneous determination of the neurotransmitters, a chemometric tool of Tchebichef curve moment (TcM) method was proposed. The TcMs calculated from the voltammograms were used to establish the quantitative models by stepwise regression. The intra-day and inter-day precisions of the proposed method were less than 3.5% and 8.1%, respectively, and the recoveries were from 87.4% to 124%. The limit of detection (LOD) for DA, EP, NE and 5-HT were 74 nM, 104 nM, 84 nM and 97 nM, respectively. The above results indicated that the proposed approach is simple and reliable for the simultaneous determination of multiple neurotransmitters in human serum.

Prediction of the inhibitory concentrations of chloroquine derivatives using deep neural networks models.

In recent years, deep neural networks have begun to receive much attention, which has obvious advantages in feature extraction and modeling. However, in the using of deep neural networks for the QSAR modeling process, the selection of various parameters (number of neurons, hidden layers, transfer functions, data set partitioning, number of iterations, etc.) becomes difficult. Thus, we proposed a new and easy method for optimizing the model and selecting Deep Neural Networks (DNN) parameters through uniform design ideas and orthogonal design methods. By using this approach, 222 chloroquine (CQ) derivatives with half maximal inhibitory concentration values reported in different kinds of literature were selected to establish DNN models and a total number of 128,000 DNN models were built to determine the optimized parameters for selecting the better models. Comparing with linear and Artificial Neural Network (ANN) models, we found that DNN models showed better performance.Communicated by Ramaswamy H. Sarma.

An effective approach to the early diagnosis of colorectal cancer based on three-dimensional fluorescence spectra of human blood plasma.

Colorectal cancer (CRC) is a common malignancy in the gastrointestinal tract, and its screening rates remain relatively low in the general population due to the lack of specific symptoms and effective methods. It is still in urgent need to develop rapid and reliable approach to the early diagnosis of CRC. Herein, based on the three-dimensional (3D) fluorescence spectra of human blood plasma, a combination strategy of Tchebichef image moments coupled with partial least squares-discriminate analysis (TM-PLS-DA) was proposed for the detection of CRC from three classes (CRC samples, adenomas samples and non-malignant findings). The established TM-PLS-DA classification model provided an 84 % correct classification for CRC prediction. Venetian blinds 10-fold cross validation was carried out. The error rates both in cross validation and test sets were less than 0.16. Sensitivity and specificity for CRC prediction were 0.95 and 0.88, respectively. At the same time, the diagnostic capacity of the proposed method was tested by receiver operating characteristics (ROC) analysis with area under the curve (AUC) of 0.94 for CRC diagnosis. These results demonstrate that the proposed TM-PLS-DA method based on the 3D fluorescence spectra of blood plasma has great advantage for the accurate CRC detection, which will provide a potential alternative approach for cancer diagnostics.

Novel Approach to the Analysis of Chemical Third-Order Data.

For the more complex samples, chemical higher-order data can be collected from various information sources, which become the necessary foundation of accurate analysis. In this article, the Tchebichef cubic moment (TCM) was developed for the analysis of chemical third-order data for the first time. Then, the proposed TCM approach was applied to the fluorescence excitation-emission time data for the analysis of adrenaline and noradrenaline in urinary samples (Data I) and the data fusion of the excitation-emission matrix (EEM), NMR, and liquid chromatography-mass spectrometry (LC-MS) spectra for the determination of the five target components (Data II). For Data I, all of the cross-validation correlation coefficients (Rcv2) of the obtained linear models on the calibration set were more than 0.9937 and the prediction root-mean-square errors (RMSEp) of the external independent test samples were less than 0.0250 µM. For Data II, all of the Rcv2 were higher than 0.9846 and RMSEp were less than 0.2267 µM. Compared with several conventional methods, the proposed method was more convenient and accurate. This study provides another effective approach to the analysis of complex samples based on their chemical third-order data.

Study of the controversial resveratrol that interact with the endogenous glutathione thiyl radical in cancer cells.

Found in various natural food products, many in vitro evidence indicated that resveratrol (RES) has been linked to neuroprotective and cardioprotective effects and prevent cancer development. However, human clinical trials have been conducted with varying results, making the usage of RES controversial. In this paper, we demonstrated that the drug RES could be conjugated with the high levels of endogenous GS• in cancer cells. 5,5-Dimethyl-1-Pyrroline-N-Oxide (DMPO) was employed to capture the GS•. The molecular mechanism of the reaction between RES and GS• was further studied by UV-Vis spectrometry, mass spectrometry and Density Functional Theory (DFT) calculations. Besides, the formation of the adduct GS-RES in cancer cell was obtained when RES was added during incubation. Further study indicated that over 77.6% of the RES was consumed in cancer cells. This study suggested that endogenous GS• may be one of the important factors to cause the depletion of anti-tumour drugs during chemotherapy, which should be paid special attention in clinical therapeutics and drug development.

One-pot facile synthesis of CuNCs/RGO nanocomposite for the sensitive detection of heparin in human serum samples.

A simple and facile one-pot approach for the synthesis of copper nanoclusters decorated reduced graphene oxide (CuNCs/RGO) nanocomposite was proposed, in which the CuNCs attached to the surface of the reduced glutathione (GSH) functionalized RGO through ligand exchange via their thiol functionalities. The synthesized nanocomposite was verified by structural characterizations, and the further investigation of density functional theory (DFT) indicated that Cu3R2 cluster (R = C10H16O6N3S) with the lowest energy was the most stable structure in GSH-capped CuNCs. Although the CuNCs/RGO nanocomposite exhibited rather weak fluorescence, with the addition of heparin (Hep), the significant enhancement of fluorescence at 595 nm was achieved, which was developed to detect Hep in human serum samples with high selectivity and sensitivity. The mechanisms of fluorescence quenching of CuNCs/RGO nanocomposite and the sensing of Hep were discussed. The linear range was 0.1-10 µM with the detection limit of 26 nM in buffer solution containing 2% human serum sample, and satisfactory recovery in the range of 96.6%-104% was obtained, suggesting that the proposed method could applied to the detection of Hep in human serum samples.

The binding mechanism of nitroreductase fluorescent probe: Active pocket deformation and intramolecular hydrogen bonds.

Nitroreductase (NTR), a member of the flavoenzyme family, could react with nicotinamide adenine dinucleotide by reducing nitro to amino at hypoxic tumor, which can be monitored by some fluorescent probes in vivo. Here, molecular docking and molecular dynamics simulation techniques were used to explore the molecular mechanisms between NTR and probes. The results showed that formation of hydrogen bond in 1F5V-13 between A@His215 and B@Ser41 with 74.53% occupancy might be the main reason for the decrease of probe fluorescence emission in experiment. Moreover, Probe 16 was rotated by nearly 60 degrees with respect to the position of other probes in protein binding pocket, deforming the protein active pocket, changing the hydrogen bond formation, which leads to the fluorescence performance of 16 with electron donor and electron acceptor groups was superior to other probes in experiment. The deformation of protein active pocket and the formation of intramolecular hydrogen bonds revealed the difference in performance of NTR fluorescent probe at molecular level, which provide theoretical guidance for latter design of fluorescent probes with better performance.

A Simple Approach to the Toxicity Prediction of Anilines and Phenols Towards Aquatic Organisms.

Chemicals pollution in the environment has attracted attention all over the world, and the toxicity prediction of chemical pollutants has become quite important. In this paper, we introduce a simple approach to predict the toxicity of some chemical components, in which the Tchebichef image moment (TM) method was employed to extract useful chemical information from the images of molecular structures to establish quantitative structure-activity relationship (QSAR) prediction models. The proposed approach was applied to predict the toxicity of anilines and phenols for the aquatic organisms of P. subcapitata and V. fischeri, in which the obtained TMs were defined as the independent variables, while the biological toxicity (pEC50) was regarded to be the dependent variable. Then, the predictive models were established by stepwise regression, respectively. The obtained squared correlation coefficients of leave-one-out cross-validation (Q2) for training sets and the predictive squared correlation coefficients (Rp2) for test sets of the two groups of data were higher than 0.79 and 0.75, respectively, which indicated that the obtained models possessed satisfactory accuracy and reliability. Compared with several reported methods, the proposed approach was more convenient and has a higher predictive capability. Our study provides another perspective in QSAR research.

The activity prediction of indole inhibitors against HCV NS5B polymerase.

Non-structural viral protein 5B (NS5B) is a viral protein in hepatitis C virus. Although various inhibitors against NS5B have been found, the activity prediction of similar untested inhibitors is still highly desirable. In this respect, the Tchebichef moments (TMs) calculated from the images of molecular structures were regarded as the independent variables while the inhibitory activity (pIC50 ) was the dependent variable, and the predictive model was established by means of stepwise regression. The R-squared of leave-one-out cross-validation (Q2 ) for the training set and the R-squared of prediction ( Rp2 ) for external independent test set were 0.919 and 0.927, respectively. The obtained model was also evaluated strictly. Compared with the multivariate curve resolution with alternating least squares (MCR-ALS) and the QSAR approaches derived from the literature, the proposed method is more accurate and reliable. This study not only provides an effective approach to predict the biological activity of RNA replication's inhibitors, but also extends the QSAR modeling technique.

In silico toxicity evaluation of dioxins using structure-activity relationship (SAR) and two-dimensional quantitative structure-activity relationship (2D-QSAR).

Prediction of pEC50 values of dioxins binding with the aryl hydrocarbon receptor (AhR) is of great significance for exploring how dioxins induce toxicity in human body and evaluating their environmental behaviors and risks. To reveal the factors that influence the toxicity of dioxins, provide more accurate mathematical models for predicting the pEC50 values of dioxins, and supplement the toxicity database of persistent organic pollutants, qualitative structure-activity relationship (SAR) and two-dimensional quantitative structure-activity relationship (2D-QSAR) were used in this study. The research objects in this study were 60 organic compounds with pEC50 values and 162 compounds without pEC50 values, which included polychlorinated dibenzofurans (PCDFs), polychlorinated dibenzo-p-dioxins (PCDDs), and polybrominated dibenzo-p-dioxins (PBDDs). The qualitative structure-activity relationship (SAR) was performed first and concluded that halogen substitutions at any of the 2, 3, 7, and 8 sites increased the pEC50 value of the compound. Moreover, two-dimensional quantitative structure-activity relationship (2D-QSAR) models were established by employing multiple linear regression (MLR) method and artificial neural network (ANN) algorithm to investigate the factors affecting the pEC50 values of dioxins molecules. MLR was used to establish the well-understood linear model and ANN was used to establish a more accurate non-linear model. Both models have good fitting, robustness, and predictive ability. Importantly, the ability of dioxins binding to AhR is mainly determined by molecular descriptors including E1m, SM09_AEA (dm), RDF065u, F05 [Cl-Cl], and Neoplastic-80. In addition, the pEC50 values of the 162 dioxins without toxicity data were predicted by MLR and ANN models, respectively.

Applications of Discrete Shmaliy Moments on the Quantitative Analysis of Multitarget Compounds Based on the Chemical Spectra.

To extract the features in first-order or second-order signals, the two kinds of discrete Shmaliy moment (DSM) methods were proposed and applied to the quantitative analysis of multitarget compounds in complexes based on the UV-vis and high-performance liquid chromatography with pulsed amperometric detector (HPLC-PAD) spectra of samples for the first time. All the statistical parameters demonstrated that the obtained models were accurate and the established analytical methods were reliable, even in the presence of a different degree of overlapping signals as well as various interferences. Compared with Tchebichef moment (TM) and other classical methods such as multivariate curve resolution-alternating least-squares (MCR-ALS), partial least-squares (PLS) regression, and N-way partial least-squares (N-PLS), the proposed methods are more convenient and efficient, which not only provides another suitable tool for the quantitative analysis of multitarget components in complex samples but also extends the application of moment invariants in chemical signal analyses.

Study on Biocompatibility of AuNPs and Theoretical Design of a Multi-CDR-Functional Nanobody.

The investigation on proteinlike specific functions of nanoparticles (NPs) has been a huge challenge. Here, the biocompatibility of Au nanoparticles (AuNPs) to antigens hen egg white lysozyme and epidermal growth factor receptor was studied first by molecular dynamics (MD) simulations and the research results revealed that antigens could form quickly a stable binding with the AuNPs and kept the structural integrity of the protein, which demonstrated better biocompatibility of AuNPs. Then, two types of complementary-determining regions (CDRs) were grafted onto the AuNPs to design a novel multi-CDR-functional nanobody. By means of MD simulations under physiological conditions, we found that the bindings of the designed nanobody and the antigens were stable and safe. Compared with the results of antigens interacting with the natural antibody, the redundant CDRs on AuNPs bound with the nonactive site in the antigens to form a stable conformation, which leaded to the powerful binding capacity of the designed nanobody than that of the natural antibody. This study provided available insights into the biocompatibility of AuNPs and important theoretical proofs to the multi-CDR-functional nanobody applied in biological systems, which were expected to help in design of novel multifunctional nanobodies.