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.

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.

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.

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.