TCM-Blast for traditional Chinese medicine genome alignment with integrated resources

Screening of rosmarinic acid from Salvia miltiorrhizae acting on the novel target TRPC1 based on the 'homology modelling-virtual screening-molecular docking-affinity assay-activity evaluation' method

CONTEXT

Salvia miltiorrhizae Bunge (Lamiaceae) is a traditional Chinese medicine (TCM) for the treatment of 'thoracic obstruction'. Transient receptor potential canonical channel 1 (TRPC1) is a important target for myocardial injury treatment.

OBJECTIVE

This work screens the active component acting on TRPC1 from Salvia miltiorrhizae.

MATERIALS AND METHODS

TCM Systems Pharmacology Database and Analysis Platform (TCMSP) was used to retrieve Salvia miltiorrhiza compounds for preliminary screening by referring to Lipinski's rule of five. Then, the compound group was comprehensively scored by AutoDock Vina based on TRPC1 protein. Surface plasmon resonance (SPR) was used to determine the affinity of the optimal compound to TRPC1 protein. Western blot assay was carried out to observe the effect of the optimal compound on TRPC1 protein expression in HL-1 cells, and Fura-2/AM detection was carried out to observe the effect of the optimal compound on calcium influx in HEK293 cells.

RESULTS

Twenty compounds with relatively good characteristic parameters were determined from 202 compounds of Salvia miltiorrhiza. Rosmarinic acid (RosA) was obtained based on the molecular docking scoring function. RosA had a high binding affinity to TRPC1 protein (KD value = 1.27 µM). RosA (50 μM) could reduce the protein levels (417.1%) of TRPC1 after oxygen-glucose deprivation/reperfusion (OGD/R) in HL-1 cells and it could inhibit TRPC1-mediated Ca²⁺ influx injury (0.07 ΔRatio340/380) in HEK293 cells.

DISCUSSION AND CONCLUSIONS

We obtained the potential active component RosA acting on TRPC1 from Salvia miltiorrhizae, and we speculate that RosA may be a promising clinical candidate for myocardial injury therapy.

ResNet18DNN: prediction approach of drug-induced liver injury by deep neural network with ResNet18

Drug-induced liver injury (DILI) has always been the focus of clinicians and drug researchers. How to improve the performance of the DILI prediction model to accurately predict liver injury was an urgent problem for researchers in the field of medical research. In order to solve this scientific problem, this research collected a comprehensive and accurate dataset of DILI with high recognition and high quality based on clinically confirmed DILI compound datasets, including 1446 chemical compounds. Then, the residual neural network with 18-layer by using more 5-layer blocks (ResNet18) with deep neural network (ResNet18DNN) model was proposed to predict DILI, which was an improved model for DILI prediction through vectorization of compound structure image. In predicting DILI, the ResNet18DNN learned greatly and outperformed the existing state-of-the-art DILI predictors. The results of DILI prediction model based on ResNet18DNN showed that the AUC (area under the curve), accuracy, recall, precision, F1-score and specificity of the training set were 0.973, 0.992, 0.995, 0.994, 0.995 and 0.975; those of test set were, respectively, 0.958, 0.976, 0.935, 0.947, 0.926 and 0.913, which were better than the performance of previously published described DILI prediction models. This method adopted ResNet18 embedding method to vectorize molecular structure images and the evaluation indicators of Resnet18DNN were obtained after 10 000 iterations. This prediction approach will greatly improve the performance of the predictive model of DILI and provide an accurate and precise early warning method for DILI in drug development and clinical medication.