Rapid and Simple Analysis of the Human Pepsin Secondary Structure Using a Portable Raman Spectrometer.

Human pepsin is a digestive protease that plays an important role in the human digestive system. The secondary structure of human pepsin determines its bioactivity. Therefore, an in-depth understanding of human pepsin secondary structure changes is particularly important for the further improvement of the efficiency of human pepsin biological function. However, the complexity and diversity of the human pepsin secondary structure make its analysis difficult. Herein, a convenient method has been developed to quickly detect the secondary structure of human pepsin using a portable Raman spectrometer. According to the change of surface-enhanced Raman spectroscopy (SERS) signal intensity and activity of human pepsin at different pH values, we analyze the change of the human pepsin secondary structure. The results show that the content of the ß-sheet gradually increased with the increase in the pH in the active range, which is in good agreement with circular dichroism (CD) measurements. The change of the secondary structure improves the sensitivity of human pepsin SERS detection. Meanwhile, human pepsin is a commonly used disease marker for the noninvasive diagnosis of gastroesophageal reflux disease (GERD); the detection limit of human pepsin we obtained is 2 µg/mL by the abovementioned method. The real clinical detection scenario is also simulated by spiking pepsin solution in saliva, and the standard recovery rate is 80.7-92.3%. These results show the great prospect of our method in studying the protein secondary structure and furthermore promote the application of SERS in clinical diagnosis.

Computational prediction for the metabolism of human UDP-glucuronosyltransferase 1A1 substrates.

UDP-glucuronosyltransferase (UGT) 1A1, one of the most important isoforms in UGTs superfamily, has attracted increasing concerns for its special role in the clearance and detoxification of endogenous and exogenous substances. To avoid the clinical drug-drug interactions, it is of great importance to have the knowledge of the metabolic profile of UGT1A1 substrates early. Herein, we purposed to establish machine learning models to predict the metabolic propeties of UGT1A1 substrates. On the basis of the literature-derived substrates database of UGT1A1, automatic metabolism prediction models for the aromatic hydroxyl (ArOH) and carboxyl (COOH) groups were developed with eight machine learning methods, among which, three methods, i.e. Random Forest, Random Subspace and J48, illustrated the best performance either for the aromatic hydroxyl and the carboxyl model. The models illustrated good robustness when they were evaluated with functions like "Precision", "Recall", "F-Measure", "AUC", "MCC", etc. Nice accuracy was observed for the aromatic hydroxyl and carboxyl model of these methods, whose AUCs ranged from 0.901 to 0.997. Additionally, the ArOH model was applied to predict the UGT1A1-mediated metabolism of an external set. Two new unknown substrates, cytochrome P450 (CYPs)-mediated metabolites of gefitinib, were predicted and identified, which were validated by in vitro assays. In summary, this study provides a reliable and robust strategy to predict UGT1A1 metabolites, which will be helpful either in rational-optimization of drug metabolism or in avoiding drug-drug interactions in clinic.