The xylose reductase of Pichia stipitis is one of the most important enzymes. It can be used to build up recombinant Saccharomyces cerevisiae strain for utilizing xylose and producing ethanol. Intercellular redox imbalance caused by NADPH preference over NADH for Pichia stipitis xylose reductase (PsXR) has been considered to be one of the main factors for poor ethanol productivity. Some key amino acids of PsXR, which affect the activity or coenzyme preference, were investigated in our previous study. In this study, Lys21 were rational designed for site-directed mutagenesis to alter coenzyme specificity of PsXR from NADPH and NADH into single NADH. The wild gene and mutagenesis genes were ligated into pET28b, and were transferred into E.coli BL21(DE3). After induced by IPTG, the xylose reductases were purified. Purified mutants K21A (Lys21-->Ala), K21R(Lys21-->Arg) were characterized by steady-state kinetic analysis. The results showed that the coenzyme dependence of K21A was completely reversed to NADH.
Aldehyde Reductase ; metabolism ; Amino Acid Substitution ; genetics ; Coenzymes ; pharmacology ; Escherichia coli ; genetics ; metabolism ; Ethanol ; pharmacology ; Lysine ; genetics ; Mutagenesis, Site-Directed ; NAD ; metabolism ; NADP ; metabolism ; Pichia ; chemistry ; genetics ; metabolism ; Recombinant Proteins ; biosynthesis ; genetics ; metabolism ; Recombination, Genetic ; Xylose ; pharmacology

ObjectiveThis study aims to develop a prediction model for the compatibility of Chinese medicinal pairs based on Graph Convolutional Networks （GCN）， named HC-GCN. The model integrates the properties of herbs with modern pharmacological mechanisms to predict pairs with specific therapeutic effects. It serves as a demonstration by applying the model to predict and validate the efficacy of blood-activating herb pairs. MethodsThe training dataset for herb pair prediction was constructed by systematically collecting commonly used herb pairs along with their characteristic data， including Qi， flavor， meridian tropism， and target genes. Integrating traditional characteristics of herb with modern bioinformatics， we developed an efficacy-oriented herb pair compatibility prediction model （HC-GCN） using graph convolutional networks （GCN）. This model leverages machine learning to capture the complex relationships in herb pair compatibility， weighted by efficacy features. The performance of the HC-GCN model was evaluated using accuracy （ACC）， recall， precision， F1 score （F1）， and area under the ROC curve （AUC）. Its predictive effectiveness was then compared to five other machine learning models： eXtreme Gradient Boosting （XGBoost）， logistic regression （LR）， Naive Bayes， K-nearest neighbor （KNN）， and support vector machine （SVM）. ResultsUsing herb pairs with blood-activating effects as a demonstration， a prediction model was constructed based on a foundational dataset of 46 blood-activating herb pairs， incorporating their Qi， flavor， meridian tropism， and target gene characteristics. The HC-GCN model outperforms other commonly used machine learning models in key performance metrics， including ACC， recall， precision， F1 score， and AUC. Through the predictive analysis of the HC-GCN model， 60 herb pairs with blood-activating effects were successfully identified. Among of these potential herb pairs， 44 include at least one herb with blood-activating effects. ConclusionIn this study， we established an efficacy-oriented compatibility prediction model for herb pairs based on GCN by integrating the unique characteristics of traditional herbs with modern pharmacological mechanisms. This model demonstrated high predictive performance， offering a novel approach for the intelligent screening and optimization of traditional Chinese medicine prescriptions， as well as their clinical applications.