The Effect of Acidic Residues on the Binding between Opicalcin1 and Ryanodine Receptor from the Structure-Functional Analysis.

Calcin is a group ligand with high affinity and specificity for the ryanodine receptors (RyRs). Little is known about the effect of its acidic residues on the spacial structure as well as the interaction with RyRs. We screened the opicalcin1 acidic mutants and investigated the effect of mutation on activity. The results indicated that all acidic mutants maintained the structural features, but their surface charge distribution underwent significant changes. Molecular docking and dynamics simulations were used to analyze the interaction between opicalcin1 mutants and RyRs, which demonstrated that all opicalcin1 mutants effectively bound to the channel domain of RyR1. This stable binding induced a pronounced asymmetry in the structure of the RyR tetramer, exhibiting a high degree of structural dissimilarity. [3H]Ryanodine binding to RyR1 was enhanced in D2A and D15A, which was similar to opicalcin1, but that effect was suppressed in E12A and E29A and reversed for the DE-4A, thereby inhibiting ryanodine binding. Opicalcin1 and DE-4A also exhibited the ability to form stable docking structures with RyR2. Acidic residues play a crucial role in the structure of calcin and its functional interaction with RyRs that is beneficial for the calcin optimization to develop more active peptide lead compounds for RyR-related diseases.

Building a predictive model for hypertension related to environmental chemicals using machine learning.

Hypertension is a chronic cardiovascular disease characterized by elevated blood pressure that can lead to a number of complications. There is evidence that the numerous environmental substances to which humans are exposed facilitate the emergence of diseases. In this work, we sought to investigate the relationship between exposure to environmental contaminants and hypertension as well as the predictive value of such exposures. The National Health and Nutrition Survey (NHANES) provided us with the information we needed (2005-2012). A total of 4492 participants were included in our study, and we incorporated more common environmental chemicals and covariates by feature selection followed by regularized network analysis. Then, we applied various machine learning (ML) methods, such as extreme gradient boosting (XGBoost), random forest classifier (RF), logistic regression (LR), multilayer perceptron (MLP), and support vector machine (SVM), to predict hypertension by chemical exposure. Finally, SHapley Additive exPlanations (SHAP) were further applied to interpret the features. After the initial feature screening, we included a total of 29 variables (including 21 chemicals) for ML. The areas under the curve (AUCs) of the five ML models XGBoost, RF, LR, MLP, and SVM were 0.729, 0.723, 0.721, 0.730, and 0.731, respectively. Butylparaben (BUP), propylparaben (PPB), and 9-hydroxyfluorene (P17) were the three factors in the prediction model with the highest SHAP values. Comparing five ML models, we found that environmental exposure may play an important role in hypertension. The assessment of important chemical exposure parameters lays the groundwork for more targeted therapies, and the optimized ML models are likely to predict hypertension.