Hydroxylase Thermostability Prediction Based on Self-Trained Semisupervised Iteration and Bayesian Dynamic Tuning

Current enzyme thermostability prediction models are predominantly designed for cross-family generalization, with limited focus on hydroxylases, which restricts their accuracy and applicability in hydroxylase-specific thermostability design. In this study, we develop HyS-BST, a dedicated self-trained semisupervised framework for hydroxylase thermostability prediction. Leveraging a limited hydroxylase data set, HyS-BST integrates a self-training strategy with Bayesian dynamic tuning to achieve high-precision prediction of mutant thermostability in terms of ΔΔG. Experimental results demonstrate that after only ten training iterations, HyS-BST attains a coefficient of determination (R2) of 0.96, a Pearson correlation coefficient (PCC) of 0.98, and a root mean squared error (RMSE) as low as 0.06 on the test set. Compared with the optimal cross-family generalization model, HyS-BST improves PCC and RMSE by approximately 70%. Overall, this framework provides a specialized, efficient, and cost-effective solution for hydroxylase thermostability prediction, substantially reducing the candidate search space and experimental resources required for downstream validation.