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.