RoCO-ET: Structured Conditional Observation Learning for Enzyme Temperature Prediction

Predicting temperature properties from enzyme sequences is important for selecting enzymes suited to target temperature conditions and guiding the design of experimental conditions. However, temperature observations are affected by multiple experimental conditions, and the relevant context is incompletely documented. Observations with large deviations can therefore exert excessive influence on model fitting. Predicting active temperature ranges additionally requires learning both range location and span. To address these challenges, we propose RoCO-ET (Robust Conditional Observation for Enzyme Temperature Prediction), which unifies the modeling of scalar temperatures and ordered active temperature ranges in a sequence-conditioned location–scale framework. The model uses conditional scales to measure observation deviations, modulates the influence of large standardized residuals on fitting through a Student-t objective, and treats the two range endpoints jointly as a complete observation during training. A readout parameterized by a center and a positive half-width produces ordered endpoints by construction, allowing record-level modulation to act on both range location and span. On three public tasks, compared with PatchET under the same protocol, RoCO-ET reduces root mean square error (RMSE) by 10.88% for optimum temperature prediction and by 6.46% for operational stability temperature prediction, and increases mean intersection over union (IoU) for active temperature ranges from 0.516 to 0.581. Matched ablations and training interventions further show that sequence-dependent scales and joint modulation of complete records together improve temperature prediction and range fitting.