Modeling Enzyme Temperature Stability from Sequence Segment Perspective

Developing enzymes with desired thermal properties is
crucial for
a wide range of industrial and research applications, and determining
temperature stability is an essential step in this process. Experimental
determination of thermal parameters is labor-intensive, time-consuming,
and costly. Moreover, existing computational approaches are often
hindered by limited data availability and imbalanced distributions.
To address these challenges, we introduce a curated temperature stability
data set designed for model development and benchmarking in enzyme
thermal modeling. Leveraging this data set, we present the Segment Transformer, a novel deep learning framework that
enables efficient and accurate prediction of enzyme temperature stability.
The model achieves state-of-the-art performance with RMSE of 23.29,
MAE of 17.37, Pearson correlation of 0.35, and Spearman correlation
of 0.34, respectively. These results highlight the effectiveness of
incorporating segment-level representations, grounded in the biological
observation that different regions of a protein sequence contribute
unequally to thermal behavior. As a proof of concept, we applied the
Segment Transformer to guide the engineering of a cutinase enzyme.
Experimental validation demonstrated a 1.64-fold improvement in relative
activity following heat treatment, achieved through only 17 mutations
and without compromising catalytic function.