  Thermostability is an important prerequisite for enzymes employed for
industrial applications. Several machine learning based models have thus been
formulated for protein classification based on this particular trait. These
models have employed features derived from sequences, structures or both
resulting in a >93% accuracy based on a 10-fold cross-validation. Besides using
various proteins from a wide range of organisms, such studies also rely on
hundreds of features. In the present study, an enzyme specific classification
model was created using significantly less number of features that provides a
similar accuracy of classification for thermophilic and non-thermophilic enzyme
serine proteases. For building the classifier, 219 thermophilic and 200
mesophilic bacterial genomes were mined for their respective serine protease
sequences. Features were extracted for 800 sequences followed by feature
selection. We deployed a random forest based classifier that identified
thermophilic and non-thermophilic serine proteases with an accuracy of 95.71%.
Knowledge of thermostability along with amino acid positional shifts can be
vital for downstream protein engineering techniques. Thus, to emphasize the
real time application of the enzyme specific classification model, a web
platform has been designed. Combining the sequence data and the classification
model, this prototype can allow users to align their query serine protease
sequence against the custom database and identify its thermophilic nature.
