Background: Peptidases are a group of enzymes which catalyzes the cleavage of
peptide bonds. Around 2-3% of the whole genome codes for proteases and about one third of
all known proteases are serine proteases which are divided into 13 clans and 40 families. They
are involved in diverse physiological roles such as digestion, coagulation of blood,
fibrinolysis, processing of proteins and prohormones, signaling pathways, complement
fixation and have a vital role in immune defense system. Based on their functions, they can
broadly be divided into two classes; GASPIDs (Granule Associated Serine Peptidases
involved in Immune Defense System) and Non-GASPIDs. GASPIDs, in particular are
involved in immune associated functions i.e. initiating apoptosis to kill virally infected and
cancerous cells, cytokine modulation for generation of inflammatory responses and direct
killing of pathogens through phagosomes.
Methods: In this study, sequence-based characterization of these two types of serine proteases
is performed. We first identified sequences by analyzing multiple online databases as well as
by analyzing whole genomes of different species from different orthologous and nonorthologous species. Sequences were identified by devising a distinct criterion to differentiate
GASPIDs from Non-GASPIDs. The translated version of these sequences were then subjected
to feature extraction. Using these distinctive features, we differentiated GASPIDs from NonGASPIDs by applying multiple supervised machine learning models.
Results and Conclusion: Our results show that, among the three classifiers used in this study,
SVM classifier coupled with tripeptide as feature method, has shown the best accuracy in
classification of sequences as GASPIDs and Non-GASPIDs.