In Silico prediction of the Caspase degradome

Abstract

Caspases belong to a unique class of cysteine proteases which play critical roles in important processes such as cell death, differentiation and inflammation. The central feature of caspase function resides on their ability to selectively cleave cellular proteins at specific recognition motifs. The caspase degradome, or the natural repertoire of caspase substrates, spans across a multitude of functional classes. With more than 300 substrates characterized to date and many more expected to be discovered, the proteome-wide identification of caspase substrates presents a refreshing direction for deepening our understanding of caspase biology. In this thesis, novel computational studies were conducted to address this goal. Firstly, a multi-factor support vector machines system utilizing cleavage site specificities, solvent accessibilities and secondary structures was developed to predict for caspase cleavage sites. Secondly, the family of receptor tyrosine kinases was predicted for potential caspase substrates and the results were analyzed for functional implications.