||January 6, 2012
||Dr. Jiangning Song, Department of Biochemistry& Molecular Biology Faculty of Medicine, Monash University, Australia
Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences
||Predicting substrate cleavage sites of proteases using machine learning techniques and sequence-derived features
Proteases have central roles in “life and death” processes due to their
important ability to catalytically hydrolyze protein substrates, usually
altering the function and/or activity of the target in the process.
Knowledge of the substrate specificity of a protease should, in theory,
dramatically improve the ability to predict target protein substrates.
However, experimental identification and characterization of protease
substrates is often difficult and time-consuming. Thus solving the
“substrate identification” problem is fundamental to both understanding
protease biology and the development of therapeutics that target
specific protease-regulated pathways. Solving the “substrate
identification” problem is fundamental to both understanding protease
systems biology and the development of therapeutics that target specific
protease regulated pathways. In this talk, I will describe the
development of novel bioinformatic approaches to make testable
predictions in regards to the biological targets of proteases.