アブストラクト
| Date | January 6, 2012 |
| Speaker | Dr. Jiangning Song, Department of Biochemistry& Molecular Biology Faculty of Medicine, Monash University, Australia
Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences |
| Title | Predicting substrate cleavage sites of proteases using machine learning techniques and sequence-derived features |
| Abstract | 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. |