报告题目：Bioinformatic prediction of protease substrate cleavage sites: application of machine learning techniques

　　报告人：Dr. Song Jiangning

　　报告时间：2013年1月21日 16:10~17:30

　　报告地点：信息工程学院二层多功能厅

　　报告摘要：
　　
　　The ability to catalytically cleave protein substrates after synthesis is fundamental for all forms of life. Accordingly, site-specific proteolysis is one of the most important post-translational modifications. The key to our understanding of the physiological role of a protease is to identify its natural substrate(s). Knowledge of the substrate specificity of a protease can dramatically improve our ability to predict its target protein substrates, but this information must be utilized in an effective manner by in silico approaches in order to efficiently identify protein substrates. To address this problem, we present PROSPER, an integrated feature-based server for in silico identification of protease substrates and their cleavage sites for twenty-four different protease families. PROSPER utilizes established specificity information for these proteases (derived from the MEROPS database) with a machine learning approach to predict protease cleavage sites by using different, but complementary sequence and structure characteristics. Features used by PROSPER include local amino acid sequence profile, predicted secondary structure, solvent accessibility and predicted native disorder. Thus, for proteases with known amino acid specificity, PROSPER provides a convenient, pre-prepared tool for use in identifying protein substrates for the enzymes. Systematic prediction analysis for the twenty-four proteases thus far included in the database revealed that the features we have included in the tool strongly improve performance in terms of cleavage site prediction, as evidenced by their contribution to the performance improvement. In comparison with two state-of-the-art prediction tools, PoPS and SitePrediction, PROSPER achieves a greater accuracy and coverage. To our knowledge, PROSPER is the first comprehensive server capable of predicting cleavage sites of multiple proteases within a single substrate sequence using machine learning techniques.

　　报告人简介：

　　Dr. Song Jiangning obtained Ph.D. degree in Bioinformatics in 2005.  From 2005 to 2007, he was a Postdoctoral Research Fellow at the Advanced Computational Modelling Centre (ACMC), The University of Queensland, Australia. From 2007 to 2009, he was a JSPS Research Fellow at the Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan. He is currently an NHMRC Peter Doherty Fellow in ARC Federation Fellow Prof. James Whisstock’s group in the Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Australia. His research interests are mainly in structural bioinformatics, computational systems biology, machine learning and data mining.

　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　信息工程学院
　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　2013年1月11日