Protease Substrate Site Predictors Based on Multilevel Substrate Phage Display Data
BackgroundPredictions of the substrate sites in a proteome for the proteases would facilitate understanding the biological functions of the proteases. High throughput experiments could generate suitable dataset for machine learning to grasp complex relationships between the substrate sequences and the enzymatic specifieities.But the capability in predicting protease substrate sites by integrating the machine learning algorithms with the experimental methodology has yet to be demonstrated.Data & MethodsFactor Xa, a key regulatory protease in the blood coagulation system, was used as model system. A multilevel substrate phage display experiment together with quantitative enzyme-linked immuno sorbent assay (ELISA) were carried out to produce the dataset (Hsu, H.J., et al), named DS-312, consisting of 312 6-residue sequences as well as the corresponding kobs values which represent the binding specificity between the substrate sequences and factor Xa (fXa).
Ching-Tai Chen Ei-Wen Yang Wen-Lian Hsu An-Suei Yang
Institute of Information Science, Academia Sinica, Taipei, Taiwan 115 Institute of bioinformatics, N Institute of Information Science, Academia Sinica, Taipei, Taiwan 115 Genomics Research Center, Academia Sinica, Taipei, Taiwan 115
国际会议
The 7th Asia-Pacific Bioinformatics Conference(第七届亚太生物信息学大会)
北京
英文
875
2009-01-01（万方平台首次上网日期，不代表论文的发表时间）