Wee Lawrence J K, Tan Tin Wee, Ranganathan Shoba
Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
Bioinformatics. 2007 Dec 1;23(23):3241-3. doi: 10.1093/bioinformatics/btm334. Epub 2007 Jun 28.
Caspases belong to a unique class of cysteine proteases which function as critical effectors of apoptosis, inflammation and other important cellular processes. Caspases cleave substrates at specific tetrapeptide sites after a highly conserved aspartic acid residue. Prediction of such cleavage sites will complement structural and functional studies on substrates cleavage as well as discovery of new substrates. We have recently developed a support vector machines (SVM) method to address this issue. Our algorithm achieved an accuracy ranging from 81.25 to 97.92%, making it one of the best methods currently available. CASVM is the web server implementation of our SVM algorithms, written in Perl and hosted on a Linux platform. The server can be used for predicting non-canonical caspase substrate cleavage sites. We have also included a relational database containing experimentally verified caspase substrates retrievable using accession IDs, keywords or sequence similarity.
半胱天冬酶属于一类独特的半胱氨酸蛋白酶，在细胞凋亡、炎症及其他重要细胞过程中起着关键效应器的作用。半胱天冬酶在高度保守的天冬氨酸残基之后的特定四肽位点切割底物。预测此类切割位点将补充对底物切割的结构和功能研究，以及新底物的发现。我们最近开发了一种支持向量机（SVM）方法来解决这个问题。我们的算法准确率在81.25%至97.92%之间，使其成为目前可用的最佳方法之一。CASVM是我们用Perl编写并托管在Linux平台上的SVM算法的网络服务器实现。该服务器可用于预测非典型半胱天冬酶底物切割位点。我们还包含一个关系数据库，其中包含可通过登录号、关键词或序列相似性检索的经实验验证的半胱天冬酶底物。
