Boyd Sarah E, Garcia de la Banda Maria, Pike Robert N, Whisstock James C, Rudy George B
School of Computer Science and Software Engineering and Victorian Bioinformatics Consortium, Monash University, Melbourne, Australia.
Proc IEEE Comput Syst Bioinform Conf. 2004:372-81. doi: 10.1109/csb.2004.1332450.
Proteases play a fundamental role in the control of intra- and extracellular processes by binding and cleaving specific amino acid sequences. Identifying these targets is extremely challenging. Current computational attempts to predict cleavage sites are limited, representing these amino acid sequences as patterns or frequency matrices. Here we present PoPS, a publicly accessible bioinformatics tool (http://pops.csse.monash.edu.au/) which provides a novel method for building computational models of protease specificity that, while still being based on these amino acid sequences, can be built from any experimental data or expert knowledge available to the user. PoPS specificity models can be used to predict and rank likely cleavages within a single substrate, and within entire proteomes. Other factors, such as the secondary or tertiary structure of the substrate, can be used to screen unlikely sites. Furthermore, the tool also provides facilities to infer, compare and test models, and to store them in a publicly accessible database.
蛋白酶通过结合并切割特定氨基酸序列，在细胞内和细胞外过程的调控中发挥着重要作用。识别这些靶点极具挑战性。目前预测切割位点的计算方法有限，仅将这些氨基酸序列表示为模式或频率矩阵。在此，我们展示了PoPS，这是一种可公开访问的生物信息学工具（http://pops.csse.monash.edu.au/），它提供了一种构建蛋白酶特异性计算模型的新方法。该方法虽然仍基于这些氨基酸序列，但可以根据用户可获得的任何实验数据或专业知识构建。PoPS特异性模型可用于预测单个底物内以及整个蛋白质组中可能的切割位点，并对其进行排序。其他因素，如底物的二级或三级结构，可用于筛选不太可能的切割位点。此外，该工具还提供了推断、比较和测试模型以及将它们存储在可公开访问数据库中的功能。
