PoPS: A computational tool for modeling and predicting protease specificity
PoPS: A computational tool for modeling and predicting protease specificity
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DOI：
10.1142/s021972000500117x
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发表时间：
2005-06-01
影响因子：
1
通讯作者：
De La Banda, Maria Garcia
中科院分区：
文献类型：
--
作者：
Boyd, Sarah E.;Pike, Robert N.;De La Banda, Maria Garcia
蛋白酶通过结合和切割特定的氨基酸序列，在控制细胞内和细胞外过程中发挥重要作用。确定这些目标极具挑战性。目前预测切割位点的计算尝试是有限的，将这些氨基酸序列表示为模式或频率矩阵。在这里，我们提出了PoPS，一个公开访问的生物信息学工具（http：//pops.csse.monash.edu.au/），它提供了一种新的方法，用于建立蛋白酶特异性的计算模型，同时仍然是基于这些氨基酸序列，可以建立从任何实验数据或专家知识提供给用户。PoPS特异性模型可用于预测和排序单个底物内和整个蛋白质组内的可能裂解。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位点。此外，该工具还提供了推断、比较和测试模型的设施，并将其存储在一个可公开访问的数据库中。
Proteases play a fundamental role in the control of intra- and extra-cellular 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/) that provides a novel method for building computational models of protease specificity, which 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.