PoPS: a computational tool for modeling and predicting protease specificity.

Sarah E Boyd, Maria Garcia de la Banda, Robert N Pike, James C Whisstock, George B Rudy
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引用次数: 55

Abstract

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.

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持久性有机污染物:建模和预测蛋白酶特异性的计算工具。
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着重要作用。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/),它提供了一种新的方法来构建蛋白酶特异性的计算模型,虽然仍然基于这些氨基酸序列,但可以从任何实验数据或用户可用的专家知识中构建。持久性有机污染物特异性模型可用于预测单个底物和整个蛋白质组内可能的裂解并对其进行排序。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位点。此外,该工具还提供了推断、比较和测试模型的工具,并将它们存储在可公开访问的数据库中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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