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首页> 美国卫生研究院文献>PLoS Clinical Trials >Software-aided workflow for predicting protease-specific cleavage sites using physicochemical properties of the natural and unnatural amino acids in peptide-based drug discovery
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Software-aided workflow for predicting protease-specific cleavage sites using physicochemical properties of the natural and unnatural amino acids in peptide-based drug discovery

机译：使用基于肽的药物发现中天然和非天然氨基酸的理化特性预测蛋白酶特异性切割位点的软件辅助工作流程

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Peptide drugs have been used in the treatment of multiple pathologies. During peptide discovery, it is crucially important to be able to map the potential sites of cleavages of the proteases. This knowledge is used to later chemically modify the peptide drug to adapt it for the therapeutic use, making peptide stable against individual proteases or in complex medias. In some other cases it needed to make it specifically unstable for some proteases, as peptides could be used as a system to target delivery drugs on specific tissues or cells. The information about proteases, their sites of cleavages and substrates are widely spread across publications and collected in databases such as MEROPS. Therefore, it is possible to develop models to improve the understanding of the potential peptide drug proteolysis. We propose a new workflow to derive protease specificity rules and predict the potential scissile bonds in peptides for individual proteases. WebMetabase stores the information from experimental or external sources in a chemically aware database where each peptide and site of cleavage is represented as a sequence of structural blocks connected by amide bonds and characterized by its physicochemical properties described by Volsurf descriptors. Thus, this methodology could be applied in the case of non-standard amino acid. A frequency analysis can be performed in WebMetabase to discover the most frequent cleavage sites. These results were used to train several models using logistic regression, support vector machine and ensemble tree classifiers to map cleavage sites for several human proteases from four different families (serine, cysteine, aspartic and matrix metalloproteases). Finally, we compared the predictive performance of the developed models with other available public tools PROSPERous and SitePrediction.
机译：肽药物已用于治疗多种病理。在发现肽的过程中，至关重要的是能够绘制蛋白酶切割的潜在位点。该知识可用于以后对肽药物进行化学修饰以使其适合治疗用途，从而使肽对单个蛋白酶或在复杂介质中稳定。在另一些情况下，需要使它对某些蛋白酶特别不稳定，因为肽可以用作将药物靶向特定组织或细胞的系统。有关蛋白酶，其切割位点和底物的信息广泛分布在出版物中，并收集在MEROPS等数据库中。因此，有可能开发模型以增进对潜在肽药物蛋白水解的理解。我们提出了一个新的工作流程，以导出蛋白酶特异性规则并预测单个蛋白酶在肽中的潜在易断裂键。 WebMetabase将来自实验或外部来源的信息存储在化学上已知的数据库中，其中每个肽和裂解位点均表示为通过酰胺键连接的结构嵌段序列，并由Volsurf描述符描述其理化性质。因此，该方法可用于非标准氨基酸的情况。可以在WebMetabase中执行频率分析，以发现最频繁的切割位点。这些结果用于使用逻辑回归，支持向量机和集成树分类器训练几种模型，以绘制来自四个不同家族（丝氨酸，半胱氨酸，天冬氨酸和基质金属蛋白酶）的几种人类蛋白酶的切割位点。最后，我们将开发模型的预测性能与其他可用的公共工具PROSPERous和SitePrediction进行了比较。

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