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Minimal and optimal subsites set of HIV-1 protease cleavage site based on rough set

机译:基于粗糙集的HIV-1蛋白酶切割位点的最小和最佳亚位点集

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Identifying the cleavage sites in proteins by HIV protease will greatly expedite the pace in searching for proper inhibitors of HIV protease. In this study, we focus on how many subsites are needed for predicting the cleavage sites. Using the rough set theory of information gain and reduct to search for the minimal and optimal subsites set, we found that, instead of the 8-subsite octapeptide model as usually adopted, the 6-subsite hexapeptide model would suffice to obtain equally good or even slightly better results. To verify the conclusion, prediction with the 6-subsite hexapeptide mode and the 8-subsite octapeptide model are both studied. And the predictive rate and ROC curve are also reported. Such a finding not only suggests many tedious labors could be avoided in synthesizing peptide inhibitor candidates, but also implies it would be possible to design smaller peptide drugs based on the distorted key theory.
机译:通过HIV蛋白酶鉴定蛋白质中的切割位点将大大加快寻找合适的HIV蛋白酶抑制剂的步伐。在这项研究中,我们集中于预测分裂位点需要多少个子位点。使用信息获取和还原的粗糙集理论来搜索最小和最佳子位点集,我们发现,代替通常采用的8个子位八肽模型,6个子位六肽模型足以获得同样好的结果,甚至效果略好。为了验证结论,研究了6-亚位六肽模式和8-亚位八肽模型的预测。并报告了预测率和ROC曲线。这样的发现不仅表明可以在合成肽抑制剂候选物中避免许多繁琐的工作,而且还暗示有可能基于扭曲的关键理论设计较小的肽药物。

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