From precursor to final peptides: A statistical sequence-based approach to predicting prohormone processing
From precursor to final peptides: A statistical sequence-based approach to predicting prohormone processing
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DOI：
10.1021/pr034046d
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发表时间：
2003-11-01
影响因子：
4.4
通讯作者：
Sweedler, JV
中科院分区：
文献类型：
--
作者：
Hummon, AB;Hummon, NP;Sweedler, JV
从神经肽基因预测最终的神经肽产物一直是一个问题，因为大量的酶负责它们的加工。对代表750个裂解位点的22种海兰前激素的基本加工过程进行了分析，并用二元Logistic回归分析对其进行了统计建模。提出了两种基于前激素序列预测碱性残基切割概率的模型。当在海兔数据集上进行测试时，该复杂模型的正确分类率为97%，灵敏度为97%，特异度为96%。
Predicting the final neuropeptide products from neuropeptides genes has been problematic because of the large number of enzymes responsible for their processing. The basic processing of 22 Aplysia californica prohormones representing 750 cleavage sites have been analyzed and statistically modeled using binary logistic regression analyses. Two models are presented that predict cleavage probabilities at basic residues based on prohormone sequence. The complex model has a correct classification rate of 97%, a sensitivity of 97%, and a specificity of 96% when tested on the Aplysia dataset.