NeuroCS: A Tool to Predict Cleavage Sites of Neuropeptide Precursors.
NeuroCS: A Tool to Predict Cleavage Sites of Neuropeptide Precursors.
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DOI：
10.2174/0929866526666191112150636
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发表时间：
2019-11
影响因子：
1.6
通讯作者：
Ying Wang;Juanjuan Kang;Ning Li;Yuwei Zhou;Zhongjie Tang;Bifang He;Jian Huang
中科院分区：
文献类型：
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作者：
Ying Wang;Juanjuan Kang;Ning Li;Yuwei Zhou;Zhongjie Tang;Bifang He;Jian Huang
背景神经肽是由神经肽前体经过一系列极其复杂的过程产生的一类生物活性肽，在许多方面介导神经元的调节。准确鉴定神经肽前体的切割位点对神经科学和脑科学的发展具有重要意义。目的随着神经肽前体数据的爆炸式增长，迫切需要发展快速有效的预测神经肽前体切割位点的生物信息学方法。方法我们首先将SwissProt和NueoPedia的神经肽前体数据处理成两组数据，训练数据集和测试数据集。随后，六个特征提取方案被应用到生成不同的特征集，然后使用特征选择方法来找到每个的最佳特征集。然后利用支持向量机对不同的特征类型进行建模。最后，利用独立的测试数据集对模型的性能进行了评价。结果通过支持向量机建立了6个模型。其中基于氨基酸组成的增强型模型在5折交叉验证中准确率最高，达到91.60%。当用独立的测试数据集进行评估时，它也表现出了优异的性能，准确率高达90.37%，接受者工作特征曲线下的面积高达0.9576。结论所建立的模型性能良好。此外，为了用户的方便，一个在线的Web服务器称为NeuroCS，这是免费提供的www.example.com。NeuroCS可以有效地预测神经肽前体的切割位点。
BACKGROUND Neuropeptides are a class of bioactive peptides produced from neuropeptide precursors through a series of extremely complex processes, mediating neuronal regulations in many aspects. Accurate identification of cleavage sites of neuropeptide precursors is of great significance for the development of neuroscience and brain science. OBJECTIVE With the explosive growth of neuropeptide precursor data, it is pretty much needed to develop bioinformatics methods for predicting neuropeptide precursors' cleavage sites quickly and efficiently. METHOD We started with processing the neuropeptide precursor data from SwissProt and NueoPedia into two sets of data, training dataset and testing dataset. Subsequently, six feature extraction schemes were applied to generate different feature sets and then feature selection methods were used to find the optimal feature set of each. Thereafter the support vector machine was utilized to build models for different feature types. Finally, the performance of models were evaluated with the independent testing dataset. RESULTS Six models are built through support vector machine. Among them the enhanced amino acid composition-based model reaches the highest accuracy of 91.60% in the 5-fold cross validation. When evaluated with independent testing dataset, it also showed an excellent performance with a high accuracy of 90.37% and Area under Receiver Operating Characteristic curve up to 0.9576. CONCLUSION The performance of the developed model was decent. Moreover, for users' convenience, an online web server called NeuroCS is built, which is freely available at http://i.uestc.edu.cn/NeuroCS/dist/index.html#/. NeuroCS can be used to predict neuropeptide precursors' cleavage sites effectively.