Prediction of signal peptides using scaled window
Prediction of signal peptides using scaled window
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DOI：
10.1016/s0196-9781(01)00540-x
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发表时间：
2001-12-01
期刊：
影响因子：
3
通讯作者：
Chou, KC
中科院分区：
文献类型：
--
作者：
Chou, KC
细胞使用邮政编码系统对新合成的蛋白质进行分类，并将它们送到需要的地方：进入称为细胞器的不同内部隔室，甚至完全离开细胞。邮政编码系统最重要的特征之一是信号序列或“地址标签”，它最初存在于蛋白质的n端部分，并在分泌时被修剪掉。由于信号肽在理解遗传疾病的分子机制、基因治疗的细胞重编程以及构建纠正特定缺陷的药物方面的重要性，开发一种快速准确的方法来识别信号肽是非常必要的。本文提出了一种尺度窗口模型。在此基础上，结合马尔可夫链理论，提出了一种新的信号肽预测算法。对1939种分泌蛋白和1440种非分泌蛋白的测试结果表明，新算法在总体成功率上特别成功，因此可以作为现有信号肽预测算法的补充工具。(C) 2001爱思唯尔科学公司版权所有。
Cells use a ZIP code system to sort newly synthesized proteins and deliver them wherever they are needed: into different internal compartments called organelles or even out of the cell altogether. One of the most essential features of the ZIP code system is the signal sequence or "address tag," which is originally present in the N-terminal part of the protein and is trimmed away by the time it is secreted. Owing to the importance of signal peptides for understanding the molecular mechanisms of genetic diseases, reprogramming cells for gene therapy, and constructing now drugs for correcting a specific defect, it is highly desirable to develop a fast and accurate method to identify the signal peptides. In this paper, a scaled window model is proposed. Based on such a model as well as Markov chain theory, a new algorithm is formulated for predicting the signal peptides. Test results for the 1939 secretory proteins and 1440 non-secretary proteins have indicated that the new algorithm is particularly successful in the overall success rate, and hence can serve as a complementary tool to the existing algorithms for signal peptide prediction. (C) 2001 Elsevier Science Inc. All rights reserved.