Improved prediction of signal peptides: SignalP 3.0
Improved prediction of signal peptides: SignalP 3.0
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DOI：
10.1016/j.jmb.2004.05.028
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发表时间：
2004-07-16
影响因子：
5.6
通讯作者：
Brunak, S
中科院分区：
文献类型：
--
作者：
Bendtsen, JD;Nielsen, H;Brunak, S
我们描述了对目前最流行的经典分泌蛋白预测方法SignalP的改进。SignalP由基于神经网络和隐马尔可夫模型算法的两种不同预测器组成，这两个组件都已更新。基于信号肽的切割位点位置和氨基酸组成相关的想法，新的特征已被作为输入纳入神经网络。这种添加，再结合对一个新数据集的彻底纠错，使得预测器的性能相比SignalP 2.0版本有了显著提高。在3.0版本中，对于真核生物、革兰氏阴性菌和革兰氏阳性菌这三种生物类别，切割位点预测的准确性都有显著提高。切割位点预测的准确性比之前的版本提高了6 - 17%，而信号肽区分度的提高主要是由于消除了假阳性预测，以及为神经网络引入了一种新的区分分数。这种新方法已经与其他可用方法进行了基准测试。可以在公开的网络服务器http://www.cbs.dtu.dk/services/SignaIP/上进行预测。（C）2004 Elsevier Ltd. 保留所有权利。
We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea that the cleavage site position and the amino acid composition of the signal peptide are correlated, new features have been included as input to the neural network. This addition, combined with a thorough error-correction of a new data set, have improved the performance of the predictor significantly over SignalP version 2. In version 3, correctness of the cleavage site predictions has increased notably for all three organism groups, eukaryotes, Gram-negative and Gram-positive bacteria. The accuracy of cleavage site prediction has increased in the range 6-17% over the previous version, whereas the signal peptide discrimination improvement is mainly due to the elimination of false-positive predictions, as well as the introduction of a new discrimination score for the neural network. The new method has been benchmarked against other available methods. Predictions can be made at the publicly available web server http://www.cbs.dtu.dk/services/SignaIP/ (C) 2004 Elsevier Ltd. All rights reserved.