Prediction of disulfide-bonded cysteines in proteomes with a hidden neural network
Prediction of disulfide-bonded cysteines in proteomes with a hidden neural network
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DOI：
10.1002/pmic.200300745
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发表时间：
2004-06-01
期刊：
影响因子：
3.4
通讯作者：
Casadio, R
中科院分区：
文献类型：
--
作者：
Martelli, PL;Fariselli, P;Casadio, R
基于隐藏神经网络的方法用于从蛋白质链的残基序列开始预测半胱氨酸的键合状态。该方法的每个半胱氨酸残基和每个蛋白质的得分分别高达 89% 和 86%，这克服了同一类别的其他预测因子。然后，我们探讨了预测器在计算大肠杆菌（K12 和 O157）、Aeropirum pernix、Thermotoga maritima 和智人整个蛋白质组的二硫键含量方面的功效。我们发现细胞外含二硫键的蛋白质比例高于细胞内二硫键的比例，并且人类蛋白质组是迄今为止蛋白质中硫-硫键含量最高的蛋白质组。
A hidden neural network-based method is used to predict the bonding state of cysteines starting from the residue sequence of the protein chain. The method scores as high as 89% and 86% per cysteine residue and per protein, respectively, and in this overcomes other predictors of the same category. We then explore the efficacy of our predictor in computing the disulfide content of the whole proteome of Escherichia coli (K12 and O157), Aeropirum pernix, Thermotoga maritima, and Homo sapiens. We find that the percentage of extracellular disulfide containing proteins is higher than that of intracellular one, and that the human proteome is by far the one with the highest content of sulfur-sulfur linkages in proteins.