Prediction of the transmembrane regions of β-barrel membrane proteins with a neural network-based predictor
Prediction of the transmembrane regions of β-barrel membrane proteins with a neural network-based predictor
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DOI：
10.1110/ps.37201
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发表时间：
2001-04-01
期刊：
影响因子：
8
通讯作者：
Casadio, R
中科院分区：
文献类型：
--
作者：
Jacoboni, I;Martelli, PL;Casadio, R
对基于神经网络的方法进行了基于神经网络的方法，并测试了一组不冗余的β-桶膜蛋白，并通过折刀程序在原子分辨率上已知。该方法预测，当将进化信息用作网络的输入时，跨膜β链的地形高达78％。在训练集中包含的跨膜β-链条中，正确分配了93％。该预测因子包括基于动态编程的模型优化算法，该算法正确对训练/测试集中存在的11种蛋白质中的8种建模。此外，蛋白质拓扑是根据模型中最长循环的位置分配的。我们将其作为填补β-贝尔贝尔膜蛋白预测空白的一般方法。
A method based on neural networks is trained and tested on a nonredundant set of beta -barrel membrane proteins known at atomic resolution with a jackknife procedure. The method predicts the topography of transmembrane beta strands with residue accuracy as high as 78% when evolutionary information is used as input to the network. Of the transmembrane beta -strands included in the training set, 93% are correctly assigned. The predictor includes an algorithm of model optimization, based on dynamic programming, that correctly models eight out of the 11 proteins present in the training/testing set. In addition, protein topology is assigned on the basis of the location of the longest loops in the models. We propose this as a general method to fill the gap of the prediction of beta -barrel membrane proteins.