基于神经网络的预测器对β-桶状膜蛋白跨膜区域的预测
Prediction of the transmembrane regions of beta-barrel membrane proteins with a neural network-based predictor.
作者信息
Jacoboni I, Martelli P L, Fariselli P, De Pinto V, Casadio R
机构信息
Laboratory of Biocomputing, Centro Interdipartimentale per le Ricerche Biotecnologiche (CIRB), Bologna, Italy.
出版信息
Protein Sci. 2001 Apr;10(4):779-87. doi: 10.1110/ps.37201.
Abstract
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.
摘要
一种基于神经网络的方法在一组已知原子分辨率的非冗余β-桶膜蛋白上进行训练和测试，采用留一法。当将进化信息用作网络输入时，该方法预测跨膜β链的拓扑结构，残基准确率高达78%。在训练集中包含的跨膜β链中，93%被正确分配。该预测器包括一种基于动态规划的模型优化算法，该算法能正确模拟训练/测试集中11种蛋白质中的8种。此外，蛋白质拓扑结构是根据模型中最长环的位置来确定的。我们提出这是一种填补β-桶膜蛋白预测空白的通用方法。
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