Martelli Pier Luigi, Fariselli Piero, Tasco Gianluca, Casadio Rita
Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, via Irnerio 42, Bologna 40126, Italy.
Comp Funct Genomics. 2003;4(4):406-9. doi: 10.1002/cfg.308.
New methods, essentially based on hidden Markov models (HMM) and neural networks (NN), can predict the topography of both beta-barrel and all-alpha membrane proteins with high accuracy and a low rate of false positives and false negatives. These methods have been integrated in a suite of programs to filter proteomes of Gram-negative bacteria, searching for new membrane proteins.
新方法主要基于隐马尔可夫模型（HMM）和神经网络（NN），能够高精度地预测β-桶状膜蛋白和全α螺旋膜蛋白的拓扑结构，且假阳性和假阴性率较低。这些方法已被整合到一套程序中，用于筛选革兰氏阴性菌的蛋白质组，以寻找新的膜蛋白。
