OCTOPUS: improving topology prediction by two-track ANN-based preference scores and an extended topological grammar
OCTOPUS: improving topology prediction by two-track ANN-based preference scores and an extended topological grammar
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DOI：
10.1093/bioinformatics/btn221
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发表时间：
2008-08-01
期刊：
影响因子：
5.8
通讯作者：
Elofsson, Arne
中科院分区：
文献类型：
--
作者：
Viklund, Hakan;Elofsson, Arne
动机：由于-螺旋跨膜蛋白构成了典型基因组的大约25个，并且是许多基本生物过程的重要部分，这些蛋白的结构知识对于增加我们对这些过程的理解是必要的。由于跨膜蛋白的结构知识是很难达到实验，改进的方法预测这些蛋白质的结构特征是important.Results：OCTOPUS，一种新的方法预测跨膜蛋白的拓扑结构，并基准使用的数据集的124个序列与已知结构。使用隐马尔可夫模型和人工神经网络的新组合，OCTOPUS预测了94个序列的正确拓扑结构。特别是，OCTOPUS是第一个拓扑预测器，完全集成了拓扑语法中的折返/膜浸渍区域和跨膜发夹的建模。
Motivation: As -helical transmembrane proteins constitute roughly 25 of a typical genome and are vital parts of many essential biological processes, structural knowledge of these proteins is necessary for increasing our understanding of such processes. Because structural knowledge of transmembrane proteins is difficult to attain experimentally, improved methods for prediction of structural features of these proteins are important.Results: OCTOPUS, a new method for predicting transmembrane protein topology is presented and benchmarked using a dataset of 124 sequences with known structures. Using a novel combination of hidden Markov models and artificial neural networks, OCTOPUS predicts the correct topology for 94 of the sequences. In particular, OCTOPUS is the first topology predictor to fully integrate modeling of reentrant/membrane-dipping regions and transmembrane hairpins in the topological grammar.