A Hidden Markov Model method, capable of predicting and discriminating beta-barrel outer membrane proteins.
A Hidden Markov Model method, capable of predicting and discriminating beta-barrel outer membrane proteins.
复制标题
DOI：
10.1186/1471-2105-5-29
复制
发表时间：
2004-03-15
影响因子：
3
通讯作者：
Hamodrakas SJ
中科院分区：
文献类型：
--
作者：
Bagos PG;Liakopoulos TD;Spyropoulos IC;Hamodrakas SJ
在完全测序的基因组中，整合膜蛋白约占所有蛋白质的 20-30%。它们有两种结构类别：α-螺旋膜蛋白和β-桶膜蛋白，表现出不同的理化特征、结构和定位。虽然目前 α 螺旋整合膜蛋白的跨膜片段预测似乎是一项简单的任务，但对于 β 桶膜蛋白来说，跨膜片段预测要困难得多。我们开发了一种基于隐马尔可夫模型的方法，能够预测革兰氏阴性细菌外膜蛋白的跨膜 β 链，并在大型数据集中区分这些蛋白和水溶性蛋白。该模型以判别性方式进行训练，旨在最大化正确预测的概率而不是序列的可能性。训练是在一个包含 14 种外膜蛋白的非冗余数据库上进行的，这些蛋白的结构在原子分辨率下是已知的；它已经用刀刀程序进行了测试，每个残留物的准确度为 84.2%，相关系数为 0.72，而对于自我一致性测试，每个残留物的准确度为 88.1%，相关系数为 0.824。在自一致性测试中，正确预测的拓扑总数为 14 个中的 10 个，而在 Jacknife 测试中，正确预测的拓扑总数为 14 个中的 9 个。此外，该模型能够在大规模应用中区分外膜和水溶性蛋白，外膜和水溶性蛋白正确分类的成功率分别为88.8%和89.2%，为文献中最高的分类成功率。该测试是在一组已知的外膜蛋白上独立进行的，这些外膜蛋白彼此之间以及与训练集的蛋白质之间的序列同一性较低。基于上述，我们制定了一种策略，使我们能够筛选大肠杆菌整个蛋白质组的外膜蛋白。结果令人满意，因此这里提出的方法似乎适合筛选整个蛋白质组以发现新型外膜蛋白。可供非商业用户使用的 Web 界面位于： ，它是唯一免费提供的基于 HMM 的 β-桶外膜蛋白拓扑预测器。
Integral membrane proteins constitute about 20–30% of all proteins in the fully sequenced genomes. They come in two structural classes, the α-helical and the β-barrel membrane proteins, demonstrating different physicochemical characteristics, structure and localization. While transmembrane segment prediction for the α-helical integral membrane proteins appears to be an easy task nowadays, the same is much more difficult for the β-barrel membrane proteins. We developed a method, based on a Hidden Markov Model, capable of predicting the transmembrane β-strands of the outer membrane proteins of gram-negative bacteria, and discriminating those from water-soluble proteins in large datasets. The model is trained in a discriminative manner, aiming at maximizing the probability of correct predictions rather than the likelihood of the sequences. The training has been performed on a non-redundant database of 14 outer membrane proteins with structures known at atomic resolution; it has been tested with a jacknife procedure, yielding a per residue accuracy of 84.2% and a correlation coefficient of 0.72, whereas for the self-consistency test the per residue accuracy was 88.1% and the correlation coefficient 0.824. The total number of correctly predicted topologies is 10 out of 14 in the self-consistency test, and 9 out of 14 in the jacknife. Furthermore, the model is capable of discriminating outer membrane from water-soluble proteins in large-scale applications, with a success rate of 88.8% and 89.2% for the correct classification of outer membrane and water-soluble proteins respectively, the highest rates obtained in the literature. That test has been performed independently on a set of known outer membrane proteins with low sequence identity with each other and also with the proteins of the training set. Based on the above, we developed a strategy, that enabled us to screen the entire proteome of E. coli for outer membrane proteins. The results were satisfactory, thus the method presented here appears to be suitable for screening entire proteomes for the discovery of novel outer membrane proteins. A web interface available for non-commercial users is located at: , and it is the only freely available HMM-based predictor for β-barrel outer membrane protein topology.
登录
查看更多内容
影响因子：
2.9
作者：
Pasquier, C;Promponas, VJ;Hamodrakas, SJ
通讯作者：
Hamodrakas, SJ
影响因子：
8
作者：
Diederichs, K;Freigang, J;Breed, J
通讯作者：
Breed, J
影响因子：
8
作者：
Jacoboni, I;Martelli, PL;Casadio, R
通讯作者：
Casadio, R
影响因子：
3.1
作者：
Liu, Q;Zhu, YS;Li, YX
通讯作者：
Li, YX
影响因子：
3
作者：
Gouaux, E
通讯作者：
Gouaux, E