Predicting transmembrane beta-barrels in proteomes
Predicting transmembrane beta-barrels in proteomes
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DOI：
10.1093/nar/gkh580
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发表时间：
2004-04-01
影响因子：
14.9
通讯作者：
Rost, B
中科院分区：
文献类型：
--
作者：
Bigelow, HR;Petrey, DS;Rost, B
很少有方法解决直接从序列预测β-桶状膜蛋白的问题。一个原因是，到目前为止，只有很少的跨膜β-桶(TMB)蛋白的高分辨率结构被确定。在这里，我们介绍了一种新的基于轮廓的隐马尔可夫模型的设计、统计和结果，用于预测和识别TMBS。该方法谨慎地试图避免对稀疏的实验数据进行过拟合。虽然我们的模型训练和评分程序与最近发表的一项工作非常相似，但架构和基于结构的标签明显不同。特别是，我们引入了β-发夹基序的新定义，跨膜链的显式状态建模，以及对数赔率全蛋白识别分数。所得到的方法达到了总体四态(上、下链、周质、外环)高达86%的准确率。此外，准确地区分了TMB和非TMB蛋白(覆盖率为45%，准确率为100%)。这种高精度使其能够应用于72种完全测序的革兰氏阴性细菌。我们发现了超过164个以前未鉴定的TMB蛋白，其可信度很高。数据库搜索没有发现这些蛋白质中的任何一种与膜有关。我们质疑我们的164个预测中的绝大多数最终将得到实验验证。所有蛋白质组预测和Proftmb预测方法都可以在http://www.rostlab.org/服务/proftmb/上获得。
Very few methods address the problem of predicting beta-barrel membrane proteins directly from sequence. One reason is that only very few high-resolution structures for transmembrane beta-barrel (TMB) proteins have been determined thus far. Here we introduced the design, statistics and results of a novel profile-based hidden Markov model for the prediction and discrimination of TMBs. The method carefully attempts to avoid over-fitting the sparse experimental data. While our model training and scoring procedures were very similar to a recently published work, the architecture and structure-based labelling were significantly different. In particular, we introduced a new definition of beta- hairpin motifs, explicit state modelling of transmembrane strands, and a log-odds whole-protein discrimination score. The resulting method reached an overall four-state (up-, down-strand, periplasmic-, outer-loop) accuracy as high as 86%. Furthermore, accurately discriminated TMB from non-TMB proteins (45% coverage at 100% accuracy). This high precision enabled the application to 72 entirely sequenced Gram-negative bacteria. We found over 164 previously uncharacterized TMB proteins at high confidence. Database searches did not implicate any of these proteins with membranes. We challenge that the vast majority of our 164 predictions will eventually be verified experimentally. All proteome predictions and the PROFtmb prediction method are available at http://www.rostlab.org/ services/PROFtmb/.