Tsaousis Georgios N, Hamodrakas Stavros J, Bagos Pantelis G
Department of Cell Biology and Biophysics, Faculty of Biology, National and Kapodistrian University of Athens, Panepistimiopolis, Athens, 15701, Greece.
Department of Computer Science and Biomedical Informatics, University of Thessaly, Papasiopoulou 2-4, Lamia, 35100, Greece.
Methods Mol Biol. 2017;1552:43-61. doi: 10.1007/978-1-4939-6753-7_4.
Transmembrane beta-barrels (TMBBs) constitute an important structural class of membrane proteins located in the outer membrane of gram-negative bacteria, and in the outer membrane of chloroplasts and mitochondria. They are involved in a wide variety of cellular functions and the prediction of their transmembrane topology, as well as their discrimination in newly sequenced genomes is of great importance as they are promising targets for antimicrobial drugs and vaccines. Several methods have been applied for the prediction of the transmembrane segments and the topology of beta barrel transmembrane proteins utilizing different algorithmic techniques. Hidden Markov Models (HMMs) have been efficiently used in the development of several computational methods used for this task. In this chapter we give a brief review of different available prediction methods for beta barrel transmembrane proteins pointing out sequence and structural features that should be incorporated in a prediction method. We then describe the procedure of the design and development of a Hidden Markov Model capable of predicting the transmembrane beta strands of TMBBs and discriminating them from globular proteins.
跨膜β桶（TMBBs）是一类重要的膜蛋白结构，存在于革兰氏阴性菌的外膜、叶绿体和线粒体的外膜中。它们参与多种细胞功能，预测其跨膜拓扑结构以及在新测序基因组中对它们进行识别非常重要，因为它们是抗菌药物和疫苗的潜在靶点。已经应用了几种方法，利用不同的算法技术来预测β桶跨膜蛋白的跨膜片段和拓扑结构。隐马尔可夫模型（HMMs）已被有效地用于开发用于此任务的几种计算方法。在本章中，我们简要回顾了针对β桶跨膜蛋白的不同可用预测方法，指出了预测方法中应纳入的序列和结构特征。然后，我们描述了一种隐马尔可夫模型的设计和开发过程，该模型能够预测TMBBs的跨膜β链并将它们与球状蛋白区分开来。
