Center for Biomembrane Research, Department of Biochemistry and Biophysics, Stockholm Bioinformatics Center, SciLifeLab, Swedish E-science Research Center, Stockholm University, SE-10691 Stockholm, Sweden.
Bioinformatics. 2012 Feb 15;28(4):516-22. doi: 10.1093/bioinformatics/btr710. Epub 2012 Jan 13.
Transmembrane β barrel proteins (TMBs) are found in the outer membrane of Gram-negative bacteria, chloroplast and mitochondria. They play a major role in the translocation machinery, pore formation, membrane anchoring and ion exchange. TMBs are also promising targets for antimicrobial drugs and vaccines. Given the difficulty in membrane protein structure determination, computational methods to identify TMBs and predict the topology of TMBs are important.
Here, we present BOCTOPUS; an improved method for the topology prediction of TMBs by employing a combination of support vector machines (SVMs) and Hidden Markov Models (HMMs). The SVMs and HMMs account for local and global residue preferences, respectively. Based on a 10-fold cross-validation test, BOCTOPUS performs better than all existing methods, reaching a Q3 accuracy of 87%. Further, BOCTOPUS predicted the correct number of strands for 83% proteins in the dataset. BOCTOPUS might also help in reliable identification of TMBs by using it as an additional filter to methods specialized in this task.
BOCTOPUS is freely available as a web server at: http://boctopus.cbr.su.se/. The datasets used for training and evaluations are also available from this site.
跨膜β桶蛋白（TMBs）存在于革兰氏阴性菌的外膜、叶绿体和线粒体中。它们在易位子机制、孔形成、膜锚定和离子交换中起着重要作用。TMBs 也是有前途的抗菌药物和疫苗靶点。鉴于膜蛋白结构测定的困难，识别 TMBs 和预测 TMBs 拓扑结构的计算方法非常重要。
在这里，我们提出了 BOCTOPUS；一种通过结合支持向量机（SVMs）和隐马尔可夫模型（HMMs）来预测 TMB 拓扑结构的改进方法。SVMs 和 HMMs 分别用于局部和全局残基偏好。基于 10 倍交叉验证测试，BOCTOPUS 的性能优于所有现有的方法，达到了 Q3 准确度 87%。此外，BOCTOPUS 正确预测了数据集 83%蛋白质的链数。BOCTOPUS 也可以通过将其用作专门用于此任务的方法的附加过滤器，帮助可靠地识别 TMBs。
BOCTOPUS 可作为一个免费的网络服务器在：http://boctopus.cbr.su.se/ 使用。用于训练和评估的数据集也可以从该网站获得。
