Membrane proteins are a major focus for new drug discovery. Transmembrane beta-barrel (TMB) proteins play key roles in the translocation machinery, pore formation, membrane anchoring and ion exchange. Given their key roles and the difficulty in membrane protein structure determination, the use of computational modeling is essential. This paper focuses on the topology prediction of TMB proteins. In the field of bioinformatics, many years of research has been spent on the topology prediction of transmembrane alpha-helices. The efforts to TMB proteins topology prediction have been overshadowed and the prediction accuracy could be improved with further research. Various methodologies have been developed in the past for the prediction of TMB protein topology, however, the use of cascading classifier has never been fully explored. This research presents a novel approach to TMB topology prediction with the use of a cascading classifier. The MATLAB computer simulation results show that the proposed methodology predicts TMB proteins topologies with high accuracy for randomly selected proteins. By using the cascading classifier approach, the best overall accuracy is 76.3% with a precision of 0.831 and recall or probability of detection of 0.799 for TMB topology prediction. The accuracy of 76.3% is achieved using a two-layers cascading classifier.

中文翻译：

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跨膜β-桶蛋白拓扑预测的级联分类器应用

膜蛋白是新药发现的主要焦点。跨膜 β-桶 (TMB) 蛋白在易位机制、孔形成、膜锚定和离子交换中起关键作用。鉴于它们的关键作用和膜蛋白结构测定的困难，使用计算建模是必不可少的。本文重点研究 TMB 蛋白的拓扑结构预测。在生物信息学领域，多年来一直致力于跨膜α-螺旋的拓扑结构预测。TMB蛋白质拓扑预测的努力已经黯然失色，预测准确性可以通过进一步的研究来提高。过去已经开发了各种方法来预测 TMB 蛋白质拓扑结构，但是，级联分类器的使用从未得到充分探索。本研究提出了一种使用级联分类器进行 TMB 拓扑预测的新方法。MATLAB 计算机模拟结果表明，所提出的方法可以高精度地预测随机选择的蛋白质的 TMB 蛋白质拓扑结构。通过使用级联分类器方法，TMB 拓扑预测的最佳整体准确率为 76.3%，精度为 0.831，召回或检测概率为 0.799。使用两层级联分类器实现了 76.3% 的准确率。3%，精度为 0.831，召回率或检测概率为 0.799，用于 TMB 拓扑预测。使用两层级联分类器实现了 76.3% 的准确率。3%，精度为 0.831，召回率或检测概率为 0.799，用于 TMB 拓扑预测。使用两层级联分类器实现了 76.3% 的准确率。