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Cascading classifier application for topology prediction of TMB proteins

机译:级联分类器在TMB蛋白质拓扑预测中的应用

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This paper is concerned with the use of a cascading classifier for trans-membrane beta-barrel topology prediction analysis. Most of novel drug design requires the use of membrane proteins. Trans-membrane proteins have key roles such as active transport across the membrane and signal transduction among other functions. Given their key roles, understanding their structures mechanisms and regulation at the level of molecules with the use of computational modeling is essential. In the field of bioinformatics, many years have been spent on the trans-membrane protein structure prediction focusing on the alpha-helix membrane proteins. Technological developments have been increasingly utilized in order to understand in more details membrane protein function and structure. Various methodologies have been developed for the prediction of TMB (trans-membrane beta-barrel) proteins topology however the use of cascading classifier has not been fully explored. This research presents a novel approach for TMB topology prediction. The MATLAB computer simulation results show that the proposed methodology predicts trans-membrane topologies with high accuracy for randomly selected proteins.
机译:本文涉及级联分类器在跨膜β-桶形拓扑预测分析中的使用。大多数新药设计都需要使用膜蛋白。跨膜蛋白具有关键作用,例如跨膜的主动转运和信号转导等功能。考虑到它们的关键作用,使用计算模型了解它们在分子水平上的结构机理和调控是必不可少的。在生物信息学领域,多年来一直致力于跨膜蛋白结构预测,重点是α-螺旋膜蛋白。为了更详细地了解膜蛋白的功能和结构,越来越多地利用技术发展。已经开发了各种方法来预测TMB(跨膜β-桶)蛋白质拓扑,但是尚未充分探索级联分类器的用途。这项研究提出了一种TMB拓扑预测的新方法。 MATLAB计算机仿真结果表明,所提出的方法可以对随机选择的蛋白质进行高精度的跨膜拓扑预测。

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