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利用螺旋间相互作用改进跨膜蛋白共有拓扑结构预测

Improving transmembrane protein consensus topology prediction using inter-helical interaction.

作者信息

Wang Han, Zhang Chao, Shi Xiaohu, Zhang Li, Zhou You

机构信息

Jilin University, Changchun, China.

出版信息

Biochim Biophys Acta. 2012 Nov;1818(11):2679-86. doi: 10.1016/j.bbamem.2012.05.030. Epub 2012 Jun 6.

DOI:10.1016/j.bbamem.2012.05.030
PMID:22683598
Abstract

Alpha helix transmembrane proteins (αTMPs) represent roughly 30% of all open reading frames (ORFs) in a typical genome and are involved in many critical biological processes. Due to the special physicochemical properties, it is hard to crystallize and obtain high resolution structures experimentally, thus, sequence-based topology prediction is highly desirable for the study of transmembrane proteins (TMPs), both in structure prediction and function prediction. Various model-based topology prediction methods have been developed, but the accuracy of those individual predictors remain poor due to the limitation of the methods or the features they used. Thus, the consensus topology prediction method becomes practical for high accuracy applications by combining the advances of the individual predictors. Here, based on the observation that inter-helical interactions are commonly found within the transmembrane helixes (TMHs) and strongly indicate the existence of them, we present a novel consensus topology prediction method for αTMPs, CNTOP, which incorporates four top leading individual topology predictors, and further improves the prediction accuracy by using the predicted inter-helical interactions. The method achieved 87% prediction accuracy based on a benchmark dataset and 78% accuracy based on a non-redundant dataset which is composed of polytopic αTMPs. Our method derives the highest topology accuracy than any other individual predictors and consensus predictors, at the same time, the TMHs are more accurately predicted in their length and locations, where both the false positives (FPs) and the false negatives (FNs) decreased dramatically. The CNTOP is available at: http://ccst.jlu.edu.cn/JCSB/cntop/CNTOP.html.

摘要

α螺旋跨膜蛋白(αTMPs)约占典型基因组中所有开放阅读框(ORF)的30%,并参与许多关键的生物学过程。由于其特殊的物理化学性质,很难通过实验结晶并获得高分辨率结构,因此,基于序列的拓扑结构预测对于跨膜蛋白(TMPs)的研究非常必要,无论是在结构预测还是功能预测方面。已经开发了各种基于模型的拓扑结构预测方法,但由于方法本身或其使用的特征的局限性,这些单独预测器的准确性仍然很差。因此,通过结合各个预测器的优势,共识拓扑结构预测方法对于高精度应用变得切实可行。在此,基于观察到跨膜螺旋(TMHs)中普遍存在螺旋间相互作用并强烈表明其存在,我们提出了一种针对αTMPs的新型共识拓扑结构预测方法CNTOP,该方法整合了四个领先的单独拓扑结构预测器,并通过使用预测的螺旋间相互作用进一步提高了预测准确性。基于一个基准数据集,该方法实现了87%的预测准确率,基于一个由多位点αTMPs组成的非冗余数据集,准确率为78%。我们的方法比任何其他单独预测器和共识预测器都具有更高的拓扑结构准确率,同时,TMHs的长度和位置预测得更准确,其中假阳性(FPs)和假阴性(FNs)都大幅减少。CNTOP可在以下网址获取:http://ccst.jlu.edu.cn/JCSB/cntop/CNTOP.html。

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