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存在还是不存在:预测作为膜蛋白的可溶性SecA蛋白

To be or not to be: predicting soluble SecAs as membrane proteins.

作者信息

Hu Hae-Jin, Holley Jeanetta, He Jieyue, Harrison Robert W, Yang Hsiuchin, Tai Phang C, Pan Yi

机构信息

Molecular Basis of Disease Program, Georgia State University, Atlanta, GA 30303, USA.

出版信息

IEEE Trans Nanobioscience. 2007 Jun;6(2):168-79. doi: 10.1109/tnb.2007.897486.

Abstract

SecA is an important component of protein translocation in bacteria, and exists in soluble and membrane-integrated forms. Most membrane prediction programs predict SecA as being a soluble protein, with the exception of TMpred and Top-Pred. However, the membrane associated predicted segments by TMpred and TopPred are inconsistent across bacterial species in spite of high sequence homology. In this paper we describe a new method for membrane protein prediction, PSSM_SVM, which provides consistent results for integral membrane domains of SecAs across bacterial species. This PSSM encoding scheme demonstrates the highest accuracy in terms of Q2 among the common prediction methods, and produces consistent results on blind test data. None of the previously described methods showed this kind of consistency when tested against the same blind test set. This scheme predicts traditional transmembrane segments and most of the soluble proteins accurately. The PSSM scheme applied to the membrane-associated protein SecA shows characteristic features. In the set of 223 known SecA sequences, the PSSM_SVM prediction scheme predicts eight to nine residue embedded membrane segments. This predicted region is part of a 12 residue helix from known X-ray crystal structures of SecAs. This information could be important for determining the structure of SecA proteins in the membrane which have different conformational properties from other transmembrane proteins, as well as other soluble proteins that may similarly integrate into lipid bi-layers.

摘要

SecA是细菌中蛋白质转运的一个重要组成部分,以可溶性和膜整合形式存在。大多数膜预测程序预测SecA是一种可溶性蛋白质,但TMpred和Top-Pred除外。然而,尽管序列同源性很高,但TMpred和TopPred预测的膜相关片段在不同细菌物种之间并不一致。在本文中,我们描述了一种新的膜蛋白预测方法PSSM_SVM,它为不同细菌物种的SecA的完整膜结构域提供了一致的结果。这种PSSM编码方案在常见预测方法中Q2方面表现出最高的准确性,并在盲测数据上产生了一致的结果。当针对相同的盲测集进行测试时,之前描述的方法都没有表现出这种一致性。该方案能够准确预测传统的跨膜片段和大多数可溶性蛋白质。应用于膜相关蛋白SecA的PSSM方案显示出特征性特点。在223个已知的SecA序列集中,PSSM_SVM预测方案预测出8到9个残基的嵌入膜片段。这个预测区域是来自已知SecA的X射线晶体结构的12个残基螺旋的一部分。这些信息对于确定膜中SecA蛋白的结构可能很重要,SecA蛋白具有与其他跨膜蛋白不同的构象特性,以及可能类似地整合到脂质双分子层中的其他可溶性蛋白。

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