Sequence-based prediction of type III secreted proteins.
Sequence-based prediction of type III secreted proteins.
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DOI：
10.1371/journal.ppat.1000376
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发表时间：
2009-04
期刊：
影响因子：
6.7
通讯作者：
Rattei T
中科院分区：
文献类型：
--
作者：
Arnold R;Brandmaier S;Kleine F;Tischler P;Heinz E;Behrens S;Niinikoski A;Mewes HW;Horn M;Rattei T
III型分泌系统(TTSS)是植物和动物包括人类在内的多种细菌病原体和共生体利用的宿主细胞相互作用的关键机制。TTSS代表一种分子注射器，细菌用它将效应蛋白直接输送到宿主细胞胞浆中。尽管TTSS在细菌发病机制中具有重要作用，但到目前为止，对III型分泌蛋白的识别和靶向还知之甚少。讨论了几个假设，包括基于mRNA的信号，伴侣介导的过程，或N-末端信号肽。在这项研究中，我们系统地分析了100个实验验证的效应蛋白的氨基酸组成和N-末端的二级结构。在此基础上，我们开发了一种机器学习方法来预测TTSS效应蛋白，考虑了N端序列特征，如氨基酸、短肽或具有特定理化性质的残基的频率。由此得到的计算模型显示，在N末端有一个很强的III型分泌信号，可以用来检测敏感性为∼71%，选择性为∼85%的效应器。这个信号在动物病原体和植物共生体中似乎是普遍的和保守的，因为如果各自的组被排除在训练之外，我们可以成功地检测到效应蛋白。将我们的预测方法应用于739个完整的细菌和古生菌基因组序列，结果识别出0%到12%的TTSS效应蛋白。将效应蛋白与TTSS不分泌的同源蛋白进行比较，没有发现融合获得信号的明显模式，这表明融合进化过程塑造了III型分泌信号。新开发的程序EffectiveT3(http://www.chlamydiaedb.org))是第一个用于识别新型TTSS效应器的通用计算机预测程序。我们的发现将有助于进一步研究和提高我们对III型分泌物及其在病原体-宿主相互作用中的作用的理解。许多革兰氏阴性细菌与人类、动物或植物密切相关。细菌和宿主之间的致病或共生相互作用通常是由细菌蛋白分泌到宿主细胞中来调节的。III型分泌系统(TTSS)是研究得最多的细胞机制之一，它能够特异性地识别和输出效应蛋白，并通过针状结构注入真核细胞。然而，到目前为止，对通过TTSS输出的蛋白质的运输机制和识别都还没有完全了解。在这项研究中，我们开发了第一个通用的计算模型，能够基于对TTSS效应蛋白的一小部分氨基酸序列的分析来识别TTSS效应蛋白。这个信号序列的特征在人和动物的病原体和植物共生体中是普遍的。基于我们的发现，我们开发了一个计算机程序，用于对TTSS效应候选进行电子预测；例如，在新的基因组中。TTSS及其效应蛋白构成了几种细菌病原体的中心毒力机制，这些病原体导致了人和动物的严重和广泛的传染病。我们的发现将促进和改进TTSS介导的致病机制及其在病原体-宿主相互作用中的作用的进一步研究。
The type III secretion system (TTSS) is a key mechanism for host cell interaction used by a variety of bacterial pathogens and symbionts of plants and animals including humans. The TTSS represents a molecular syringe with which the bacteria deliver effector proteins directly into the host cell cytosol. Despite the importance of the TTSS for bacterial pathogenesis, recognition and targeting of type III secreted proteins has up until now been poorly understood. Several hypotheses are discussed, including an mRNA-based signal, a chaperon-mediated process, or an N-terminal signal peptide. In this study, we systematically analyzed the amino acid composition and secondary structure of N-termini of 100 experimentally verified effector proteins. Based on this, we developed a machine-learning approach for the prediction of TTSS effector proteins, taking into account N-terminal sequence features such as frequencies of amino acids, short peptides, or residues with certain physico-chemical properties. The resulting computational model revealed a strong type III secretion signal in the N-terminus that can be used to detect effectors with sensitivity of ∼71% and selectivity of ∼85%. This signal seems to be taxonomically universal and conserved among animal pathogens and plant symbionts, since we could successfully detect effector proteins if the respective group was excluded from training. The application of our prediction approach to 739 complete bacterial and archaeal genome sequences resulted in the identification of between 0% and 12% putative TTSS effector proteins. Comparison of effector proteins with orthologs that are not secreted by the TTSS showed no clear pattern of signal acquisition by fusion, suggesting convergent evolutionary processes shaping the type III secretion signal. The newly developed program EffectiveT3 (http://www.chlamydiaedb.org) is the first universal in silico prediction program for the identification of novel TTSS effectors. Our findings will facilitate further studies on and improve our understanding of type III secretion and its role in pathogen–host interactions. Many Gram-negative bacteria live closely associated with humans, animals, or plants. The pathogenic or symbiotic interactions between bacteria and host are often mediated by the secretion of bacterial proteins into the host cells. The Type III secretion system (TTSS) is one of the best studied cellular machineries for this purpose and is able to specifically recognize and export effector proteins, which are injected into the eukaryotic cells through a needle-like structure. However, neither the mechanism of transport nor the recognition of proteins to be exported via the TTSS has so far been fully comprehended. In this study we have developed the first general computational model that is able to identify TTSS effector proteins based on the analysis of a short part of their amino acid sequences. The features of this signal sequence are universal among human and animal pathogens and plant symbionts. Based on our findings, we developed a computer program for the in silico prediction of TTSS effector candidates; for example, in new genomes. The TTSS and its effector proteins constitute a central virulence mechanism of several bacterial pathogens responsible for severe and widespread infectious diseases in humans and animals. Our findings will facilitate and improve further investigations of TTSS-mediated pathogenesis and its role in pathogen–host interactions.
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Pfam：氏族、网络工具和服务。
影响因子：
14.9
作者：
Finn, Robert D.;Mistry, Jaina;Schuster-Bockler, Benjamin;Griffiths-Jones, Sam;Hollich, Volker;Lassmann, Timo;Moxon, Simon;Marshall, Mhairi;Khanna, Ajay;Durbin, Richard;Eddy, Sean R.;Sonnhammer, Erik L. L.;Bateman, Alex
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Bateman, Alex
影响因子：
14.9
作者：
Kanehisa M;Araki M;Goto S;Hattori M;Hirakawa M;Itoh M;Katayama T;Kawashima S;Okuda S;Tokimatsu T;Yamanishi Y
通讯作者：
Yamanishi Y
影响因子：
9.2
作者：
Brumell, JH;Steele-Mortimer, O;Finlay, BB
通讯作者：
Finlay, BB
影响因子：
3.1
作者：
Hansen-Wester, I;Stecher, B;Hensel, M
通讯作者：
Hensel, M
影响因子：
2.9
作者：
Keerthi, SS;Shevade, SK;Murthy, KRK
通讯作者：
Murthy, KRK