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BEAN 2.0: an integrated web resource for the identification and functional analysis of type III secreted effectors.

ABSTRACT: Gram-negative pathogenic bacteria inject type III secreted effectors (T3SEs) into host cells to sabotage their immune signaling networks. Because T3SEs constitute a meeting-point of pathogen virulence and host defense, they are of keen interest to host-pathogen interaction research community. To accelerate the identification and functional understanding of T3SEs, we present BEAN 2.0 as an integrated web resource to predict, analyse and store T3SEs. BEAN 2.0 includes three major components. First, it provides an accurate T3SE predictor based on a hybrid approach. Using independent testing data, we show that BEAN 2.0 achieves a sensitivity of 86.05% and a specificity of 100%. Second, it integrates a set of online sequence analysis tools. Users can further perform functional analysis of putative T3SEs in a seamless way, such as subcellular location prediction, functional domain scan and disorder region annotation. Third, it compiles a database covering 1215 experimentally verified T3SEs and constructs two T3SE-related networks that can be used to explore the relationships among T3SEs. Taken together, by presenting a one-stop T3SE bioinformatics resource, we hope BEAN 2.0 can promote comprehensive understanding of the function and evolution of T3SEs.

SUBMITTER: Dong X

PROVIDER: S-EPMC4483310 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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BEAN 2.0: an integrated web resource for the identification and functional analysis of type III secreted effectors.

Dong Xiaobao X Lu Xiaotian X Zhang Ziding Z

Database : the journal of biological databases and curation 20150627

Gram-negative pathogenic bacteria inject type III secreted effectors (T3SEs) into host cells to sabotage their immune signaling networks. Because T3SEs constitute a meeting-point of pathogen virulence and host defense, they are of keen interest to host-pathogen interaction research community. To accelerate the identification and functional understanding of T3SEs, we present BEAN 2.0 as an integrated web resource to predict, analyse and store T3SEs. BEAN 2.0 includes three major components. First ...[more]

PMID: 26120140

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Project description:BackgroundPlant parasitic nematodes develop an intimate and long-term feeding relationship with their host plants. They induce a multi-nucleate feeding site close to the vascular bundle in the roots of their host plant and remain sessile for the rest of their life. Nematode secretions, produced in the oesophageal glands and secreted through a hollow stylet into the host plant cytoplasm, are believed to play key role in pathogenesis. To combat these persistent pathogens, the identity and functional analysis of secreted effectors can serve as a key to devise durable control measures. In this review, we will recapitulate the knowledge over the identification and functional characterization of secreted nematode effector repertoire from phytoparasitic nematodes.ResearchDespite considerable efforts, the identity of genes encoding nematode secreted proteins has long been severely hampered because of their microscopic size, long generation time and obligate biotrophic nature. The methodologies such as bioinformatics, protein structure modeling, in situ hybridization microscopy, and protein-protein interaction have been used to identify and to attribute functions to the effectors. In addition, RNA interference (RNAi) has been instrumental to decipher the role of the genes encoding secreted effectors necessary for parasitism and genes attributed to normal development. Recent comparative and functional genomic approaches have accelerated the identification of effectors from phytoparasitic nematodes and offers opportunities to control these pathogens.ConclusionPlant parasitic nematodes pose a serious threat to global food security of various economically important crops. There is a wealth of genomic and transcriptomic information available on plant parasitic nematodes and comparative genomics has identified many effectors. Bioengineering crops with dsRNA of phytonematode genes can disrupt the life cycle of parasitic nematodes and therefore holds great promise to develop resistant crops against plant-parasitic nematodes.

Project description:BackgroundType III secretion systems (T3SSs) are central to the pathogenesis and specifically deliver their secreted substrates (type III secreted proteins, T3SPs) into host cells. Since T3SPs play a crucial role in pathogen-host interactions, identifying them is crucial to our understanding of the pathogenic mechanisms of T3SSs. This study reports a novel and effective method for identifying the distinctive residues which are conserved different from other SPs for T3SPs prediction. Moreover, the importance of several sequence features was evaluated and further, a promising prediction model was constructed.ResultsBased on the conservation profiles constructed by a position-specific scoring matrix (PSSM), 52 distinctive residues were identified. To our knowledge, this is the first attempt to identify the distinct residues of T3SPs. Of the 52 distinct residues, the first 30 amino acid residues are all included, which is consistent with previous studies reporting that the secretion signal generally occurs within the first 30 residue positions. However, the remaining 22 positions span residues 30-100 were also proven by our method to contain important signal information for T3SP secretion because the translocation of many effectors also depends on the chaperone-binding residues that follow the secretion signal. For further feature optimisation and compression, permutation importance analysis was conducted to select 62 optimal sequence features. A prediction model across 16 species was developed using random forest to classify T3SPs and non-T3 SPs, with high receiver operating curve of 0.93 in the 10-fold cross validation and an accuracy of 94.29% for the test set. Moreover, when performing on a common independent dataset, the results demonstrate that our method outperforms all the others published to date. Finally, the novel, experimentally confirmed T3 effectors were used to further demonstrate the model's correct application. The model and all data used in this paper are freely available at http://cic.scu.edu.cn/bioinformatics/T3SPs.zip.

Project description:MotivationType III secreted effectors (T3SEs) can be injected into host cell cytoplasm via type III secretion systems (T3SSs) to modulate interactions between Gram-negative bacterial pathogens and their hosts. Due to their relevance in pathogen-host interactions, significant computational efforts have been put toward identification of T3SEs and these in turn have stimulated new T3SE discoveries. However, as T3SEs with new characteristics are discovered, these existing computational tools reveal important limitations: (i) most of the trained machine learning models are based on the N-terminus (or incorporating also the C-terminus) instead of the proteins' complete sequences, and (ii) the underlying models (trained with classic algorithms) employed only few features, most of which were extracted based on sequence-information alone. To achieve better T3SE prediction, we must identify more powerful, informative features and investigate how to effectively integrate these into a comprehensive model.ResultsIn this work, we present Bastion3, a two-layer ensemble predictor developed to accurately identify type III secreted effectors from protein sequence data. In contrast with existing methods that employ single models with few features, Bastion3 explores a wide range of features, from various types, trains single models based on these features and finally integrates these models through ensemble learning. We trained the models using a new gradient boosting machine, LightGBM and further boosted the models' performances through a novel genetic algorithm (GA) based two-step parameter optimization strategy. Our benchmark test demonstrates that Bastion3 achieves a much better performance compared to commonly used methods, with an ACC value of 0.959, F-value of 0.958, MCC value of 0.917 and AUC value of 0.956, which comprehensively outperformed all other toolkits by more than 5.6% in ACC value, 5.7% in F-value, 12.4% in MCC value and 5.8% in AUC value. Based on our proposed two-layer ensemble model, we further developed a user-friendly online toolkit, maximizing convenience for experimental scientists toward T3SE prediction. With its design to ease future discoveries of novel T3SEs and improved performance, Bastion3 is poised to become a widely used, state-of-the-art toolkit for T3SE prediction.Availability and implementationhttp://bastion3.erc.monash.edu/.Contactselkrig@embl.de or wyztli@163.com or or trevor.lithgow@monash.edu.Supplementary informationSupplementary data are available at Bioinformatics online.

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BEAN 2.0: an integrated web resource for the identification and functional analysis of type III secreted effectors.

Publications

BEAN 2.0: an integrated web resource for the identification and functional analysis of type III secreted effectors.

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