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Reverse vaccinology (RV) has been widely used for screening of surface-exposed proteins (PSEs) of important pathogens, including outer membrane proteins (OMPs), and extracellular proteins (ECPs) as potential vaccine candidates. In this study, we applied a novel RV negative strategy and a pan-genome analysis for screening of PSEs from 17 L. interrogans strains covering 11 predominately epidemic serovars and 17 multilocus typing (MLST) sequence types (STs) worldwide. Our results showed, for instance, out of a total of 633 predicted PSEs in strain 56601, 92.8% were OMPs or ECPs (588/633). Among the 17 strains, 190 core PSEs, 913 dispensable PSEs and 861 unique PSEs were identified. Of the 190 PSEs, 121 were further predicted to be highly antigenic and thus may serve as potential vaccine candidates against leptospirosis. With the exception of LipL45, OmpL1, and LigB, the majority of the 121 PSEs were newly identified antigens. For example, hypothetical proteins BatC, LipL71, and the OmpA family proteins sharing many common features, such as surface-exposed localization, universal conservation, and eliciting strong antibody responses in patients, are regarded as the most promising vaccine antigens. Additionally, a wide array of potential virulence factors among the predicted PSEs including TonB-dependent receptor, sphingomyelinase 2, leucine-rich repeat protein, and 4 neighboring hypothetical proteins were identified as potential antigenicity, and deserve further investigation. Our results can contribute to the prediction of suitable antigens as potential vaccine candidates against leptospirosis and also provide further insights into mechanisms of leptospiral pathogenicity. In addition, our novel negative-screening strategy combined with pan-genome analysis can be a routine RV method applied to numerous other pathogens.
Pubmed ID: 28352257
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A Bioinformatics Resource Center bacterial bioinformatics database and analysis resource that provides researchers with an online resource that stores and integrates a variety of data types (e.g. genomics, transcriptomics, protein-protein interactions (PPIs), three-dimensional protein structures and sequence typing data) and associated metadata. Datatypes are summarized for individual genomes and across taxonomic levels. All genomes, currently more than 10 000, are consistently annotated using RAST, the Rapid Annotations using Subsystems Technology. Summaries of different data types are also provided for individual genes, where comparisons of different annotations are available, and also include available transcriptomic data. PATRIC provides a variety of ways for researchers to find data of interest and a private workspace where they can store both genomic and gene associations, and their own private data. Both private and public data can be analyzed together using a suite of tools to perform comparative genomic or transcriptomic analysis. PATRIC also includes integrated information related to disease and PPIs. The PATRIC project includes three primary collaborators: the University of Chicago, the University of Manchester, and New City Media. The University of Chicago is providing genome annotations and a PATRIC end-user genome annotation service using their Rapid Annotation using Subsystem Technology (RAST) system. The National Centre for Text Mining (NaCTeM) at the University of Manchester is providing literature-based text mining capability and service. New City Media is providing assistance in website interface development. An FTP server and download tool are available.
View all literature mentionsHarvester is a Web-based tool that bulk-collects bioinformatic data on human proteins from various databases and prediction servers. It is a meta search engine for gene and protein information. It searches 16 major databases and prediction servers and combines the results on pregenerated HTML pages. In this way Harvester can provide comprehensive gene-protein information from different servers in a convenient and fast manner. As full text meta search engine, similar to Google trade mark, Harvester allows screening of the whole genome proteome for current protein functions and predictions in a few seconds. With Harvester it is now possible to compare and check the quality of different database entries and prediction algorithms on a single page. Sponsors: This work has been supported by the BMBF with grants 01GR0101 and 01KW0013.
View all literature mentionsA subCELlular LOcalization predictor based on a multi-class support vector machine (SVM) classification system. CELLO uses 4 types of sequence coding schemes: the amino acid composition, the di-peptide composition, the partitioned amino acid composition and the sequence composition based on the physico-chemical properties of amino acids. They combine votes from these classifiers and use the jury votes to determine the final assignment.
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