55
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Discovery of Novel Leptospirosis Vaccine Candidates Using Reverse and Structural Vaccinology

      research-article

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Leptospira spp. are diderm (two membranes) bacteria that infect mammals causing leptospirosis, a public health problem with global implications. Thousands of people die every year due to leptospirosis, especially in developing countries with tropical climates. Prophylaxis is difficult due to multiple factors, including the large number of asymptomatic hosts that transmit the bacteria, poor sanitation, increasing numbers of slum dwellers, and the lack of an effective vaccine. Several leptospiral recombinant antigens were evaluated as a replacement for the inactivated (bacterin) vaccine; however, success has been limited. A prospective vaccine candidate is likely to be a surface-related protein that can stimulate the host immune response to clear leptospires from blood and organs. In this study, a comprehensive bioinformatics approach based on reverse and structural vaccinology was applied toward the discovery of novel leptospiral vaccine candidates. The Leptospira interrogans serovar Copenhageni strain L1-130 genome was mined in silico for the enhanced identification of conserved β-barrel (βb) transmembrane proteins and outer membrane (OM) lipoproteins. Orthologs of the prospective vaccine candidates were screened in the genomes of 20 additional Leptospira spp. Three-dimensional structural models, with a high degree of confidence, were created for each of the surface-exposed proteins. Major histocompatibility complex II (MHC-II) epitopes were identified, and their locations were mapped on the structural models. A total of 18 βb transmembrane proteins and 8 OM lipoproteins were identified. These proteins were conserved among the pathogenic Leptospira spp. and were predicted to have epitopes for several variants of MHC-II receptors. A structural and functional analysis of the sequence of these surface proteins demonstrated that most βb transmembrane proteins seem to be TonB-dependent receptors associated with transportation. Other proteins identified included, e.g., TolC efflux pump proteins, a BamA-like OM component of the βb transmembrane protein assembly machinery, and the LptD-like LPS assembly protein. The structural mapping of the immunodominant epitopes identified the location of conserved, surface-exposed, immunogenic regions for each vaccine candidate. The proteins identified in this study are currently being evaluated for experimental evidence for their involvement in virulence, disease pathogenesis, and physiology, in addition to vaccine development.

          Related collections

          Most cited references92

          • Record: found
          • Abstract: found
          • Article: not found

          Prediction of protein subcellular localization.

          Because the protein's function is usually related to its subcellular localization, the ability to predict subcellular localization directly from protein sequences will be useful for inferring protein functions. Recent years have seen a surging interest in the development of novel computational tools to predict subcellular localization. At present, these approaches, based on a wide range of algorithms, have achieved varying degrees of success for specific organisms and for certain localization categories. A number of authors have noticed that sequence similarity is useful in predicting subcellular localization. For example, Nair and Rost (Protein Sci 2002;11:2836-2847) have carried out extensive analysis of the relation between sequence similarity and identity in subcellular localization, and have found a close relationship between them above a certain similarity threshold. However, many existing benchmark data sets used for the prediction accuracy assessment contain highly homologous sequences-some data sets comprising sequences up to 80-90% sequence identity. Using these benchmark test data will surely lead to overestimation of the performance of the methods considered. Here, we develop an approach based on a two-level support vector machine (SVM) system: the first level comprises a number of SVM classifiers, each based on a specific type of feature vectors derived from sequences; the second level SVM classifier functions as the jury machine to generate the probability distribution of decisions for possible localizations. We compare our approach with a global sequence alignment approach and other existing approaches for two benchmark data sets-one comprising prokaryotic sequences and the other eukaryotic sequences. Furthermore, we carried out all-against-all sequence alignment for several data sets to investigate the relationship between sequence homology and subcellular localization. Our results, which are consistent with previous studies, indicate that the homology search approach performs well down to 30% sequence identity, although its performance deteriorates considerably for sequences sharing lower sequence identity. A data set of high homology levels will undoubtedly lead to biased assessment of the performances of the predictive approaches-especially those relying on homology search or sequence annotations. Our two-level classification system based on SVM does not rely on homology search; therefore, its performance remains relatively unaffected by sequence homology. When compared with other approaches, our approach performed significantly better. Furthermore, we also develop a practical hybrid method, which combines the two-level SVM classifier and the homology search method, as a general tool for the sequence annotation of subcellular localization.
            • Record: found
            • Abstract: found
            • Article: not found

            Prediction of lipoprotein signal peptides in Gram-negative bacteria.

            A method to predict lipoprotein signal peptides in Gram-negative Eubacteria, LipoP, has been developed. The hidden Markov model (HMM) was able to distinguish between lipoproteins (SPaseII-cleaved proteins), SPaseI-cleaved proteins, cytoplasmic proteins, and transmembrane proteins. This predictor was able to predict 96.8% of the lipoproteins correctly with only 0.3% false positives in a set of SPaseI-cleaved, cytoplasmic, and transmembrane proteins. The results obtained were significantly better than those of previously developed methods. Even though Gram-positive lipoprotein signal peptides differ from Gram-negatives, the HMM was able to identify 92.9% of the lipoproteins included in a Gram-positive test set. A genome search was carried out for 12 Gram-negative genomes and one Gram-positive genome. The results for Escherichia coli K12 were compared with new experimental data, and the predictions by the HMM agree well with the experimentally verified lipoproteins. A neural network-based predictor was developed for comparison, and it gave very similar results. LipoP is available as a Web server at www.cbs.dtu.dk/services/LipoP/.
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              COFACTOR: an accurate comparative algorithm for structure-based protein function annotation

              We have developed a new COFACTOR webserver for automated structure-based protein function annotation. Starting from a structural model, given by either experimental determination or computational modeling, COFACTOR first identifies template proteins of similar folds and functional sites by threading the target structure through three representative template libraries that have known protein–ligand binding interactions, Enzyme Commission number or Gene Ontology terms. The biological function insights in these three aspects are then deduced from the functional templates, the confidence of which is evaluated by a scoring function that combines both global and local structural similarities. The algorithm has been extensively benchmarked by large-scale benchmarking tests and demonstrated significant advantages compared to traditional sequence-based methods. In the recent community-wide CASP9 experiment, COFACTOR was ranked as the best method for protein–ligand binding site predictions. The COFACTOR sever and the template libraries are freely available at http://zhanglab.ccmb.med.umich.edu/COFACTOR.

                Author and article information

                Contributors
                URI : http://frontiersin.org/people/u/405301
                URI : http://frontiersin.org/people/u/388665
                URI : http://frontiersin.org/people/u/430840
                URI : http://frontiersin.org/people/u/407558
                URI : http://frontiersin.org/people/u/413606
                URI : http://frontiersin.org/people/u/334489
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                27 April 2017
                2017
                : 8
                : 463
                Affiliations
                [1] 1Biotechnology Unit, Technological Development Center, Federal University of Pelotas , Pelotas, Rio Grande do Sul, Brazil
                [2] 2Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Ministry of Health , Salvador, Bahia, Brazil
                Author notes

                Edited by: Monica E. Embers, Tulane University, USA

                Reviewed by: Catherine Ayn Brissette, University of North Dakota, USA; Maria Gomes-Solecki, University of Tennessee Health Science Center, USA

                *Correspondence: Alan John Alexander McBride, alan.mcbride@ 123456ufpel.edu.br

                Specialty section: This article was submitted to Microbial Immunology, a section of the journal Frontiers in Immunology

                Article
                10.3389/fimmu.2017.00463
                5406399
                28496441
                eb308d9a-c527-4f16-abcd-d7611095dfe7
                Copyright © 2017 Grassmann, Kremer, Santos, Souza, Pinto and McBride.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 30 October 2016
                : 04 April 2017
                Page count
                Figures: 4, Tables: 4, Equations: 7, References: 117, Pages: 20, Words: 14484
                Funding
                Funded by: Conselho Nacional de Desenvolvimento Científico e Tecnológico 10.13039/501100003593
                Award ID: 481133/2011-9, 483052/2012-4, 311852/2012-2
                Categories
                Immunology
                Original Research

                Immunology
                leptospira interrogans,outer membrane protein,epitope prediction,bioinformatics,transport proteins,structural modeling,genome mining,diderm bacteria

                Comments

                Comment on this article

                Related Documents Log