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      Potent Adjuvanticity of a Pure TLR7-Agonistic Imidazoquinoline Dendrimer

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          Abstract

          Engagement of toll-like receptors (TLRs) serve to link innate immune responses with adaptive immunity and can be exploited as powerful vaccine adjuvants for eliciting both primary and anamnestic immune responses. TLR7 agonists are highly immunostimulatory without inducing dominant proinflammatory cytokine responses. We synthesized a dendrimeric molecule bearing six units of a potent TLR7/TLR8 dual-agonistic imidazoquinoline to explore if multimerization of TLR7/8 would result in altered activity profiles. A complete loss of TLR8-stimulatory activity with selective retention of the TLR7-agonistic activity was observed in the dendrimer. This was reflected by a complete absence of TLR8-driven proinflammatory cytokine and interferon (IFN)-γ induction in human PBMCs, with preservation of TLR7-driven IFN-α induction. The dendrimer was found to be superior to the imidazoquinoline monomer in inducing high titers of high-affinity antibodies to bovine α-lactalbumin. Additionally, epitope mapping experiments showed that the dendrimer induced immunoreactivity to more contiguous peptide epitopes along the amino acid sequence of the model antigen.

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          Most cited references26

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          Toll-like receptors: critical proteins linking innate and acquired immunity.

          Recognition of pathogens is mediated by a set of germline-encoded receptors that are referred to as pattern-recognition receptors (PRRs). These receptors recognize conserved molecular patterns (pathogen-associated molecular patterns), which are shared by large groups of microorganisms. Toll-like receptors (TLRs) function as the PRRs in mammals and play an essential role in the recognition of microbial components. The TLRs may also recognize endogenous ligands induced during the inflammatory response. Similar cytoplasmic domains allow TLRs to use the same signaling molecules used by the interleukin 1 receptors (IL-1Rs): these include MyD88, IL-1R--associated protein kinase and tumor necrosis factor receptor--activated factor 6. However, evidence is accumulating that the signaling pathways associated with each TLR are not identical and may, therefore, result in different biological responses.
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            A semi-empirical method for prediction of antigenic determinants on protein antigens.

            Analysis of data from experimentally determined antigenic sites on proteins has revealed that the hydrophobic residues Cys, Leu and Val, if they occur on the surface of a protein, are more likely to be a part of antigenic sites. A semi-empirical method which makes use of physicochemical properties of amino acid residues and their frequencies of occurrence in experimentally known segmental epitopes was developed to predict antigenic determinants on proteins. Application of this method to a large number of proteins has shown that our method can predict antigenic determinants with about 75% accuracy which is better than most of the known methods. This method is based on a single parameter and thus very simple to use.
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              Improved method for predicting linear B-cell epitopes

              Background B-cell epitopes are the sites of molecules that are recognized by antibodies of the immune system. Knowledge of B-cell epitopes may be used in the design of vaccines and diagnostics tests. It is therefore of interest to develop improved methods for predicting B-cell epitopes. In this paper, we describe an improved method for predicting linear B-cell epitopes. Results In order to do this, three data sets of linear B-cell epitope annotated proteins were constructed. A data set was collected from the literature, another data set was extracted from the AntiJen database and a data sets of epitopes in the proteins of HIV was collected from the Los Alamos HIV database. An unbiased validation of the methods was made by testing on data sets on which they were neither trained nor optimized on. We have measured the performance in a non-parametric way by constructing ROC-curves. Conclusion The best single method for predicting linear B-cell epitopes is the hidden Markov model. Combining the hidden Markov model with one of the best propensity scale methods, we obtained the BepiPred method. When tested on the validation data set this method performs significantly better than any of the other methods tested. The server and data sets are publicly available at .
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                28 August 2012
                : 7
                : 8
                : e43612
                Affiliations
                [1]Department of Medicinal Chemistry, University of Kansas, Lawrence, Kansas, United States of America
                Centers for Disease Control and Prevention, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: SAD. Performed the experiments: NMS DBS CAM SSM RB. Analyzed the data: SAD NMS. Contributed reagents/materials/analysis tools: NMS DBS CAM SSM RB SAD. Wrote the paper: SAD.

                Article
                PONE-D-12-18696
                10.1371/journal.pone.0043612
                3429503
                22952720
                b88f3c07-90ff-43c5-a880-e41c41accc57
                Copyright @ 2012

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 27 June 2012
                : 26 July 2012
                Page count
                Pages: 11
                Funding
                This work was supported by National Institutes of Health/National Institute of Allergy and Infectious Diseases contract HHSN272200900033C. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Immunology
                Immune System
                Cytokines
                Immunity
                Innate Immunity
                Molecular Cell Biology
                Signal Transduction
                Membrane Receptor Signaling
                Immunologic Receptor Signaling
                Chemistry
                Medicinal Chemistry
                Organic Chemistry
                Medicine
                Clinical Immunology
                Immunity
                Vaccination
                Vaccine Development

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                Uncategorized

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