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      Analysis of the Role of TpUB05 Antigen from Theileria parva in Immune Responses to Malaria in Humans Compared to Its Homologue in Plasmodium falciparum the UB05 Antigen

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          Abstract

          Despite the amount of resources deployed and the technological advancements in molecular biology, vaccinology, immunology, genetics, and biotechnology, there are still no effective vaccines against malaria. Immunity to malaria is usually seen to be species- and/or strain-specific. However, there is a growing body of evidence suggesting the possibility of the existence of cross-strain, cross-species, and cross-genus immune responses in apicomplexans. The principle of gene conservation indicates that homologues play a similar role in closely related organisms. The homologue of UB05 in Theileria parva is TpUB05 (XP_763711.1), which has been tested and shown to be associated with protective immunity in East Coast fever. In a bid to identify potent markers of protective immunity to aid malaria vaccine development, TpUB05 was tested in malaria caused by Plasmodium falciparum. It was observed that TpUB05 was better at detecting antigen-specific antibodies in plasma compared to UB05 when tested by ELISA. The total IgG raised against TpUB05 was able to block parasitic growth in vitro more effectively than that raised against UB05. However, there was no significant difference between the two study antigens in recalling peripheral blood mononuclear cell (PBMC) memory through IFN-γ production. This study suggests, for the first time, that TpUB05 from T. parva cross-reacts with UB05 from P. falciparum and is a marker of protective immunity in malaria. Hence, TpUB05 should be considered for possible development as a potential subunit vaccine candidate against malaria.

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          Most cited references 43

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          Acquired immunity to malaria.

          Naturally acquired immunity to falciparum malaria protects millions of people routinely exposed to Plasmodium falciparum infection from severe disease and death. There is no clear concept about how this protection works. There is no general agreement about the rate of onset of acquired immunity or what constitutes the key determinants of protection; much less is there a consensus regarding the mechanism(s) of protection. This review summarizes what is understood about naturally acquired and experimentally induced immunity against malaria with the help of evolving insights provided by biotechnology and places these insights in the context of historical, clinical, and epidemiological observations. We advocate that naturally acquired immunity should be appreciated as being virtually 100% effective against severe disease and death among heavily exposed adults. Even the immunity that occurs in exposed infants may exceed 90% effectiveness. The induction of an adult-like immune status among high-risk infants in sub-Saharan Africa would greatly diminish disease and death caused by P. falciparum. The mechanism of naturally acquired immunity that occurs among adults living in areas of hyper- to holoendemicity should be understood with a view toward duplicating such protection in infants and young children in areas of endemicity.
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            Reliable prediction of T-cell epitopes using neural networks with novel sequence representations.

            In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence-encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix-driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high-binding peptides. Finally, we use the method to predict T-cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design.
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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

                Journal
                Pathogens
                Pathogens
                pathogens
                Pathogens
                MDPI
                2076-0817
                08 April 2020
                April 2020
                : 9
                : 4
                Affiliations
                [1 ]Biotechnology Unit, Faculty of Science, University of Buea, P O. Box 63 Buea, Cameroon
                [2 ]Department of Biochemistry and Molecular Biology, Faculty of Science, University of Buea, P. O. Box 63 Buea, Cameroon
                [3 ]Biosciences Eastern and Central Africa—International Livestock Research Institute (BecA-ILRI) Hub, P. O. Box 30709 Nairobi, Kenya
                [4 ]Centre for Tropical Livestock Genetics and Health, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, Easter Bush Campus, EH25 9RG Edinburgh, UK
                [5 ]Faculty of Science, Engineering and Technology, Cameroon Christian University Institute, P.O. Box 5 Bali, Cameroon
                Author notes
                [* ]Correspondence: djnyhalah@ 123456yahoo.com ; Tel.: +237-233322134
                Article
                pathogens-09-00271
                10.3390/pathogens9040271
                7238281
                32276308
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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