Please use this identifier to cite or link to this item: https://cris.library.msu.ac.zw//handle/11408/6268
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dc.contributor.authorArthur Vengesaien_US
dc.contributor.authorMarble Manuwaen_US
dc.contributor.authorHerald Midzien_US
dc.contributor.authorMasimba Mandeyaen_US
dc.contributor.authorVictor Muleyaen_US
dc.contributor.authorKeith Mujenien_US
dc.contributor.authorIsaac Chipakoen_US
dc.contributor.authorTakafira Mduluzaen_US
dc.date.accessioned2024-09-17T13:06:48Z-
dc.date.available2024-09-17T13:06:48Z-
dc.date.issued2024-08-22-
dc.identifier.urihttps://cris.library.msu.ac.zw//handle/11408/6268-
dc.description.abstractIntroduction Immunoinformatic tools can be used to predict schistosome-specific B-cell epitopes with little sequence identity to human proteins and antigens other than the target. This study reports an approach for identifying schistosome peptides mimicking linear B-cell epitopes using in-silico tools and peptide microarray immunoassay validation. Method Firstly, a comprehensive literature search was conducted to obtain published schistosome-specific peptides and recombinant proteins with the best overall diagnostic performances. For novel peptides, linear B-cell epitopes were predicted from target recombinant proteins using ABCpred, Bcepred and BepiPred 2.0 in-silico tools. Together with the published peptides, predicted peptides with the highest probability of being B-cell epitopes and the lowest sequence identity with proteins from human and other pathogens were selected. Antibodies against the peptides were measured in sera, using peptide microarray immunoassays. Area under the ROC curve was calculated to assess the overall diagnostic performances of the peptides. Results Peptide AA81008-19-30 had excellent and acceptable diagnostic performances for discriminating S. mansoni and S. haematobium positives from healthy controls, with AUC values of 0.8043 and 0.7326 respectively for IgG. Peptides MS3_10186-123-131, MS3_10385-339-354, SmSPI-177-193, SmSPI-379-388, MS3-10186-40-49 and SmS-197-214 had acceptable diagnostic performances for discriminating S. mansoni positives from healthy controls with AUC values ranging from 0.7098 to 0.7763 for IgG. Peptides SmSPI-359-372, Smp126160-438-452 and MS3 10186-25-41 had acceptable diagnostic performances for discriminating S. mansoni positives from S. mansoni negatives with AUC values of 0.7124, 0.7156 and 0.7115 respectively for IgG. Peptide MS3-10186-40-49 had an acceptable diagnostic performance for discriminating S. mansoni positives from healthy controls, with an AUC value of 0.7413 for IgM. Conclusion One peptide with a good diagnostic performance and nine peptides with acceptable diagnostic performances were identified using the immunoinformatic approach and peptide microarray validation. There is need for evaluation of the peptides with true negatives and a good standard positive reference.en_US
dc.language.isoenen_US
dc.publisherPublic Library of Scienceen_US
dc.relationReceived financial support from the Merck Schistosomiasis Research Grant through funding from ARES Trading S.A., an affiliate of Merck KGaA, Darmstadt, Germany, to A. V.en_US
dc.relation.ispartofPLoS Neglected Tropical Diseasesen_US
dc.subjectSchistosoma haematobiumen_US
dc.subjectSchistosoma mansonien_US
dc.subjectlinear B-cell epitopesen_US
dc.subjectin silico immunoinformatic toolsen_US
dc.subjectpeptide microarray technologyen_US
dc.titleIdentification of Schistosoma haematobium and Schistosoma mansoni linear B-cell epitopes with diagnostic potential using in silico immunoinformatic tools and peptide microarray technologyen_US
dc.typeresearch articleen_US
dc.identifier.doihttps://doi.org/10.1371/journal.pntd.0011887-
dc.contributor.affiliationDepartment of Biochemistry, Faculty of Medicine and Health Sciences, Midlands State University, Senga Road, Gweru, Zimbabween_US
dc.contributor.affiliationDepartment of Biotechnology and Biochemistry, Faculty of Science, University of Zimbabwe, Mount Pleasant, Harare, Zimbabween_US
dc.contributor.affiliationDepartment of Applied Biosciences and Biotechnology, Faculty of Science, Midlands State University, Senga Road, Gweru, Zimbabween_US
dc.contributor.affiliationDepartment of Biochemistry, Faculty of Medicine and Health Sciences, Midlands State University, Senga Road, Gweru, Zimbabween_US
dc.contributor.affiliationDepartment of Biochemistry, Faculty of Medicine and Health Sciences, Midlands State University, Senga Road, Gweru, Zimbabween_US
dc.contributor.affiliationPartnership in Education Training and Research Advancement, Faculty of Health Sciences, University of Zimbabwe, Harare, Zimbabween_US
dc.contributor.affiliationHealth Economics and Policy Department, Division of Health Research Graduate College, Lancaster University, Lancaster, United Kingdomen_US
dc.contributor.affiliationDepartment of Biotechnology and Biochemistry, Faculty of Science, University of Zimbabwe, Mount Pleasant, Harare, Zimbabween_US
dc.relation.issn1935-2735en_US
dc.description.volume18en_US
dc.description.issue8en_US
dc.description.startpage1en_US
dc.description.endpage19en_US
item.languageiso639-1en-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetyperesearch article-
item.cerifentitytypePublications-
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