Identification of Schistosoma haematobium and Schistosoma mansoni linear B-cell epitopes with diagnostic potential using in silico immunoinformatic tools and peptide microarray technology.


Journal

PLoS neglected tropical diseases
ISSN: 1935-2735
Titre abrégé: PLoS Negl Trop Dis
Pays: United States
ID NLM: 101291488

Informations de publication

Date de publication:
22 Aug 2024
Historique:
received: 27 12 2023
accepted: 06 08 2024
medline: 22 8 2024
pubmed: 22 8 2024
entrez: 22 8 2024
Statut: aheadofprint

Résumé

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. 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. 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. 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.

Identifiants

pubmed: 39173089
doi: 10.1371/journal.pntd.0011887
pii: PNTD-D-23-01642
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0011887

Informations de copyright

Copyright: © 2024 Vengesai et al. 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.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Auteurs

Arthur Vengesai (A)

Department of Biochemistry, Faculty of Medicine and Health Sciences, Midlands State University, Senga Road, Gweru, Zimbabwe.

Marble Manuwa (M)

Department of Biotechnology and Biochemistry, Faculty of Science, University of Zimbabwe, Mount Pleasant, Harare, Zimbabwe.

Herald Midzi (H)

Department of Applied Biosciences and Biotechnology, Faculty of Science, Midlands State University, Senga Road, Gweru, Zimbabwe.

Masimba Mandeya (M)

Department of Biochemistry, Faculty of Medicine and Health Sciences, Midlands State University, Senga Road, Gweru, Zimbabwe.

Victor Muleya (V)

Department of Biochemistry, Faculty of Medicine and Health Sciences, Midlands State University, Senga Road, Gweru, Zimbabwe.

Keith Mujeni (K)

Partnership in Education Training and Research Advancement, Faculty of Health Sciences, University of Zimbabwe, Harare, Zimbabwe.

Isaac Chipako (I)

Health Economics and Policy Department, Division of Health Research Graduate College, Lancaster University, Lancaster, United Kingdom.

Takafira Mduluza (T)

Department of Biotechnology and Biochemistry, Faculty of Science, University of Zimbabwe, Mount Pleasant, Harare, Zimbabwe.

Classifications MeSH