Design and structural bioinformatic analysis of polypeptide antigens useful for the SRLV serodiagnosis.


Journal

Journal of virological methods
ISSN: 1879-0984
Titre abrégé: J Virol Methods
Pays: Netherlands
ID NLM: 8005839

Informations de publication

Date de publication:
11 2021
Historique:
received: 09 02 2021
revised: 30 06 2021
accepted: 18 08 2021
pubmed: 30 8 2021
medline: 22 3 2022
entrez: 29 8 2021
Statut: ppublish

Résumé

Due to their intrinsic genetic, structural and phenotypic variability the Lentiviruses, and specifically small ruminant lentiviruses (SRLV), are considered viral quasispecies with a population structure that consists of extremely large numbers of variant genomes, termed mutant spectra or mutant cloud. Immunoenzymatic tests for SRLVs are available but the dynamic heterogeneity of the virus makes the development of a diagnostic "golden standard" extremely difficult. The ELISA reported in the literature have been obtained using proteins derived from a single strain or they are multi-strain based assay that may increase the sensitivity of the serological diagnosis. Hundreds of SRLV protein sequences derived from different viral strains are deposited in GenBank. The aim of this study is to verify if the database can be exploited with the help of bioinformatics in order to have a more systematic approach in the design of a set of representative protein antigens useful in the SRLV serodiagnosis. Clustering, molecular modelling, molecular dynamics, epitope predictions and aggregative/solubility predictions were the main bioinformatic tools used. This approach led to the design of SRLV antigenic proteins that were expressed by recombinant DNA technology using synthetic genes, analyzed by CD spectroscopy, tested by ELISA and preliminarily compared to currently commercially available detection kits.

Identifiants

pubmed: 34454989
pii: S0166-0934(21)00205-6
doi: 10.1016/j.jviromet.2021.114266
pii:
doi:

Substances chimiques

Peptides 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

114266

Informations de copyright

Copyright © 2021 Elsevier B.V. All rights reserved.

Auteurs

Angela Ostuni (A)

Department of Sciences, University of Basilicata, viale Ateneo Lucano 10, 85100, Potenza, Italy. Electronic address: angela.ostuni@unibas.it.

Magnus Monné (M)

Department of Sciences, University of Basilicata, viale Ateneo Lucano 10, 85100, Potenza, Italy.

Maria Antonietta Crudele (MA)

Department of Sciences, University of Basilicata, viale Ateneo Lucano 10, 85100, Potenza, Italy.

Pier Luigi Cristinziano (PL)

Department of Sciences, University of Basilicata, viale Ateneo Lucano 10, 85100, Potenza, Italy.

Stefano Cecchini (S)

Department of Sciences, University of Basilicata, viale Ateneo Lucano 10, 85100, Potenza, Italy.

Mario Amati (M)

Department of Sciences, University of Basilicata, viale Ateneo Lucano 10, 85100, Potenza, Italy.

Jolanda De Vendel (J)

OneHEco APS, 84047, Capaccio Paestum, SA, Italy.

Paolo Raimondi (P)

OneHEco APS, 84047, Capaccio Paestum, SA, Italy.

Taxiarchis Chassalevris (T)

Diagnostic Laboratory, School of Veterinary Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 11 Stavrou Voutyra Str., 54627, Thessaloniki, Greece.

Chrysostomos I Dovas (CI)

Diagnostic Laboratory, School of Veterinary Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 11 Stavrou Voutyra Str., 54627, Thessaloniki, Greece.

Alfonso Bavoso (A)

Department of Sciences, University of Basilicata, viale Ateneo Lucano 10, 85100, Potenza, Italy.

Articles similaires

Robotic Surgical Procedures Animals Humans Telemedicine Models, Animal

Odour generalisation and detection dog training.

Lyn Caldicott, Thomas W Pike, Helen E Zulch et al.
1.00
Animals Odorants Dogs Generalization, Psychological Smell
Animals TOR Serine-Threonine Kinases Colorectal Neoplasms Colitis Mice
Animals Tail Swine Behavior, Animal Animal Husbandry

Classifications MeSH