MEM INST OSWALDO CRUZ, RIO DE JANEIRO, Vol. 112 | 2017
PAGES: DOI: 10.1590/0074-02760170013 Full paper
In silico analysis of amino acid variation in human respiratory syncytial virus: insights into immunodiagnostics

Claudemir Souza1,2,3, Nilson IT Zanchin1,3, Marco A Krieger1,2,3, Adriana Ludwig1,2,+

1Fundação Oswaldo Cruz-Fiocruz, Instituto Carlos Chagas, Laboratório de Genômica Funcional, Curitiba, PR, Brasil
2Instituto de Biologia Molecular do Paraná, Curitiba, PR, Brasil
3Universidade Federal do Paraná, Programa de Pós-Graduação em Biologia Celular e Molecular, Curitiba, PR, Brasil

Abstract

BACKGROUND The highly contagious nature of human respiratory syncytial virus (HRSV) and the gravity of its infection in newborns and vulnerable adults pose a serious public health problem. Thus, a rapid and sensitive diagnostic test for viral detection that can be implemented upon the first appearance of symptoms is needed. The genetic variation of the virus must be considered for immunodiagnostic purposes.

OBJECTIVES To analyse HRSV genetic variation and discuss the possible consequences for capture immunoassay development.

METHODS We performed a wide analysis of N, F and G protein variation based on the HRSV sequences currently available in the GenBank database. We also evaluated their similarity with homologous proteins from other viruses.

FINDINGS The mean amino acid divergences for the N, F, and G proteins between HRSV-A and HRSV-B were determined to be approximately 4%, 10% and 47%, respectively. Due to their high conservation, assays based on the full-length N and F proteins may not distinguish HRSV from human metapneumovirus and other Mononegavirales viruses, and the full-length G protein would most likely produce false negative results due to its high divergence.

MAIN CONCLUSIONS We have identified specific regions in each of these three proteins that have higher potential to produce specific results, and their combined utilisation should be considered for immunoassay development.

Financial support: IBMP grants obtained from FINEP (grant nº 01.11.0286.00), BNDES (grant nº 11.2.1328.1).
CS was supported by fellowships from CAPES and CNPq. AL was supported by a fellowship from CNPq. NITZ and MAK are research fellows from CNPq.
+ Corresponding author: This e-mail address is being protected from spambots. You need JavaScript enabled to view it.
Received 12 January 2017
Accepted 16 May 2017

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