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J Immunol Methods. 2011 Nov 30;374(1-2):13-7. doi: 10.1016/j.jim.2011.08.004. Epub 2011 Aug 11.

Identification of potential human respiratory syncytial virus and metapneumovirus T cell epitopes using computational prediction and MHC binding assays.

Author information

1
Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA.

Abstract

Human respiratory syncytial virus (RSV) and human metapneumovirus (MPV) are two of the most common causes of serious viral lower respiratory tract illness in humans. CD8+ T cells have been shown to be important in animal models and human clinical studies for the clearance of viral infection, and they may contribute in part to protection against severe disease during reinfections. Precise enumeration and accurate phenotyping of RSV- or MPV-specific CD8+ T cells in humans is currently limited by the relatively small number of T cell epitopes that have been mapped with accompanying identification of MHC restriction patterns. We sought to expand the number of potential RSV and MPV epitopes for use in clinical and translational studies by identifying an expanded set of MHC-binding peptides based on RSV and MPV wild-type virus strain protein sequences. We interrogated the full protein sequences of all 9 or 11 proteins of MPV or RSV respectively using four established epitope prediction algorithms for human HLA A*0101, A*0201, or B*0702 binding and attempted to synthesize the top-scoring 150-152 peptides for each of the two viruses. Synthesis resulted in 442 synthesized and soluble peptides of the 452 predicted epitopes for MPV or RSV. We then determined the binding of the synthetic peptides to recombinant human HLA A*0101, A*0201 or B*0702 molecules with the predicted restriction using a commercially available plate-based assay, iTopia. A total of 230 of the 442 peptides tested exhibited binding to the appropriate MHC molecule. The binding results suggested that existing algorithms for prediction of MHC A*0201 binding are particularly robust. The binding results also provided a large benchmarking data collection for comparison of new prediction algorithms.

PMID:
21854782
PMCID:
PMC3220792
DOI:
10.1016/j.jim.2011.08.004
[Indexed for MEDLINE]
Free PMC Article

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