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Prediction of HIV peptide epitopes
by a novel algorithm.
Roberts CG, Meister GE,
Jesdale BM, Lieberman J, Berzofsky JA, De Groot AS.
TB/HIV
Research Laboratory, Brown University School of Medicine, Providence,
Rhode Island 02912, USA.
Identification of promiscuous or
multideterminant T cell epitopes is essential for HIV vaccine
development, however, current methods for T cell epitope identification
are both cost intensive and labor intensive. We have developed a
computer-driven algorithm, named EpiMer, which searches protein amino
acid sequences for putative MHC class I- and/or class II-restricted T
cell epitopes. This algorithm identifies peptides that contain multiple
MHC-binding motifs from protein sequences. To evaluate the predictive
power of EpiMer, the amino acid sequences of the HIV-1 proteins nef,
gp160, gag p55, and tat were searched for regions of MHC-binding motif
clustering. We assessed the algorithm's predictive power by comparing
the EpiMer-predicted peptide epitopes to T cell epitopes that have been
published in the literature. The EpiMer method of T cell epitope
identification was compared to the standard method of synthesizing
short, overlapping peptides and testing them for immunogenicity
(overlapping peptide method), and to an alternate algorithm that has
been used to identify putative T cell epitopes from primary structure
(AMPHI). For the four HIV-1 proteins analyzed, the in vitro testing of
EpiMer peptides for immunogenicity would have required the synthesis of
fewer total peptides than either AMPHI or the overlapping peptide
method. The EpiMer algorithm proved to be more efficient and more
sensitive per amino acid than both the overlapping peptide method and
AMPHI. The EpiMer predictions for these four HIV proteins are described.
Since EpiMer-predicted peptides have the potential to bind to multiple
MHC alleles, they are strong candidates for inclusion in a synthetic HIV
vaccine.
PMID: 8743085 [PubMed - indexed for MEDLINE]
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