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Ning L, Huang J, He B, Kang J. An In Silico Immunogenicity Analysis for PbHRH: An Antiangiogenic Peptibody by Fusing HRH Peptide and Human IgG1 Fc Fragment. Curr Bioinform 2020. [DOI: 10.2174/1574893614666190730104348] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Background:
Peptibodies, the hybrid of peptides and antibodies, represent a novel
strategy in therapeutic use. Previously, we computationally designed an antiangiogenic peptibody
PbHRH, which fused the HRH peptide with angiogenesis-suppressing effect and human IgG1 Fc
fragment using Romiplostim as template. Molecular modeling and simulation results indicated that
it would be a potential drug for the treatment of those angiogenesis related pathological disorders.
However, its immunogenicity is not known.
Methods:
Several bioinformatics tools are used to predict the potential epitopes for the evaluation
of the immunogenicity of PbHRH. Romiplostim is set as the control. IEDB-recommended method
is used in MHC-I and MHC-II binding prediction, and the IEDB web server
(http://tools.iedb.org/immunogenicity/) is used to determine the MHC-I immunogenicity of each
peptide.
Results:
In this work, some peptides are predicted to have the potential ability to bind to MHC-I
and MHC-II molecules both in PbHRH and Romiplostim as the potential epitopes. Most of these
selected peptides are exactly the same. Allele frequency analysis shows a low population
distribution. Combined with the analysis of MHC-I immunogenicity prediction, both HRH and
PbHRH show low immunogenicity.
Conclusion:
Some potential epitopes which could bind to both MHC-I and MHC-II molecules
are predicted using bioinformatics tools. The comparative analysis with Romiplostim and the
results of MHC-I immunogenicity prediction indicate the low immunogenicity of both HRH and
PbHRH. Thus, we form a strategy to evaluate the immunogenicity of peptibodies for the future
improvement.
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Affiliation(s)
- Lin Ning
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiang Huang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bifang He
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Juanjuan Kang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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