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2015 ; 16 Suppl 13
(Suppl 13
): S9
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Understanding and predicting binding between human leukocyte antigens (HLAs) and
peptides by network analysis
#MMPMID26424483
Luo H
; Ye H
; Ng H
; Shi L
; Tong W
; Mattes W
; Mendrick D
; Hong H
BMC Bioinformatics
2015[]; 16 Suppl 13
(Suppl 13
): S9
PMID26424483
show ga
BACKGROUND: As the major histocompatibility complex (MHC), human leukocyte
antigens (HLAs) are one of the most polymorphic genes in humans. Patients
carrying certain HLA alleles may develop adverse drug reactions (ADRs) after
taking specific drugs. Peptides play an important role in HLA related ADRs as
they are the necessary co-binders of HLAs with drugs. Many experimental data have
been generated for understanding HLA-peptide binding. However, efficiently
utilizing the data for understanding and accurately predicting HLA-peptide
binding is challenging. Therefore, we developed a network analysis based method
to understand and predict HLA-peptide binding. METHODS: Qualitative Class I
HLA-peptide binding data were harvested and prepared from four major databases.
An HLA-peptide binding network was constructed from this dataset and modules were
identified by the fast greedy modularity optimization algorithm. To examine the
significance of signals in the yielded models, the modularity was compared with
the modularity values generated from 1,000 random networks. The peptides and HLAs
in the modules were characterized by similarity analysis. The neighbor-edges
based and unbiased leverage algorithm (Nebula) was developed for predicting
HLA-peptide binding. Leave-one-out (LOO) validations and two-fold
cross-validations were conducted to evaluate the performance of Nebula using the
constructed HLA-peptide binding network. RESULTS: Nine modules were identified
from analyzing the HLA-peptide binding network with a highest modularity compared
to all the random networks. Peptide length and functional side chains of amino
acids at certain positions of the peptides were different among the modules. HLA
sequences were module dependent to some extent. Nebula archived an overall
prediction accuracy of 0.816 in the LOO validations and average accuracy of 0.795
in the two-fold cross-validations and outperformed the method reported in the
literature. CONCLUSIONS: Network analysis is a useful approach for analyzing
large and sparse datasets such as the HLA-peptide binding dataset. The modules
identified from the network analysis clustered peptides and HLAs with similar
sequences and properties of amino acids. Nebula performed well in the predictions
of HLA-peptide binding. We demonstrated that network analysis coupled with Nebula
is an efficient approach to understand and predict HLA-peptide binding
interactions and thus, could further our understanding of ADRs.
|HLA Antigens/*analysis/*genetics
[MESH]
|Histocompatibility Antigens Class II/*analysis/*genetics
[MESH]