Understanding and predicting binding between human leukocyte antigens (HLAs) and peptides by network analysis

BMC Bioinformatics

Proceedings

Leming Shi¹  Weida Tong²  Hui Wen Ng²  Hao Ye²  William Mattes²  Huixiao Hong²  Donna Mendrick²  Heng Luo³ 

[1] Center for Pharmacogenomics, School of Pharmacy, Fudan University, 826 Zhangheng Rd, 201203, Shanghai, China;National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Rd, 72079, Jefferson, AR, USA;National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Rd, 72079, Jefferson, AR, USA;University of Arkansas at Little Rock/University of Arkansas for Medical Sciences Bioinformatics Graduate Program, 2801 S University Ave, AR 72204, Little Rock, Arkansas, USA;

关键词: HLA;    peptide;    network;    Nebula;    binding;    module;    prediction;    MHC;   

DOI  :  10.1186/1471-2105-16-S13-S9

来源: Springer

【 摘 要 】

BackgroundAs 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.MethodsQualitative 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.ResultsNine 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.ConclusionsNetwork 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.

【 授权许可】

Unknown   
© Luo et al. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.