Dataset Information

SNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides.

ABSTRACT: Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.

SUBMITTER: Luo H

PROVIDER: S-EPMC4997263 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides.

Luo Heng H Ye Hao H Ng Hui Wen HW Sakkiah Sugunadevi S Mendrick Donna L DL Hong Huixiao H

Scientific reports 20160825

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula t ...[more]

PMID: 27558848

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Project description: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.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.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.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.

Project description:The IgE response has been associated with both allergic reactions and immunity to metazoan parasites. Recently, we hypothesized that all environmental allergens bear structural homology to IgE-binding antigens from metazoan parasites and that this homology defines the relatively small number of protein families containing allergenic targets. In this study, known allergen structures (Pfam domains) from major environmental allergen families were used to predict allergen-like (SmProfilin, SmVAL-6, SmLipocalin, SmHSP20, Sm triosephosphate isomerase, SmThioredoxin, Sm superoxide dismutase, SmCyclophilin, and Sm phosphoglycerate kinase) and non-allergen-like [Sm dynein light chain (SmDLC), SmAldolase SmAK, SmUbiquitin, and Sm14-3-3] proteins in Schistosoma mansoni. Recombinant antigens were produced in Escherichia coli and IgG1, IgG4, and IgE responses against them measured in a cohort of people (n = 222) infected with S. mansoni. All allergen-like antigens were targeted by IgE responses in infected subjects, whilst IgE responses to the non-allergen-like antigens, SmAK, SmUbiquitin, and Sm14-3-3 were essentially absent being of both low prevalence and magnitude. Two new IgE-binding Pfam domain families, not previously described in allergen family databases, were also found, with prevalent IgE responses against SmDLC (PF01221) and SmAldolase (PF00274). Finally, it was demonstrated that immunoregulatory serological processes typically associated with allergens also occurred in responses to allergen-like proteins in S. mansoni infections, including the production of IgG4 in people responding with IgE and the down-regulation of IgE in response to increased antigen exposure from S. mansoni eggs. This study establishes that structures of known allergens can be used to predict IgE responses against homologous parasite allergen-like molecules (parallergens) and that serological responses with IgE/IgG4 to parallergens mirror those seen against allergens, supporting our hypothesis that allergenicity is rooted in expression of certain protein domain families in metazoan parasites.

Dataset Information

SNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides.

Publications

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides.

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