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

Luo, Heng; Ye, Hao; Ng, Hui Wen; Sakkiah, Sugunadevi; Mendrick, Donna L.; Hong, Huixiao

doi:10.1038/srep32115

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

Journal Article · Thu Aug 25 00:00:00 EDT 2016 · Scientific Reports

DOI:https://doi.org/10.1038/srep32115· OSTI ID:1378368

Luo, Heng ^[1]; Ye, Hao ^[2]; Ng, Hui Wen ^[2]; Sakkiah, Sugunadevi ^[2]; Mendrick, Donna L. ^[2]; Hong, Huixiao ^[2]

U.S. Food and Drug Administration, Jefferson, AR (United States); Univ. of Arkansas at Little Rock/Univ. of Arkansas for Medical Sciences Bioinformatics Graduate Program, Little Rock, AR (United States)
U.S. Food and Drug Administration, Jefferson, AR (United States)

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. Furthermore, 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.

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Research Organization:: Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN (United States); U.S. Food and Drug Administration, Jefferson, AR (United States). National Center for Toxicological Research

Sponsoring Organization:: USDOE

OSTI ID:: 1378368

Journal Information:: Scientific Reports, Vol. 6, Issue 1; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 12 works

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