Dataset Information

Molecular docking to identify associations between drugs and class I human leukocyte antigens for predicting idiosyncratic drug reactions.

ABSTRACT: Idiosyncratic drug reactions (IDRs) are rare, somewhat dose-independent, patient-specific and hard to predict. Human leukocyte antigens (HLAs) are the major histocompatibility complex (MHC) in humans, are highly polymorphic and are associated with specific IDRs. Therefore, it is important to identify potential drug-HLA associations so that individuals who would develop IDRs can be identified before drug exposure. We harvested the associations between drugs and class I HLAs from the literature. The results revealed that there are many drug-HLA pairs without clinical data. For better potential interactions of the drug-HLA pairs, molecular docking was used to explore the potential of associations between the drugs and HLAs. From the analysis of docking scores between the 17 drugs and 74 class I HLAs, it was observed that the known significantly associated drug-HLA pairs had statistically lower docking scores than those not reported to be significantly associated (t-test p < 0.05). This indicates that molecular docking could be utilized for screening drug-HLA interactions and predicting potential IDRs. Examining the binding modes of drugs in the docked HLAs suggested several distinct binding sites inside class I HLAs, expanding our knowledge of the underlying interaction mechanisms between drugs and HLAs.

SUBMITTER: Luo H

PROVIDER: S-EPMC4581727 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Molecular docking to identify associations between drugs and class I human leukocyte antigens for predicting idiosyncratic drug reactions.

Luo Heng H Du Tingting T Zhou Peng P Yang Lun L Mei Hu H Ng Huiwen H Zhang Wenqian W Shu Mao M Tong Weida W Shi Leming L Mendrick Donna L DL Hong Huixiao H

Combinatorial chemistry & high throughput screening 20150101 3

Idiosyncratic drug reactions (IDRs) are rare, somewhat dose-independent, patient-specific and hard to predict. Human leukocyte antigens (HLAs) are the major histocompatibility complex (MHC) in humans, are highly polymorphic and are associated with specific IDRs. Therefore, it is important to identify potential drug-HLA associations so that individuals who would develop IDRs can be identified before drug exposure. We harvested the associations between drugs and class I HLAs from the literature. T ...[more]

PMID: 25747444

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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.

Dataset Information

Molecular docking to identify associations between drugs and class I human leukocyte antigens for predicting idiosyncratic drug reactions.

Publications

Molecular docking to identify associations between drugs and class I human leukocyte antigens for predicting idiosyncratic drug reactions.

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