Literature DB >> 25747444

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

Heng Luo, Tingting Du, Peng Zhou, Lun Yang, Hu Mei, Huiwen Ng, Wenqian Zhang, Mao Shu, Weida Tong, Leming Shi, Donna L Mendrick1, Huixiao Hong.   

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.

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Year:  2015        PMID: 25747444      PMCID: PMC4581727          DOI: 10.2174/1386207318666150305144015

Source DB:  PubMed          Journal:  Comb Chem High Throughput Screen        ISSN: 1386-2073            Impact factor:   1.339


  41 in total

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Journal:  Nature       Date:  2012-06-28       Impact factor: 49.962

2.  Drug hypersensitivity caused by alteration of the MHC-presented self-peptide repertoire.

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3.  HLA-A*3101 and carbamazepine-induced hypersensitivity reactions in Europeans.

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Review 4.  Drug hypersensitivity and human leukocyte antigens of the major histocompatibility complex.

Authors:  Mandvi Bharadwaj; Patricia Illing; Alex Theodossis; Anthony W Purcell; Jamie Rossjohn; James McCluskey
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Review 5.  Human leukocyte antigen-associated drug hypersensitivity.

Authors:  Patricia T Illing; Julian P Vivian; Anthony W Purcell; Jamie Rossjohn; James McCluskey
Journal:  Curr Opin Immunol       Date:  2012-11-08       Impact factor: 7.486

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9.  Human leukocyte antigen (HLA)-B*57:01-restricted activation of drug-specific T cells provides the immunological basis for flucloxacillin-induced liver injury.

Authors:  Manal M Monshi; Lee Faulkner; Andrew Gibson; Rosalind E Jenkins; John Farrell; Caroline J Earnshaw; Ana Alfirevic; Karin Cederbrant; Ann K Daly; Neil French; Munir Pirmohamed; B Kevin Park; Dean J Naisbitt
Journal:  Hepatology       Date:  2013-02       Impact factor: 17.425

10.  The IMGT/HLA database.

Authors:  James Robinson; Jason A Halliwell; Hamish McWilliam; Rodrigo Lopez; Peter Parham; Steven G E Marsh
Journal:  Nucleic Acids Res       Date:  2012-10-17       Impact factor: 16.971

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  17 in total

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

Authors:  Heng Luo; Hao Ye; Hui Ng; Leming Shi; Weida Tong; William Mattes; Donna Mendrick; Huixiao Hong
Journal:  BMC Bioinformatics       Date:  2015-09-25       Impact factor: 3.169

Review 8.  Machine Learning Methods for Predicting HLA-Peptide Binding Activity.

Authors:  Heng Luo; Hao Ye; Hui Wen Ng; Leming Shi; Weida Tong; Donna L Mendrick; Huixiao Hong
Journal:  Bioinform Biol Insights       Date:  2015-10-11

9.  Electrostatic explanation of D1228V/H/N-induced c-Met resistance and sensitivity to type I and type II kinase inhibitors in targeted gastric cancer therapy.

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Journal:  J Mol Model       Date:  2019-01-03       Impact factor: 1.810

10.  A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals.

Authors:  Huixiao Hong; Jie Shen; Hui Wen Ng; Sugunadevi Sakkiah; Hao Ye; Weigong Ge; Ping Gong; Wenming Xiao; Weida Tong
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