ArticlePLoS computational biology2009
Harvesting candidate genes responsible for serious adverse drug reactions from a chemical-protein interactome.
Article in PLoS computational biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed, 99 citations in OpenAlex.
- Joint fusion of sequences and structures of drugs and targets for identifying targets based on intra and inter cross-attention mechanisms.BMC biology · 2025Article
- Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction.iScience · 2024Article
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- Learning Multi-Types of Neighbor Node Attributes and Semantics by Heterogeneous Graph Transformer and Multi-View Attention for Drug-Related Side-Effect Prediction.Molecules (Basel, Switzerland) · 2023Article
- Large-scale prediction of adverse drug reactions-related proteins with network embedding.Bioinformatics (Oxford, England) · 2023Article
- Graph generative and adversarial strategy-enhanced node feature learning and self-calibrated pairwise attribute encoding for prediction of drug-related side effects.Frontiers in pharmacology · 2023Article
- Human adenovirus DNA polymerase is evolutionarily and functionally associated with human telomerase reverse transcriptase based onFrontiers in bioinformatics · 2023Article
- Molecular Docking: Shifting Paradigms in Drug Discovery.International journal of molecular sciences · 2019Review
- Assessment of the cardiovascular adverse effects of drug-drug interactions through a combined analysis of spontaneous reports and predicted drug-target interactions.PLoS computational biology · 2019Article
- Critical assessment of approaches for molecular docking to elucidate associations of HLA alleles with adverse drug reactions.Molecular immunology · 2018Article
- Large-scale identification of adverse drug reaction-related proteins through a random walk model.Scientific reports · 2016Article
- DPDR-CPI, a server that predicts Drug Positioning and Drug Repositioning via Chemical-Protein Interactome.Scientific reports · 2016Article
- T-cell-mediated drug hypersensitivity: immune mechanisms and their clinical relevance.Asia Pacific allergy · 2016Review
- Molecular docking as a popular tool in drug design, an in silico travel.Advances and applications in bioinformatics and chemistry : AABC · 2016Review
- Relating Essential Proteins to Drug Side-Effects Using Canonical Component Analysis: A Structure-Based Approach.Journal of chemical information and modeling · 2015Article
- Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action.PLoS computational biology · 2015Article
- RepurposeVS: A Drug Repurposing-Focused Computational Method for Accurate Drug-Target Signature Predictions.Combinatorial chemistry & high throughput screening · 2015Article
- DDI-CPI, a server that predicts drug-drug interactions through implementing the chemical-protein interactome.Nucleic acids research · 2014Article
- Article
- Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review.Pharmacology & therapeutics · 2013Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying genetic factors responsible for serious adverse drug reaction (SADR) is of critical importance to personalized medicine. However, genome-wide association studies are hampered due to the lack of case-control samples, and the selection of candidate genes is limited by the lack of understanding of the underlying mechanisms of SADRs. We hypothesize that drugs causing the same type of SADR might share a common mechanism by targeting unexpectedly the same SADR-mediating protein. Hence we propose an approach of identifying the common SADR-targets through constructing and mining an in silico chemical-protein interactome (CPI), a matrix of binding strengths among 162 drug molecules known to cause at least one type of SADR and 845 proteins. Drugs sharing the same SADR outcome were also found to possess similarities in their CPI profiles towards this 845 protein set. This methodology identified the candidate gene of sulfonamide-induced toxic epidermal necrolysis (TEN): all nine sulfonamides that cause TEN were found to bind strongly to MHC I (Cw*4), whereas none of the 17 control drugs that do not cause TEN were found to bind to it. Through an insight into the CPI, we found the Y116S substitution of MHC I (B*5703) enhances the unexpected binding of abacavir to its antigen presentation groove, which explains why B*5701, not B*5703, is the risk allele of abacavir-induced hypersensitivity. In conclusion, SADR targets and the patient-specific off-targets could be identified through a systematic investigation of the CPI, generating important hypotheses for prospective experimental validation of the candidate genes.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.