ArticleScientific reports2021
The effect of protein mutations on drug binding suggests ensuing personalised drug selection.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 23 citations in OpenAlex.
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- Article
- Introduction to Computational Biomedicine.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Breast Cancer in the Arabian Gulf Countries.Cancers · 2023Review
- DGDTA: dynamic graph attention network for predicting drug-target binding affinity.BMC bioinformatics · 2023Article
- BayeStab: Predicting effects of mutations on protein stability with uncertainty quantification.Protein science : a publication of the Protein Society · 2022Article
- Large Scale Study of Ligand-Protein Relative Binding Free Energy Calculations: Actionable Predictions from Statistically Robust Protocols.Journal of chemical theory and computation · 2022Article
- Individualized Proteogenomics Reveals the Mutational Landscape of Melanoma Patients in Response to Immunotherapy.Cancers · 2021Article
- Rapid, accurate, precise and reproducible ligand-protein binding free energy prediction.Interface focus · 2020Review
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
The advent of personalised medicine promises a deeper understanding of mechanisms and therefore therapies. However, the connection between genomic sequences and clinical treatments is often unclear. We studied 50 breast cancer patients belonging to a population-cohort in the state of Qatar. From Sanger sequencing, we identified several new deleterious mutations in the estrogen receptor 1 gene (ESR1). The effect of these mutations on drug treatment in the protein target encoded by ESR1, namely the estrogen receptor, was achieved via rapid and accurate protein-ligand binding affinity interaction studies which were performed for the selected drugs and the natural ligand estrogen. Four nonsynonymous mutations in the ligand-binding domain were subjected to molecular dynamics simulation using absolute and relative binding free energy methods, leading to the ranking of the efficacy of six selected drugs for patients with the mutations. Our study shows that a personalised clinical decision system can be created by integrating an individual patient's genomic data at the molecular level within a computational pipeline which ranks the efficacy of binding of particular drugs to variant proteins.
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