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Updated: Oct 31, 2025
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
Published on: January 26, 2024
The effect of protein mutations on drug binding suggests ensuing personalised drug selection
Shunzhou Wan1, Deepak Kumar2, Valentin Ilyin2
1Department of Chemistry, Centre for Computational Science, University College London, London, WC1H 0AJ, UK.
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.
Insights
Personalized medicine can link genetic mutations to breast cancer treatments. This study identified new estrogen receptor 1 gene mutations and ranked drug efficacy for patients, enabling tailored clinical decisions.
Area of Science:
- Genomics
- Pharmacology
- Computational Biology
Background:
- Personalized medicine aims to improve therapies by understanding disease mechanisms.
- A gap exists between genomic data and actionable clinical treatment strategies.
Purpose of the Study:
- To investigate the impact of estrogen receptor 1 gene (ESR1) mutations on drug efficacy in breast cancer patients.
- To develop a computational approach for personalized treatment selection based on individual genomic profiles.
Main Methods:
- Sanger sequencing to identify deleterious mutations in the ESR1 gene in 50 Qatari breast cancer patients.
- Protein-ligand binding affinity studies and molecular dynamics simulations to assess the impact of mutations on drug interactions.
- Absolute and relative binding free energy calculations to rank drug efficacy.
Main Results:
- Several novel deleterious mutations in the ESR1 gene were identified.
- The study ranked the efficacy of six selected drugs for patients harboring specific ESR1 mutations.
- Computational analysis revealed differential drug binding affinities to mutated estrogen receptors.
Conclusions:
- Integrating patient genomic data into computational pipelines can create personalized clinical decision systems.
- This approach allows for the ranking of drug efficacy against specific protein variants, guiding personalized breast cancer treatment.
- The findings support the advancement of precision medicine through molecular-level data integration.
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