The effect of protein mutations on drug binding suggests ensuing personalised drug selection

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.

Large Scale Study of Ligand–Protein Relative Binding Free Energy Calculations: Actionable Predictions from Statistically Robust Protocols

“… 4 They can also be useful in personalized medicine. 5 However, historical challenges such as high computational costs and consequent lack of sufficient sampling to obtain statistically robust results, force field accuracy, and time to solution as well as technical challenges in setting up and performing such calculations have limited the successful application of RBFE methods. 6 Several recent advances in software as well as hardware along with methodological improvements have provided a boost to these methods in terms of their applicability, especially in drug discovery.…”

“…Indeed, for not unrelated reasons we are still not at a stage where RBFE methods can be routinely applied with confidence in the pharmaceutical industry let alone by clinicians to predict drug resistance in personalized medicine. 5 There are many associated factors that need to be addressed in order to ensure that these methods can be employed in a routine fashion by end users. Quality of force field and ligand parameters, choice of alchemical region and the topology scheme (single, dual or mixed), handling of charge-changing transformations, protein starting structure, ligand pose placement, tautomerization, and ionization states are all issues that affect the quality of results.…”

“…Relative binding free energy (RBFE) methods based on classical molecular dynamics, that enable accurate prediction of protein–ligand binding affinities, offer an attractive route to optimize ligand–protein interactions on the drug discovery pathway . They can also be useful in personalized medicine . However, historical challenges such as high computational costs and consequent lack of sufficient sampling to obtain statistically robust results, force field accuracy, and time to solution as well as technical challenges in setting up and performing such calculations have limited the successful application of RBFE methods .…”

“…Indeed, for not unrelated reasons we are still not at a stage where RBFE methods can be routinely applied with confidence in the pharmaceutical industry let alone by clinicians to predict drug resistance in personalized medicine . There are many associated factors that need to be addressed in order to ensure that these methods can be employed in a routine fashion by end users.…”

Individualized Proteogenomics Reveals the Mutational Landscape of Melanoma Patients in Response to Immunotherapy

“…Several reasons may explain the differing results of bosutinib and trametinib, for example, the variant on the target protein may influence the drug binding in the case of trametinib. Alternatively, the effect observed with bosutinib could be a result of off-target inhibition (e.g., BCR, ABL1, LYN, SRC) [ 61 , 62 ].…”