ArticlePloS one2015
Linear Interaction Energy Based Prediction of Cytochrome P450 1A2 Binding Affinities with Reliability Estimation.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 24 citations in OpenAlex.
- The Interaction Mechanism Between C14-Polyacetylene Compounds and the Rat TRPA1 Receptor: An In Silico Study.International journal of molecular sciences · 2024Article
- Docking Studies and Molecular Dynamics Simulation ofJournal of pharmacy & bioallied sciences · 2020Article
- How Good is Jarzynski's Equality for Computer-Aided Drug Design?The journal of physical chemistry. B · 2020Article
- Combined Linear Interaction Energy and Alchemical Solvation Free-Energy Approach for Protein-Binding Affinity Computation.Journal of chemical theory and computation · 2020Article
- Recent Developments in Linear Interaction Energy Based Binding Free Energy Calculations.Frontiers in molecular biosciences · 2020Article
- A Comparative Linear Interaction Energy and MM/PBSA Study on SIRT1-Ligand Binding Free Energy Calculation.Journal of chemical information and modeling · 2019Article
- Calculate protein-ligand binding affinities with the extended linear interaction energy method: application on the Cathepsin S set in the D3R Grand Challenge 3.Journal of computer-aided molecular design · 2019Article
- Probing inhibition mechanisms of adenosine deaminase by using molecular dynamics simulations.PloS one · 2018Article
- Binding free energy predictions of farnesoid X receptor (FXR) agonists using a linear interaction energy (LIE) approach with reliability estimation: application to the D3R Grand Challenge 2.Journal of computer-aided molecular design · 2018Article
- Development of an Infrastructure for the Prediction of Biological Endpoints in Industrial Environments. Lessons Learned at the eTOX Project.Frontiers in pharmacology · 2018Article
- eTOX ALLIES: an automated pipeLine for linear interaction energy-based simulations.Journal of cheminformatics · 2017Article
- Comprehensive and Automated Linear Interaction Energy Based Binding-Affinity Prediction for Multifarious Cytochrome P450 Aromatase Inhibitors.Journal of chemical information and modeling · 2017Article
- Improving the iterative Linear Interaction Energy approach using automated recognition of configurational transitions.Journal of molecular modeling · 2016Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of human Cytochrome P450 (CYP) binding affinities of small ligands, i.e., substrates and inhibitors, represents an important task for predicting drug-drug interactions. A quantitative assessment of the ligand binding affinity towards different CYPs can provide an estimate of inhibitory activity or an indication of isoforms prone to interact with the substrate of inhibitors. However, the accuracy of global quantitative models for CYP substrate binding or inhibition based on traditional molecular descriptors can be limited, because of the lack of information on the structure and flexibility of the catalytic site of CYPs. Here we describe the application of a method that combines protein-ligand docking, Molecular Dynamics (MD) simulations and Linear Interaction Energy (LIE) theory, to allow for quantitative CYP affinity prediction. Using this combined approach, a LIE model for human CYP 1A2 was developed and evaluated, based on a structurally diverse dataset for which the estimated experimental uncertainty was 3.3 kJ mol-1. For the computed CYP 1A2 binding affinities, the model showed a root mean square error (RMSE) of 4.1 kJ mol-1 and a standard error in prediction (SDEP) in cross-validation of 4.3 kJ mol-1. A novel approach that includes information on both structural ligand description and protein-ligand interaction was developed for estimating the reliability of predictions, and was able to identify compounds from an external test set with a SDEP for the predicted affinities of 4.6 kJ mol-1 (corresponding to 0.8 pKi units).
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