Comprehensive and Automated Linear Interaction Energy Based Binding-Affinity Prediction for Multifarious Cytochrome P450 Aromatase Inhibitors
| Autor: | Luigi Capoferri, Antonius Ter Laak, Daan P. Geerke, Marc van Dijk, Jörg Wichard, Nico P. E. Vermeulen |
|---|---|
| Přispěvatelé: | Molecular and Computational Toxicology, AIMMS |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Protein Conformation General Chemical Engineering In silico Computational biology Library and Information Sciences Molecular Dynamics Simulation Bioinformatics Ligands 01 natural sciences Article 03 medical and health sciences Automation Aromatase SDG 3 - Good Health and Well-being 0103 physical sciences Screening method Journal Article Screening study 010304 chemical physics biology Aromatase Inhibitors Cytochrome P450 Computational Biology General Chemistry Interaction energy Chemical space 3. Good health Computer Science Applications 030104 developmental biology Workflow biology.protein Linear Models Thermodynamics Protein Binding |
| Zdroj: | Journal of Chemical Information and Modeling Journal of Chemical Information and Modeling, 57(9), 2294-2308. American Chemical Society van Dijk, M, Ter Laak, A M, Wichard, J D, Capoferri, L, Vermeulen, N P E & Geerke, D P 2017, ' Comprehensive and Automated Linear Interaction Energy Based Binding-Affinity Prediction for Multifarious Cytochrome P450 Aromatase Inhibitors ', Journal of Chemical Information and Modeling, vol. 57, no. 9, pp. 2294-2308 . https://doi.org/10.1021/acs.jcim.7b00222 |
| ISSN: | 1549-9596 |
| DOI: | 10.1021/acs.jcim.7b00222 |
| Popis: | Cytochrome P450 aromatase (CYP19A1) plays a key role in the development of estrogen dependent breast cancer, and aromatase inhibitors have been at the front line of treatment for the past three decades. The development of potent, selective and safer inhibitors is ongoing with in silico screening methods playing a more prominent role in the search for promising lead compounds in bioactivity-relevant chemical space. Here we present a set of comprehensive binding affinity prediction models for CYP19A1 using our automated Linear Interaction Energy (LIE) based workflow on a set of 132 putative and structurally diverse aromatase inhibitors obtained from a typical industrial screening study. We extended the workflow with machine learning methods to automatically cluster training and test compounds in order to maximize the number of explained compounds in one or more predictive LIE models. The method uses protein-ligand interaction profiles obtained from Molecular Dynamics (MD) trajectories to help model search and define the applicability domain of the resolved models. Our method was successful in accounting for 86% of the data set in 3 robust models that show high correlation between calculated and observed values for ligand-binding free energies (RMSE < 2.5 kJ mol(-1)), with good cross-validation statistics. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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