Using DFT methodology for more reliable predictive models: Design of inhibitors of Golgi α-Mannosidase II
| Autor: | Adela Bobovská, Igor Tvaroška, Juraj Kóňa |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Models
Molecular Quantitative structure–activity relationship Golgi Apparatus Quantitative Structure-Activity Relationship Mannosidase II Computational biology Selective inhibition Ligands 010402 general chemistry 01 natural sciences Force field (chemistry) alpha-Mannosidase Computational chemistry Neoplasms Materials Chemistry Humans Enzyme Inhibitors Physical and Theoretical Chemistry Spectroscopy Binding Sites 010405 organic chemistry Chemistry Zinc ion Computer Graphics and Computer-Aided Design 0104 chemical sciences Docking (molecular) Drug Design Quantum Theory |
| Zdroj: | Journal of Molecular Graphics and Modelling. 66:47-57 |
| ISSN: | 1093-3263 |
| DOI: | 10.1016/j.jmgm.2016.03.004 |
| Popis: | Human Golgi α-mannosidase II (GMII), a zinc ion co-factor dependent glycoside hydrolase (E.C.3.2.1.114), is a pharmaceutical target for the design of inhibitors with anti-cancer activity. The discovery of an effective inhibitor is complicated by the fact that all known potent inhibitors of GMII are involved in unwanted co-inhibition with lysosomal α-mannosidase (LMan, E.C.3.2.1.24), a relative to GMII. Routine empirical QSAR models for both GMII and LMan did not work with a required accuracy. Therefore, we have developed a fast computational protocol to build predictive models combining interaction energy descriptors from an empirical docking scoring function (Glide-Schrodinger), Linear Interaction Energy (LIE) method, and quantum mechanical density functional theory (QM-DFT) calculations. The QSAR models were built and validated with a library of structurally diverse GMII and LMan inhibitors and non-active compounds. A critical role of QM-DFT descriptors for the more accurate prediction abilities of the models is demonstrated. The predictive ability of the models was significantly improved when going from the empirical docking scoring function to mixed empirical-QM-DFT QSAR models (Q2 = 0.78–0.86 when cross-validation procedures were carried out; and R2 = 0.81–0.83 for a testing set). The average error for the predicted ΔGbind decreased to 0.8–1.1 kcal mol−1. Also, 76–80% of non-active compounds were successfully filtered out from GMII and LMan inhibitors. The QSAR models with the fragmented QM-DFT descriptors may find a useful application in structure-based drug design where pure empirical and force field methods reached their limits and where quantum mechanics effects are critical for ligand-receptor interactions. The optimized models will apply in lead optimization processes for GMII drug developments. |
| Databáze: | OpenAIRE |
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