3D-QSAR predictions for α-cyclodextrin binding constants using quantum mechanically based descriptors. (February 2017)

• Record Type: Journal Article
• Authors: Linden, Lukas; Goss, Kai-Uwe; Endo, Satoshi
• Is Part Of: Chemosphere. Volume 169(2017)
• Page Start: 693
• Page End: 699
• Publication Date: 2017-02
• Subjects: α-Cyclodextrin (CD) -- Binding constant -- Inclusion complex -- Prediction; αCD α-cyclodextrin -- QSAR quantitative structure activity relationship -- LSPs local sigma profiles -- COSMO conductor-like screening model -- CoMFA comparative molecular field analysis -- MIFs molecular interaction fields -- PLS partial least square -- COSMO-RS conductor-like screening model for real solvents -- LSER linear solvation energy relationship -- pp-LFER poly-parameter linear free energy relationship -- O3A Open3DALIGN -- MDsim molecular dynamics simulation -- ele electrostatic; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2016.11.115
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: Binding of organic chemicals to α-cyclodextrin (αCD) is a typical example for host-guest complexation that is influenced by the 3D-structure of both the binding site (host) and the solute (guest). Prediction of the binding constant is challenging and requires a successful representation of the binding site-solute interactions in the 3D-space. In this study, we tested if a 3D quantitative structure activity relationship (3D-QSAR) model with quantum mechanically based local sigma profiles (LSPs) derived from the COSMOsar3D method is capable of predicting αCD binding constants from the most recent literature and how the model performs in comparison to a standard comparative molecular field analysis and to a reference 2D-QSAR. The results showed that the new 3D-QSAR model was more predictive than both reference models (RMSE 0.45 vs 0.53/0.52, R 2 0.70 vs 0.53/0.68). Furthermore, only the new model captured the differences in the binding constants between structural isomers of aliphatic alcohols and allowed an extrapolation of the prediction to another literature data set. The high performance of the 3D-QSAR model with LSPs tested in this study and its theoretical robustness suggest that this modeling approach should be applicable to other binding processes including protein binding. Highlights: Successful prediction of α-cyclodextrin binding constants with a new 3D-QSAR model. The 3D-QSAR model uses local sigma profiles that emerge from the COSMO-RS theory. Comparison of the new 3D-QSAR model with two standard models. Accurate predictions of steric effects that influence the binding by the new model. Validation of the modeling approaches with a literature data set.