Receptor-based 3D QSAR analysis of estrogen receptor ligands--merging the accuracy of receptor-based alignments with the computational efficiency of ligand-based methods.
One of the major challenges in computational approaches to drug design is the accurate prediction of binding affinity of biomolecules. In the present study several prediction methods for a published set of estrogen receptor ligands are investigated and compared. The binding modes of 30 ligands were determined using the docking program AutoDock and were compared with available X-ray structures of estrogen receptor-ligand complexes. On the basis of the docking results an interaction energy-based model, which uses the information of the whole ligand-receptor complex, was generated. Several parameters were modified in order to analyze their influence onto the correlation between binding affinities and calculated ligand-receptor interaction energies. The highest correlation coefficient (r2 = 0.617, q2Loo = 0.570) was obtained considering protein flexibility during the interaction energy evaluation. The second prediction method uses a combination of receptor-based and 3D quantitative structure-activity relationships (3D QSAR) methods. The ligand alignment obtained from the docking simulations was taken as basis for a comparative field analysis applying the GRID/GOLPE program. Using the interaction field derived with a water probe and applying the smart region definition (SRD) variable selection, a significant and robust model was obtained (r2 = 0.991, q2LOO = 0.921). The predictive ability of the established model was further evaluated by using a test set of six additional compounds. The comparison with the generated interaction energy-based model and with a traditional CoMFA model obtained using a ligand-based alignment (r2 = 0.951, q2L00 = 0.796) indicates that the combination of receptor-based and 3D QSAR methods is able to improve the quality of the underlying model.
Institute for Pharmaceutical Chemistry, Heinrich-Heine-University Düsseldorf, Germany. sippl@pharm.uni-duesseldorf.de
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D001665:Binding Sites; D003198:Computer Simulation; D015195:Drug Design; D004967:Estrogens / Q000737:chemistry / Q000378:metabolism; D007700:Kinetics; D008024:Ligands; D008958:Models, Molecular; D008968:Molecular Conformation; D011487:Protein Conformation; D021281:Quantitative Structure-Activity Relationship; D011960:Receptors, Estrogen / Q000737:chemistry* / Q000378:metabolism*; D012984:Software; D013816:Thermodynamics
D004967:Estrogens; D008024:Ligands; D011960:Receptors, Estrogen