3D QSAR studies on protein tyrosine phosphatase 1B inhibitors: comparison of the quality and predictivity among 3D QSAR models obtained from different conformer-based alignments.
A set of 65 flexible peptidomimetic competitive inhibitors (52 in the training set and 13 in the test set) of protein tyrosine phosphatase 1B (PTP1B) has been used to compare the quality and predictive power of 3D quantitative structure-activity relationship (QSAR) comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models for the three most commonly used conformer-based alignments, namely, cocrystallized conformer-based alignment (CCBA), docked conformer-based alignment (DCBA), and global minima energy conformer-based alignment (GMCBA). These three conformers of 5-[(2S)-2-({(2S)-2-[(tert-butoxycarbonyl)amino]-3-phenylpropanoyl}amino)3-oxo-3-pentylamino)propyl]-2-(carboxymethoxy)benzoic acid (compound number 66) were obtained from the X-ray structure of its cocrystallized complex with PTP1B (PDB ID: 1JF7), its docking studies, and its global minima by simulated annealing. Among the 3D QSAR models developed using the above three alignments, the CCBA provided the optimal predictive CoMFA model for the training set with cross-validated r2 (q2)=0.708, non-cross-validated r2=0.902, standard error of estimate (s)=0.165, and F=202.553 and the optimal CoMSIA model with q2=0.440, r2=0.799, s=0.192, and F=117.782. These models also showed the best test set prediction for the 13 compounds with predictive r2 values of 0.706 and 0.683, respectively. Though the QSAR models derived using the other two alignments also produced statistically acceptable models in the order DCBA>GMCBA in terms of the values of q2, r2, and predictive r2, they were inferior to the corresponding models derived using CCBA. Thus, the order of preference for the alignment selection for 3D QSAR model development may be CCBA>DCBA>GMCBA, and the information obtained from the CoMFA and CoMSIA contour maps may be useful in designing specific PTP1B inhibitors.
Pandey
Gyanendra
G
Medicinal and Process Chemistry Division, Central Drug Research Institute, Lucknow, India.
Saxena
Anil K
AK
D002626:Chemistry, Pharmaceutical / Q000379:methods*; D003198:Computer Simulation; D018360:Crystallography, X-Ray; D004345:Drug Industry / Q000379:methods; D004791:Enzyme Inhibitors / Q000737:chemistry*; D006801:Humans; D006860:Hydrogen Bonding; D021621:Imaging, Three-Dimensional; D016018:Least-Squares Analysis; D008956:Models, Chemical; D011485:Protein Binding; D054562:Protein Tyrosine Phosphatase, Non-Receptor Type 1; D017027:Protein Tyrosine Phosphatases / Q000037:antagonists & inhibitors*; D021281:Quantitative Structure-Activity Relationship; D055672:Static Electricity; D014965:X-Rays
D004791:Enzyme Inhibitors; C516607:PTPN1 protein, human; D054562:Protein Tyrosine Phosphatase, Non-Receptor Type 1; D017027:Protein Tyrosine Phosphatases