4D-LQTA-QSAR and docking study on potent gram-negative specific LpxC inhibitors: a comparison to CoMFA modeling

A quasi 4D-QSAR has been carried out on a series of potent Gram-negative LpxC inhibitors. This approach makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package. This new methodology is based on the generation of a conformational ensemble profile, CEP, for each compound instead of only one conformation, followed by the calculation intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are independent variables employed in a QSAR analysis. The comparison of the proposed methodology to comparative molecular field analysis (CoMFA) formalism was performed. This methodology explores jointly the main features of CoMFA and 4D-QSAR models. Step-wise multiple linear regression was used for the selection of the most informative variables. After variable selection, multiple linear regression (MLR) and partial least squares (PLS) methods used for building the regression models. Leave-N-out cross-validation (LNO), and Y-randomization were performed in order to confirm the robustness of the model in addition to analysis of the independent test set. Best models provided the following statistics : $${{R}^{2}= 0.943, {q}^{2}_{\rm LOO}= 0.802, {q}^{2}_{\rm LNO}= 0.798, {R}^{2}_{\rm Pred}= 0.936}$$ (PLS) and $${{R}^{2 }= 0.948, {q}^{2}_{\rm LOO}= 0.823, {q}^{2}_{\rm LNO}= 0.818, {R}^{2}_{\rm Pred} = 0.928}$$ (MLR). Docking study was applied to investigate the major interactions in protein–ligand complex with CDOCKER algorithm. Visualization of the descriptors of the best model helps us to interpret the model from the chemical point of view, supporting the applicability of this new approach in rational drug design.