In silico Design of Phosphonic Arginine and Hydroxamic Acid Inhibitors of Plasmodium falciparum M17 Leucyl Aminopeptidase with Favorable Pharmacokinetic Profile

摘要

We virtually design here new subnanomolar range antimalarials, inhibitors of plasmodium falciparum M17 Aminopeptidase (pfA-M17), by means of structure-based molecular design. Complexation QSAR models were elaborated for two training sets (6 methylphosphonic acids (APP) resp. 13 Hydroxamic Acid derivatives (AHO): QSAR_(APP). resp. QSAR_(AHO)) and a linear correlation was established between the computed Gibbs free energies of binding (GFE: ΔΔG_(com)) and observed enzyme inhibition constants (K_i~(exp)) for each training set: QSAR_(APP): pK_i~(exp)=-0.1665×ΔΔG_(com)+7.9581, R~2=0.97 resp. QSAR_(AHO): pK_i~(exp)=-0.4626×ΔΔG_(com)+8.1842, R~2=0.98. The predictive power of the QSAR models was validated with 3D-QSAR pharmacophore generation (PH4): PH4_(APP): pK_i~(exp)=0.99677×pK_i~(pred)- 0.00457, R~2=0.99 resp. PH4_(AHO): pK_i~(exp)=1.02016×pK_i~(pred)-0.10478, R~2=0.99. Breakdown of computed pfA-M17:APPs resp. pfA-M17:AHOs interaction energy into each active site residue's contribution provided additional helpful structural information to design new APP and AHO analogues in a consistent way. In a first step we designed a virtual library (VL_(APP) resp. VL_(AHO)) from P_1 and P'_1 substitutions to explore both S_1 and S'_1 pockets. Further the VLs screened with the 3D-QSAR PH4s and the K_i~(pred) of the best fit hits virtually evaluated with QSAR_(APP) resp. QSAR_(AHO) models. This approach combining use of molecular modeling, PH4 and in silico VL screening helpfully provided valuable structural information for the synthesis of novel pfA-M17 inhibitors.