3D Pharmacophore-Based Virtual Screening and Docking Approaches toward the Discovery of Novel HPPD Inhibitors.

p-Hydroxyphenylpyruvate dioxygenase (HPPD) is not only the useful molecular target in treating life-threatening tyrosinemia type I, but also an important target for chemical herbicides. A combined in silico structure-based pharmacophore and molecular docking-based virtual screening were performed to identify novel potential HPPD inhibitors. The complex-based pharmacophore model (CBP) with 0.721 of ROC used for screening compounds showed remarkable ability to retrieve known active ligands from among decoy molecules. The ChemDiv database was screened using CBP-Hypo2 as a 3D query, and the best-fit hits subjected to molecular docking with two methods of LibDock and CDOCKER in Accelrys Discovery Studio 2.5 (DS 2.5) to discern interactions with key residues at the active site of HPPD. Four compounds with top rankings in the HipHop model and well-known binding model were finally chosen as lead compounds with potential inhibitory effects on the active site of target. The results provided powerful insight into the development of novel HPPD inhibitors herbicides using computational techniques.

Quantitative Structure Activity Relationship Studies and Molecular Dynamics Simulations of 2-(Aryloxyacetyl)cyclohexane-1,3-Diones Derivatives as 4-Hydroxyphenylpyruvate Dioxygenase Inhibitors.

4-Hydroxyphenylpyruvate dioxygenase (HPPD) is a significant enzyme in the biosynthesis of plastoquinone and tocopherol. Moreover, it is also a potential target to develop new herbicide. The technology of computer-aided drug design (CADD) is a useful tool in the efficient discovery of new HPPD inhibitors. Forty-three compounds with known activities were used to generate comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models based on common framework and molecular docking. The structural contribution to the activity was determined, which provided further information for the design of novel inhibitors. Molecular docking was used to explain the changes in activity caused by the binding mode between ligand and protein. The molecular dynamics (MD) results indicated that the electrostatic energy was the major driving force for ligand-protein interaction and the Phe403 made the greatest contribution to the binding. The present work has provided useful information for the rational design of novel HPPD inhibitors with improved activity.

Combination of Virtual Screening Protocol by in Silico toward the Discovery of Novel 4-Hydroxyphenylpyruvate Dioxygenase Inhibitors.

4-Hydroxyphenylpyruvate dioxygenase (EC 1.13.11.27, HPPD) is a potent new bleaching herbicide target. Therefore, in silico structure-based virtual screening was performed in order to speed up the identification of promising HPPD inhibitors. In this study, an integrated virtual screening protocol by combining 3D-pharmacophore model, molecular docking and molecular dynamics (MD) simulation was established to find novel HPPD inhibitors from four commercial databases. 3D-pharmacophore Hypo1 model was applied to efficiently narrow potential hits. The hit compounds were subsequently submitted to molecular docking studies, showing four compounds as potent inhibitor with the mechanism of the Fe(II) coordination and interaction with Phe360, Phe403, and Phe398. MD result demonstrated that nonpolar term of compound 3881 made great contributions to binding affinities. It showed an IC50 being 2.49 µM against AtHPPD in vitro. The results provided useful information for developing novel HPPD inhibitors, leading to further understanding of the interaction mechanism of HPPD inhibitors.

New Research for Quinazoline-2,4-diones as HPPD Inhibitors Based on 2D-MLR and 3D-QSAR Models.

AIM AND OBJECTIVE: 4-Hydroxyphenylpyruvate dioxygenase (HPPD), converting phydroxyphenylpyruvate (HPPA) to homogentisate (HGA), is an important target for treating type I tyrosinemia and synthesizing novel herbicides due to its significant role in tyrosine catabolism. Hence, it is imperative to design novel HPPD inhibitors that can block HPPA-HGA conversion, which leads to the deficiency in isoprenoid redox cofactors such as plastoquinone and tocopherol, and finally caused growth inhibition. This study was undertaken to investigate structural requirements for their HPPD inhibition with better biological activity. MATERIALS AND METHODS: Based on the structure-activity relationships, a series of quinolinone-2,4- diones derivatives were studied using combined of 2D multiple linear regression (2D-MLR) and 3D quantitative structure-activity relationship (3D-QSAR). Firstly, genetic algorithm (GA) was applied and descriptors generated in DRAGON 5.5 software were used for building 2D-MLR models in the QSARINGS. Then CoMFA and CoMSIA models were performed by using alignment of the common framework and the pharmacophore model. The obtained models were validated through internal and external validation to verify predictive abilities. Especially, the CoMFA and CoMSIA contour maps were used to show vital structural characteristics related to HPPD inhibitors activities. RESULTS: The 2D-MLR liner equation and corresponding parameters were listed as follows: pKi = -38.2034Me+22.4078GATS2m-1.4265EEig15r-2.1849Hy+32.9158 ntr=28, npred=6, R2=0.863, Q2LOO=0.787, Q2LMO=0.607, Q2F1=0.780, Q2F2=0.780, Q2F3=0.860, CCCpred=0.920. RMSEtr=0.253, RMSEpred=0.555, F=36.289 The steric contours graph indicated that small and negative electrostatic substitutions at R1 and R2 regions were favorable for the better activity, and hydrogen-bond donors at this region would also increase the activity. Positive electrostatic and bulky substitutions in the R3 position would enhance the activity. The analysis of these models suggested that the steric factor of R4 position was crucial for activity of quinazoline-2,4-diones HPPD inhibitors, bulky substitutions might improve the bioactivity of these inhibitors greatly, meanwhile, hydrogen-bond acceptor groups in this position were required for higher activity. CONCLUSION: In this study, a combined 2D-MLR, CoMFA and CoMSIA models demonstrated satisfying results through internal and external validation, especially good predictive abilities and the CoMFA and CoMSIA contour maps showed vital structural characteristics related to HPPD inhibitors activities.