Docking and 3D QSAR studies of protoporphyrinogen oxidase inhibitor 3H-pyrazolo[3,4-d][1,2,3]triazin-4-one derivatives

AbstractDocking and three dimensional quantitative structure - activity relationship (3D-QSAR) studies have been performed for protoporphyrinogen oxidase (PPO) inhibitor 3H-pyrazolo[3,4-d][1,2,3]triazin-4-one analogues which are potential herbicides to protect agricultural products from unwanted weeds. The 3D-QSAR studies have been carried out using shape, spatial, electronic and molecular field descriptors along with a few structural parameters. The chemometric tools used for the analyses are genetic function approximation (GFA), partial least squares (PLS) and genetic partial least squares (G/PLS). The whole data set (n = 34) was divided into a training set (75% of the data set) and a test set (remaining 25%) on the basis of K-means clustering technique applied on topological, spatial and electronic descriptor matrix. Models developed from the training set were used to predict the activity of the test set compounds. All the models have been validated internally, externally and by Y-randomization technique. Docking studies suggest that the molecules bind with a hydrophobic pocket of the enzyme formed by some nonpolar amino acid (Ile168, Ile311, Ile412, Met365, Phe65 and Val164) residues. The QSAR studies suggest that for better activity the molecules should have symmetrical shape in the 3D space. For better PPO inhibitory activity, there should be a balance between the electrophilic and nucleophilic characters of the inhibitors. The charged surface area descriptors suggest that, the positive charge distributed over a large surface area may enhance the activity. Molecular field probes reflect that increase in steric volume and positively charged surface area may enhance the herbicidal activity. FigureDocking and three dimensional quantitative structure activity relationship (3D-QSAR) studies have been performed for protoporphyrinogen oxidase (PPO) inhibitor 3H-pyrazolo[3,4-d][1,2,3]triazin-4-one analogues

[1]  S. Ekins,et al.  Three-Dimensional Quantitative Structure-Activity Relationship Analysis of Human CYP51 Inhibitors , 2007, Drug Metabolism and Disposition.

[2]  S. Deswal,et al.  Quantitative structure activity relationship studies of aryl heterocycle-based thrombin inhibitors. , 2006, European journal of medicinal chemistry.

[3]  Kunal Roy,et al.  On Selection of Training and Test Sets for the Development of Predictive QSAR models , 2006 .

[4]  B. Everitt,et al.  Cluster Analysis: Low Temperatures and Voting in Congress , 2001 .

[5]  P. Roy,et al.  Exploring the impact of size of training sets for the development of predictive QSAR models , 2008 .

[6]  Kunal Roy,et al.  On some aspects of validation of predictive quantitative structure–activity relationship models , 2007, Expert opinion on drug discovery.

[7]  Kunal Roy,et al.  Comparative QSAR Studies of CYP1A2 Inhibitor Flavonoids Using 2D and 3D Descriptors , 2008, Chemical biology & drug design.

[8]  J N Weinstein,et al.  Quantitative structure-antitumor activity relationships of camptothecin analogues: cluster analysis and genetic algorithm-based studies. , 2001, Journal of medicinal chemistry.

[9]  Paul S. Charifson,et al.  Practical Application of Computer-Aided Drug Design , 1997 .

[10]  Jian Wan,et al.  Molecular docking and three-dimensional quantitative structure-activity relationship studies on the binding modes of herbicidal 1-(substituted phenoxyacetoxy)alkylphosphonates to the E1 component of pyruvate dehydrogenase. , 2007, Journal of agricultural and food chemistry.

[11]  Guang-Fu Yang,et al.  Development of quantitative structure-activity relationships and its application in rational drug design. , 2006, Current pharmaceutical design.

[12]  Peter Kirkpatrick,et al.  Virtual screening: Gliding to success , 2004, Nature Reviews Drug Discovery.

[13]  Jian Wan,et al.  A DFT-based QSARs study of protoporphyrinogen oxidase inhibitors: phenyl triazolinones. , 2004, Bioorganic & medicinal chemistry.

[14]  Anton J. Hopfinger,et al.  Application of Genetic Function Approximation to Quantitative Structure-Activity Relationships and Quantitative Structure-Property Relationships , 1994, J. Chem. Inf. Comput. Sci..

[15]  Brian Everitt,et al.  Cluster analysis , 1974 .

[16]  A. Tropsha,et al.  Beware of q2! , 2002, Journal of molecular graphics & modelling.

[17]  K. Grossmann,et al.  Protoporphyrinogen oxidase-inhibiting activity of the new, wheat-selective isoindoldione herbicide, cinidon-ethyl , 1999 .

[18]  Hugo Kubinyi,et al.  3D QSAR in drug design : theory, methods and applications , 2000 .

[19]  Roberto Marcondes Cesar Junior,et al.  Inference from Clustering with Application to Gene-Expression Microarrays , 2002, J. Comput. Biol..

[20]  H. Kubinyi,et al.  Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices. , 1998, Journal of medicinal chemistry.

[21]  Ning Ma,et al.  Structure-activity relationships for a new family of sulfonylurea herbicides , 2005, J. Comput. Aided Mol. Des..

[22]  P. Roy,et al.  On Some Aspects of Variable Selection for Partial Least Squares Regression Models , 2008 .

[23]  Han van de Waterbeemd,et al.  Chemometric methods in molecular design , 1995 .

[24]  Zhen Xi,et al.  Development of a general quantum‐chemical descriptor for steric effects: Density functional theory based QSAR study of herbicidal sulfonylurea analogues , 2006, J. Comput. Chem..

[25]  Kunal Roy,et al.  Exploring 2D and 3D QSARs of 2,4-diphenyl-1,3-oxazolines for ovicidal activity against Tetranychus urticae , 2009 .

[26]  Rajarshi Guha,et al.  Determining the Validity of a QSAR Model - A Classification Approach , 2005, J. Chem. Inf. Model..

[27]  Kenji Hirai,et al.  Herbicide Classes in Development , 2002 .

[28]  Bin Liu,et al.  Novel protoporphyrinogen oxidase inhibitors: 3H-pyrazolo[3,4-d][1,2,3]triazin-4-one derivatives. , 2008, Journal of agricultural and food chemistry.