LIGAND-BASED PHARMACOPHORE MODEL AND QSAR STUDIES ON HERBICIDES TARGETING PHOTOSYSTEM II FROM CHLAMYDOMONAS REINHARDTII

The resistance of weeds is a problem which can be overcome by finding new herbicides. For this purpose, beyond the experimental methods, in silico approaches can be helpful, as a starting point. In this regard, pharmacophore mapping and 3D-QSAR studies were carried out on several series of herbicide, already known to act on the Photosystem II (PS II) D1 protein. Using PHASE software, three pharmacophore features, H-bond acceptor (A), hydrophobic (H) and aromatic ring (R) were taken into account to be the best hypothesis. For this hypothesis an atom-based 3D-QSAR model was generated with statistically significant parameters (the correlation coefficient of regression (R2) of 0.839, the standard error of estimates (SD) of 0.370, the Fisher test (F) of 53.7 for the training set, the external explained variance Q2 = 0.640, the Pearson-R = 0.916 and Root Mean Square Error (RMSE) = 0.572, for the test set). This hypothesis, validated by the 3D atom-based QSAR approach, assures the selection of novel scaffolds of herbicide derivatives and can be used for the design of new chemical entities active on the PS II D1 protein.

 The resistance of weeds is a problem which can be overcome by finding new herbicides. For this purpose, beyond the experimental methods, in silico approaches can be helpful, as a starting point.
 In this regard, pharmacophore mapping and 3D-QSAR studies were carried out on several series of herbicide, already known to act on the Photosystem II (PS II) D1 protein. Using PHASE software, three pharmacophore features, H-bond acceptor (A), hydrophobic (H) and aromatic ring (R) were taken into account to be the best hypothesis.
 For this hypothesis an atom-based 3D-QSAR model was generated with statistically significant parameters (the correlation coefficient of regression (R 2 ) of 0.839, the standard error of estimates (SD) of 0.370, the Fisher test (F) of 53.7 for the training set, the external explained variance Q 2 = 0.640, the Pearson-R = 0.916 and Root Mean Square Error (RMSE) = 0.572, for the test set).
 This hypothesis, validated by the 3D atom-based QSAR approach, assures the selection of novel scaffolds of herbicide derivatives and can be used for the design of new chemical entities active on the PS II D1 protein. Table 1. The structure of the most active compounds (1 to 8), the unaligned ligands (9 and 10) and the less active compounds (11 and 12) and their herbicidal activity in logarithmic units

Pharmacophore modeling and validation
 The "Develop Pharmacophore Model" module of Phase software [12][13][14] implemented in the Schrödinger suite was used in order to generate all possible pharmacophore hypothesis using four PLS factors. The number of PLS factors was increased, but the model statistics or predictive ability did not improve.
 The pharmacophore validation was carried out by atom-based 3D-QSAR regression including both internal and external validation. The training set includes 80% randomly selected molecules, whereas the remaining 20% were denominated to validate the model (test set). The external predictive ability for the test set prediction using Pearson-R was considered and the models which have values greater than 0.6 were selected.
Taking into account this statistical parameter but also high value of Q2 test (correlation coefficient of prediction for the test set) and R2 training (correlation coefficient for the training set) we selected the best QSAR model.  Ten pharmacophore (Table 2) Table 1. Figures 3 to  6.       Pharmacophore-based 3D-QSAR study of PSII D1 inhibitors is carried out in order to explain the structural features of some herbicide derivatives (pyrimidine, pyridine, cinnoline, triazine and quinine) required for their inhibitory activity.

 A graphical representation of the significant favourable and unfavourable features for the herbicidal activity of the compounds that resulted when the QSAR model is applied is shows in
 The selected 3D-QSAR model indicates a significant correlation and a good predictive capacity. One hydrogen bond acceptors (A), one lipophilic/hydrophobic group (H) and one aromatic ring (R), as pharmacophore features, are important for the PSII D1 herbicidal activity. The best hypothesis AHR.7, in this study, is characterized by the best values of the R 2 regression coefficient (0.839) and the highest values for the Pearson-R coefficient (0.916).
 In future studies this pharmacophore model will be used for screening molecular databases in order to find potential new herbicides.

Conclusion
This project was financially supported by Project 1.1 of the Institute of Chemistry of the Romanian Academy. The authors thank Dr. Ramona Curpăn (Institute of Chemistry Timisoara of Romanian Academy), for providing access to Schrödinger software acquired through the PN-II-RU-TE-2014-4-422 projects funded by CNCS-UEFISCDI Romania.