Comparative Three-Dimensional Quantitative Structure−Activity Relationship Study of Safeners and Herbicides

The competitive antagonist hypothesis for safeners and herbicides was investigated by studying the 3D similarity between 28 safener and 20 herbicide molecules in their putative biologically active, low-energy conformations using comparative molecular field analysis (CoMFA). In addition, CoMFA provid...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of agricultural and food chemistry 2000-03, Vol.48 (3), p.926-931
Hauptverfasser: Bordás, Barna, Kömíves, Tamás, Szántó, Zoltán, Lopata, Antal
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The competitive antagonist hypothesis for safeners and herbicides was investigated by studying the 3D similarity between 28 safener and 20 herbicide molecules in their putative biologically active, low-energy conformations using comparative molecular field analysis (CoMFA). In addition, CoMFA provided information about the structural requirements for the interactions of safeners and herbicides with a proteinaceous component (SafBP) isolated from etiolated corn seedlings. Statistically significant CoMFA models have been developed for the united and separate safener and herbicide molecule sets using retrospective binding affinity data of the ligands measured at the SafBP receptor. The predictive power of the models was characterized by squared cross-validated correlation coefficients (q 2) of 0.708, 0.564, and 0.4000 for the united safener plus herbicide set, the safener set, and the herbicide set, respectively. The CoMFA results support the competitive antagonist hypothesis between certain types of safeners and herbicides. The findings suggest that structural similarity between these two classes of agrochemicals is a useful guide in the design of new safeners. Keywords: Safener; herbicide; QSAR; comparative molecular field analysis; CoMFA
ISSN:0021-8561
1520-5118
DOI:10.1021/jf990395+