J Chem Inf Model - Development of novel 3D-QSAR combination approach for screening and optimizing B-Raf inhibitors in silico.

Tópicos

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Resumo

B-Raf is a member of the RAF family of serine/threonine kinases: it mediates cell division, differentiation, and apoptosis signals through the RAS-RAF-MAPK pathway. Thus, B-Raf is of keen interest in cancer therapy, such as melanoma. In this study, we propose the first combination approach to integrate the pharmacophore (PhModel), CoMFA, and CoMSIA models for B-Raf, and this approach could be used for screening and optimizing potential B-Raf inhibitors in silico. Ten PhModels were generated based on the HypoGen BEST algorithm with the flexible fit method and diverse inhibitor structures. Each PhModel was designated to the alignment rule and screening interface for CoMFA and CoMSIA models. Therefore, CoMFA and CoMSIA models could align and recognize diverse inhibitor structures. We used two quality validation methods to test the predication accuracy of these combination models. In the previously proposed combination approaches, they have a common factor in that the number of training set inhibitors is greater than that of testing set inhibitors. In our study, the 189 known diverse series B-Raf inhibitors, which are 7-fold the number of training set inhibitors, were used as a testing set in the partial least-squares validation. The best validation results were made by the CoMFA09 and CoMSIA09 models based on the Hypo09 alignment model. The predictive r(2)(pred) values of 0.56 and 0.56 were derived from the CoMFA09 and CoMSIA09 models, respectively. The CoMFA09 and CoMSIA09 models also had a satisfied predication accuracy of 77.78% and 80%, and the goodness of hit test score of 0.675 and 0.699, respectively. These results indicate that our combination approach could effectively identify diverse B-Raf inhibitors and predict the activity.

Resumo Limpo

braf member raf famili serinethreonin kinas mediat cell divis differenti apoptosi signal rasrafmapk pathway thus braf keen interest cancer therapi melanoma studi propos first combin approach integr pharmacophor phmodel comfa comsia model braf approach use screen optim potenti braf inhibitor silico ten phmodel generat base hypogen best algorithm flexibl fit method divers inhibitor structur phmodel design align rule screen interfac comfa comsia model therefor comfa comsia model align recogn divers inhibitor structur use two qualiti valid method test predic accuraci combin model previous propos combin approach common factor number train set inhibitor greater test set inhibitor studi known divers seri braf inhibitor fold number train set inhibitor use test set partial leastsquar valid best valid result made comfa comsia model base hypo align model predict rpred valu deriv comfa comsia model respect comfa comsia model also satisfi predic accuraci good hit test score respect result indic combin approach effect identifi divers braf inhibitor predict activ

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