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. 2019 Oct;26(10):1156-1167.
doi: 10.1089/cmb.2019.0052. Epub 2019 Jun 24.

Curcumin-Synthetic Analogs Library Screening by Docking and Quantitative Structure-Activity Relationship Studies for AXL Tyrosine Kinase Inhibition in Cancers

Fatima Ghrifi  1 , Loubna Allam  1 , Lakhlili Wiame  1 , Azeddine Ibrahimi  1
Affiliations

Curcumin-Synthetic Analogs Library Screening by Docking and Quantitative Structure-Activity Relationship Studies for AXL Tyrosine Kinase Inhibition in Cancers

Fatima Ghrifi et al. J Comput Biol. 2019 Oct.

Abstract

AXL is an important drug target for cancers. Two-dimensional quantitative structure-activity relationship (2D-QSAR) tests were performed to elucidate a relationship between molecular structures and the activity of a series of 400 curcumin derivatives subjected to AXL kinase by ATP competition in the catalytic site. The partial least square regression method implanted in molecular operating environment software was applied to develop QSAR models, which were further validated for statistical significance by internal and external validation. The best model has proven to be statistically robust with a good predictive correlation ofR2 = 0.996 and a significant cross-validation correlation coefficient ofq2 = 0.707. Docking analysis reveled that three curcumin derivatives have the best affinity for AXL and formed a hydrogen bond with the important amino acid residues in the binding pocket. As treated in this article, the docking studies and 2D-QSAR approach will pave the way for the development of new drugs while highlighting curcumin and its derivatives.

Keywords: 2D-QSAR; AXL kinase; internal and external validation; partial least square regression.

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Conflict of interest statement

The authors declare there are no competing financial interests.

Figures

<b>FIG. 1.</b>
FIG. 1.
The (2D) descriptors proposed by “QuaSAR Contingency module” application in MOE software. MOE, molecular operating environment.
<b>FIG. 2.</b>
FIG. 2.
Fitting the dependent variable (pIC50) to the independent variables (descriptors) of the training set in the MOE “QuasSAR-Model” to generate the quantitative structure–activity relationship model and the parameters of validation. Note that the gray circle surrounds the validation parameters (R2 and RMSE), whereas the black circle surrounds the cross-validation parameters. RMSE, root-mean squared error.
<b>FIG. 3.</b>
FIG. 3.
Plot of predicted pIC50 values versus experimental ones for the PLS model. Note the compound of training set in dots and prediction set in square. PLS, partial least square.
<b>FIG. 4.</b>
FIG. 4.
(a) All interactions of CID 10765707 against AXL protein. (b) All interactions of CID 11257493 against AXL protein. (c) All interactions of CID 21159180 against AXL protein.
See this image and copyright information in PMC

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