Investigation of indirubin derivatives: a combination of 3D-QSAR, molecular docking, and ADMET towards the design of new DRAK2 inhibitors
Kinase-related apoptosis-inducing kinase 2 (DRAK2) is a serine/threonine kinase and belongs to the death-associated protein kinase DPAK family, which is responsible for induction of apoptosis in many cell types. Thus, DRAK2 is regarded as a promising target for treatment of autoimmune diseases. To investigate the binding between DRAK2 and indirubin inhibitors and design potent inhibitors, a three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking were performed. Comparative Molecular Similarity Indices Analysis (CoMSIA) was developed using 33 molecules having pIC50 ranging from 8.523 to 5.000 (IC50 in nM). The best CoMSIA model gave a significant coefficient of determination (R2 = 0.93), as well as a (leave-one-out cross-validation coefficient Q2 of 0.81. The predictive ability of this model was evaluated by external validation using a test set of eight compounds and yielded a predicted coefficient of determination R2test of 0.94. The contour maps could provide structural features to improve inhibitory activity. Good consistency between contour maps and molecular docking strongly suggests that the molecular modeling is reliable. Based on these satisfactory results, we designed several new DRAK2 inhibitors and their inhibitory activities were predicted using different models, which are developed on different training and test sets. Additionally, these newly designed inhibitors showed promising results in the preliminary in silico ADMET evaluations compared to the best inhibitor from the studied dataset. This study could be useful in lead identification and optimization for early drug discovery of DRAK2 inhibitors.
KeywordsQSAR Molecular docking DRAK2 Drug design Indirubin In silico ADMET
We are grateful to the “Association Marocaine des Chimistes Théoriciens” (AMCT) and “Moroccan Centre of Scientific and Technique research” (CNRST) for their pertinent help concerning the programs.
Compliance with ethical standards
The authors declare that they have no competing interests.
- 14.Roy PP, Paul S, Mitra I, Roy K (2009) Models. 1660–1701. doi: https://doi.org/10.3390/molecules14051660
- 19.Kubinyi H (2003) Comparative molecular field analysis (CoMFA). Handb Chemoinformatics:1555–1574. https://doi.org/10.1002/9783527618279.ch44d
- 26.Dassault Systèmes BIOVIA Discovery Studio Modeling Environment.Google Scholar
- 27.Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN (2000) PEB. The Protein Data Bank 28:235–242Google Scholar
- 29.Hickey JP, Passino-reader DR (1991) Linear solvation energy relationships: “rules of thumb” for estimation of variable values 25:1753–1760Google Scholar
- 31.Matthews DJ, Gerritsen ME (2010) Targeting protein kinases for cancer therapy. John Wiley & Sons, HobokenGoogle Scholar
- 33.Pires DE V, Blundell TL, Ascher DB pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. doi: https://doi.org/10.1021/acs.jmedchem.5b00104