Determination of comprehensive in silico determinants as a strategy for identification of novel PI3Kα inhibitors
- 106 Downloads
The PI3KCA gene functions by activating cascade signaling pathways leading to cell proliferation, survival, and growth. Being one of the frequently aberrant kinase in various malignancies, isoform selectivity among kinases remains a challenging aspect. In present study, efforts have been made to conceptualize determinants that are responsible for PI3Kα inhibition. Drug design techniques such as 3D-QSAR models, e-pharmacophore models, and shape-based screening utilities were derived from set of molecules and clinical trial candidates. QSAR models were validated using structure-based cross validation technique. Further, ROC analysis and molecular dynamics simulations were performed for the selected crystal structure for its validation. Virtual screening was employed for selection of hits and based on interaction pattern, binding affinity, and energy scores three hits with central scaffold as theino[2,3-d] pyrimidine (SS-RM-03), theino[3,2-d] pyrimidine (SS-RM-04), and oxadiazole (SS-RM-05) have been identified. The screened hits were then subjected to molecular dynamics simulations and quantum mechanical calculations. Further structure-guided methodology was adopted for analyzing prominent features of the hits and was correlated using common site feature analysis. The developed models along with structural features provided by molecular dynamics simulations serve as tools for identification of structural features essential for PI3Kα inhibition. Molecular determinants using diverse in silico tools have been identified which will facilitate drug discovery programs worldwide.
KeywordsKinase Molecular dynamics pi3kα Quantum mechanics Determinants
The authors would like to thank Central University of Rajasthan for providing basic infrastructure facilities.
Ruchi Malik received research grant from DST-Rajasthan for pursuing present work acknowledgement number P.7(3) S&T/R&D/2016/2616. Shubham Srivastava received senior research fellowship from CSIR with grant number 09/1131(0014)/18-EMR-I.
Compliance with ethical standards
The authors declare that they have no competing interests.
- 5.Cortes JE, Talpaz M, O'Brien S, Faderl S, Garcia-Manero G, Ferrajoli A, Verstovsek S, Rios MB, Shan J, Kantarjian HM (2006) Staging of chronic myeloid leukemia in the imatinib era: an evaluation of the World Health Organization proposal. Cancer 106(6):1306–1315. https://doi.org/10.1002/cncr.21756 CrossRefGoogle Scholar
- 14.Woo YM, Shin Y, Lee EJ, Lee S, Jeong SH, Kong HK, Park EY, Kim HK, Han J, Chang M, Park J-H (2015) Inhibition of aerobic glycolysis represses Akt/mTOR/HIF-1α Axis and restores tamoxifen sensitivity in antiestrogen-resistant breast Cancer cells. PLoS One 10(7):e0132285. https://doi.org/10.1371/journal.pone.0132285 CrossRefGoogle Scholar
- 15.Ali K, Camps M, Pearce WP, Ji H, Rückle T, Kuehn N, Pasquali C, Chabert C, Rommel C, Vanhaesebroeck B (2008) Isoform-specific functions of phosphoinositide 3-kinases: p110δ but not p110γ promotes optimal allergic responses in vivo. J Immunol (Baltimore, Md : 1950) 180(4):2538–2544CrossRefGoogle Scholar
- 16.Jackson SP, Schoenwaelder SM, Goncalves I, Nesbitt WS, Yap CL, Wright CE, Kenche V, Anderson KE, Dopheide SM, Yuan Y, Sturgeon SA, Prabaharan H, Thompson PE, Smith GD, Shepherd PR, Daniele N, Kulkarni S, Abbott B, Saylik D, Jones C, Lu L, Giuliano S, Hughan SC, Angus JA, Robertson AD, Salem HH (2005) PI 3-kinase p110beta: a new target for antithrombotic therapy. Nat Med 11(5):507–514. https://doi.org/10.1038/nm1232 CrossRefGoogle Scholar
- 18.Park S, Chapuis N, Bardet V, Tamburini J, Gallay N, Willems L, Knight ZA, Shokat KM, Azar N, Viguie F, Ifrah N, Dreyfus F, Mayeux P, Lacombe C, Bouscary D (2008) PI-103, a dual inhibitor of class IA phosphatidylinositide 3-kinase and mTOR, has antileukemic activity in AML. Leukemia 22(9):1698–1706. https://doi.org/10.1038/leu.2008.144 CrossRefGoogle Scholar
- 20.Wallin JJ, Guan J, Prior WW, Lee LB, Berry L, Belmont LD, Koeppen H, Belvin M, Friedman LS, Sampath D (2012) GDC-0941, a novel class I selective PI3K inhibitor, enhances the efficacy of docetaxel in human breast cancer models by increasing cell death in vitro and in vivo. Clin Cancer Res 18(14):3901–3911. https://doi.org/10.1158/1078-0432.ccr-11-2088 CrossRefGoogle Scholar
- 21.Tiwary P, Mondal J, Berne BJ (2017) How and when does an anticancer drug leave its binding site? Sci Adv 3(5):e1700014. https://doi.org/10.1126/sciadv.1700014
- 23.Valsson O, Tiwary P, Parrinello M (2016) Enhancing important fluctuations: rare events and Metadynamics from a conceptual viewpoint. Annu Rev Phys Chem 67:159–184. https://doi.org/10.1146/annurev-physchem-040215-112229 CrossRefGoogle Scholar
- 24.Srivastava S, Choudhary BS, Mehta P, Sukanya SM, Malik R (2018) Molecular dynamics insights for PI3K-δ inhibition & structure guided identification of novel PI3K-δ inhibitors. J Biomol Struct Dyn:1–24. https://doi.org/10.1080/07391102.2018.1489304
- 30.Rewcastle GW, Kolekar S, Buchanan CM, Gamage SA, Giddens AC, Tsang KY, Kendall JD, Singh R, Lee WJ, Smith GC, Han W, Matthews DJ, Denny WA, Shepherd PR, Jamieson SMF (2017) Biological characterization of SN32976, a selective inhibitor of PI3K and mTOR with preferential activity to PI3Kalpha, in comparison to established pan PI3K inhibitors. Oncotarget 8(29):47725–47740. https://doi.org/10.18632/oncotarget.17730 CrossRefGoogle Scholar
- 31.Zhao Y, Zhang X, Chen Y, Lu S, Peng Y, Wang X, Guo C, Zhou A, Zhang J, Luo Y, Shen Q, Ding J, Meng L, Zhang J (2014) Crystal structures of PI3Kα complexed with PI103 and its derivatives: new directions for inhibitors design. ACS Med Chem Lett 5(2):138–142. https://doi.org/10.1021/ml400378e CrossRefGoogle Scholar
- 33.Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47(7):1739–1749. https://doi.org/10.1021/jm0306430 CrossRefGoogle Scholar
- 34.Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem 49(21):6177–6196. https://doi.org/10.1021/jm051256o CrossRefGoogle Scholar
- 36.Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA (2006) PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J Comput Aided Mol Des 20(10–11):647–671. https://doi.org/10.1007/s10822-006-9087-6 CrossRefGoogle Scholar
- 43.Bowers KJ, Chow DE, Xu H, Dror RO, Eastwood MP, Gregersen BA, Klepeis JL, Kolossvary I, Moraes MA, Sacerdoti FD, Salmon JK, Shan Y, Shaw DE (2006) Scalable algorithms for molecular dynamics simulations on commodity clusters. SC 2006 conference, proceedings of the ACM/IEEE, 11–17 Nov. 2006, pp 43–43. https://doi.org/10.1109/SC.2006.54 Google Scholar
- 44.Saurat T, Buron F, Rodrigues N, de Tauzia M-L, Colliandre L, Bourg S, Bonnet P, Guillaumet G, Akssira M, Corlu A, Guillouzo C, Berthier P, Rio P, Jourdan M-L, Bénédetti H, Routier S (2014) Design, synthesis, and biological activity of Pyridopyrimidine scaffolds as novel PI3K/mTOR dual inhibitors. J Med Chem 57(3):613–631. https://doi.org/10.1021/jm401138v CrossRefGoogle Scholar
- 45.Zhan M, Deng Y, Zhao L, Yan G, Wang F, Tian Y, Zhang L, Jiang H, Chen Y (2017) Design, synthesis, and biological evaluation of Dimorpholine substituted Thienopyrimidines as potential class I PI3K/mTOR dual inhibitors. J Med Chem 60(9):4023–4035. https://doi.org/10.1021/acs.jmedchem.7b00357 CrossRefGoogle Scholar
- 46.Morales GA, Garlich JR, Su J, Peng X, Newblom J, Weber K, Durden DL (2013) Synthesis and cancer stem cell-based activity of substituted 5-morpholino-7H-thieno[3,2-b]pyran-7-ones designed as next generation PI3K inhibitors. J Med Chem 56(5):1922–1939. https://doi.org/10.1021/jm301522m CrossRefGoogle Scholar
- 50.Bochevarov AD, Harder E, Hughes TF, Greenwood JR, Braden DA, Philipp DM, Rinaldo D, Halls MD, Zhang J, Friesner RA (2013) Jaguar: a high-performance quantum chemistry software program with strengths in life and materials sciences. Int J Quantum Chem 113(18):2110–2142. https://doi.org/10.1002/qua.24481 CrossRefGoogle Scholar