3D QSAR Pharmacophore Based Virtual Screening for Identification of Potential Inhibitors for CDC25B

Owing to its fundamental roles in cell cycle phases, the cell division cycle 25B (CDC25B) was broadly considered as potent clinical drug target for cancers. In this study, 3D QSAR pharmacophore models for CDC25B inhibitors were developed by the module of Hypogen. Three methods (cost analysis, test set prediction, and Fisher's test) were applied to validate that the models could be used to predict the biological activities of compounds. Subsequently, 26 compounds satisfied Lipinski's rule of five were obtained by the virtual screening of the Hypo-1-CDC25B against ZINC databases. It was then discovered that 9 identified molecules had better binding affinity than a known CDC25B inhibitors-compound 1 using docking studies. The molecular dynamics simulations showed that the compound had favorable conformations for binding to the CDC25B. Thus, our findings here would be helpful to discover potent lead compounds for the treatment of cancers.

[1]  Wei Yang,et al.  Computer-aided Drug Design: Using Numbers to your Advantage. , 2013, ACS medicinal chemistry letters.

[2]  B Rubin,et al.  Design of specific inhibitors of angiotensin-converting enzyme: new class of orally active antihypertensive agents. , 1977, Science.

[3]  M. Diederich,et al.  Development of a matrix-assisted laser desorption/ionization-mass spectrometry screening test to evidence reversible and irreversible inhibitors of CDC25 phosphatases. , 2012, Analytical biochemistry.

[4]  Yeng-Tseng Wang,et al.  Finding a Potential Dipeptidyl Peptidase-4 (DPP-4) Inhibitor for Type-2 Diabetes Treatment Based on Molecular Docking, Pharmacophore Generation, and Molecular Dynamics Simulation , 2016, International journal of molecular sciences.

[5]  Bernard Ducommun,et al.  The when and wheres of CDC25 phosphatases. , 2006, Current opinion in cell biology.

[6]  Yu Zong Chen,et al.  Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness. , 2007, Drug discovery today.

[7]  M. Pirrung,et al.  Inhibition of Cdc25 phosphatases by indolyldihydroxyquinones. , 2003, Journal of medicinal chemistry.

[8]  Berk Hess,et al.  P-LINCS:  A Parallel Linear Constraint Solver for Molecular Simulation. , 2008, Journal of chemical theory and computation.

[9]  P. Wipf,et al.  Independent Mechanistic Inhibition of Cdc25 Phosphatases by a Natural Product Caulibugulone , 2007, Molecular Pharmacology.

[10]  S. Longhorn,et al.  Revised generic placement of Brachypelma embrithes (Chamberlin & Ivie, 1936) and Brachypelma angustum Valerio, 1980, with definition of the taxonomic features for identification of female Sericopelma Ausserer, 1875 (Araneae, Theraphosidae) , 2015, ZooKeys.

[11]  Charles L. Brooks,et al.  Detailed analysis of grid‐based molecular docking: A case study of CDOCKER—A CHARMm‐based MD docking algorithm , 2003, J. Comput. Chem..

[12]  Lisa Yan,et al.  The dominant role of side‐chain backbone interactions in structural realization of amino acid code. ChiRotor: A side‐chain prediction algorithm based on side‐chain backbone interactions , 2007, Protein science : a publication of the Protein Society.

[13]  Tamanna Gandhi,et al.  Exploring QSTR and toxicophore of hERG K+ channel blockers using GFA and HypoGen techniques. , 2008, Journal of molecular graphics & modelling.

[14]  A. W. Schüttelkopf,et al.  PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. , 2004, Acta crystallographica. Section D, Biological crystallography.

[15]  J. Rudolph,et al.  Cdc25 phosphatases and cancer. , 2004, Chemistry & biology.

[16]  M. Jacobson,et al.  Molecular mechanics methods for predicting protein-ligand binding. , 2006, Physical chemistry chemical physics : PCCP.

[17]  N. Gresh,et al.  Novel naphthoquinone and quinolinedione inhibitors of CDC25 phosphatase activity with antiproliferative properties. , 2008, Bioorganic & medicinal chemistry.

[18]  H Verli,et al.  GROMOS96 43a1 performance in predicting oligosaccharide conformational ensembles within glycoproteins. , 2010, Carbohydrate research.

[19]  Naresh Kandakatla,et al.  Ligand Based Pharmacophore Modeling and Virtual Screening Studies to Design Novel HDAC2 Inhibitors , 2014, Adv. Bioinformatics.

[20]  Alexander Tropsha,et al.  Modeling of p38 mitogen-activated protein kinase inhibitors using the Catalyst HypoGen and k-nearest neighbor QSAR methods. , 2004, Journal of molecular graphics & modelling.

[21]  Santosh A. Khedkar,et al.  Successful applications of computer aided drug discovery: moving drugs from concept to the clinic. , 2010, Current topics in medicinal chemistry.

[22]  H. Sasaki,et al.  Expression of the cdc25B gene as a prognosis marker in non-small cell lung cancer. , 2001, Cancer letters.

[23]  Y. Liu,et al.  Discovery and structural optimization of pyrazole derivatives as novel inhibitors of Cdc25B. , 2010, Bioorganic & medicinal chemistry letters.

[24]  Zhon-Yin Zhang,et al.  Small molecule tools for functional interrogation of protein tyrosine phosphatases , 2013, The FEBS journal.

[25]  P. Wipf,et al.  Computational design, synthesis and biological evaluation of para-quinone-based inhibitors for redox regulation of the dual-specificity phosphatase Cdc25B. , 2008, Organic & biomolecular chemistry.

[26]  Xiaobo Li,et al.  SAHA-based novel HDAC inhibitor design by core hopping method. , 2014, Journal of molecular graphics & modelling.

[27]  H. Niida,et al.  DNA damage checkpoints in mammals. , 2006, Mutagenesis.

[28]  Ettore Novellino,et al.  Discovery of new inhibitors of Cdc25B dual specificity phosphatases by structure-based virtual screening. , 2012, Journal of medicinal chemistry.

[29]  P. Wipf,et al.  Discovery and biological evaluation of a new family of potent inhibitors of the dual specificity protein phosphatase Cdc25. , 2001, Journal of medicinal chemistry.

[30]  E. Novellino,et al.  CDC25 phosphatase inhibitors: an update. , 2012, Mini reviews in medicinal chemistry.

[31]  Daniela Schuster,et al.  Ligand-Based Pharmacophore Modeling and Virtual Screening for the Discovery of Novel 17β-Hydroxysteroid Dehydrogenase 2 Inhibitors , 2014, Journal of medicinal chemistry.

[32]  Bernard Ducommun,et al.  CDC25 phosphatases in cancer cells: key players? Good targets? , 2007, Nature Reviews Cancer.

[33]  José L Medina-Franco,et al.  Integrating virtual and biochemical screening for protein tyrosine phosphatase inhibitor discovery. , 2014, Methods.

[34]  H Osada,et al.  Synthesis of a tetronic acid library focused on inhibitors of tyrosine and dual-specificity protein phosphatases and its evaluation regarding VHR and cdc25B inhibition. , 2001, Journal of medicinal chemistry.

[35]  Wei-Ren Xu,et al.  The Discovery of a Novel and Selective Inhibitor of PTP1B Over TCPTP: 3D QSAR Pharmacophore Modeling, Virtual Screening, Synthesis, and Biological Evaluation , 2014, Chemical biology & drug design.

[36]  Gaurao V. Dhoke,et al.  Structure based virtual screening to identify selective phosphodiesterase 4B inhibitors. , 2015, Journal of molecular graphics & modelling.

[37]  Rahul P Gangwal,et al.  Identification of p38α MAP kinase inhibitors by pharmacophore based virtual screening. , 2014, Journal of molecular graphics & modelling.

[38]  Peter Wipf,et al.  Redox Regulation of Cdc25B by Cell-Active Quinolinediones , 2005, Molecular Pharmacology.

[39]  Ingrid Hoffmann,et al.  Cdc25b and Cdc25c Differ Markedly in Their Properties as Initiators of Mitosis , 1999, The Journal of cell biology.

[40]  Keun Woo Lee,et al.  Novel chemical scaffolds of the tumor marker AKR1B10 inhibitors discovered by 3D QSAR pharmacophore modeling , 2015, Acta Pharmacologica Sinica.

[41]  J. Rudolph.  Cdc25 phosphatases: structure, specificity, and mechanism. , 2007, Biochemistry.