Novel Inhibitor Discovery through Virtual Screening against Multiple Protein Conformations Generated via Ligand-Directed Modeling: A Maternal Embryonic Leucine Zipper Kinase Example

Kinase targets have been demonstrated to undergo major conformational reorganization upon ligand binding. Such protein conformational plasticity remains a significant challenge in structure-based virtual screening methodology and may be approximated by screening against an ensemble of diverse protein conformations. Maternal embryonic leucine zipper kinase (MELK), a member of serine-threonine kinase family, has been recently found to be involved in the tumerogenic state of glioblastoma, breast, ovarian, and colon cancers. We therefore modeled several conformers of MELK utilizing the available chemogenomic and crystallographic data of homologous kinases. We carried out docking pose prediction and virtual screening enrichment studies with these conformers. The performances of the ensembles were evaluated by their ability to reproduce known inhibitor bioactive conformations and to efficiently recover known active compounds early in the virtual screen when seeded with decoy sets. A few of the individual MELK conformers performed satisfactorily in reproducing the native protein-ligand pharmacophoric interactions up to 50% of the cases. By selecting an ensemble of a few representative conformational states, most of the known inhibitor binding poses could be rationalized. For example, a four conformer ensemble is able to recover 95% of the studied actives, especially with imperfect scoring function(s). The virtual screening enrichment varied considerably among different MELK conformers. Enrichment appears to improve by selection of a proper protein conformation. For example, several holo and unliganded active conformations are better to accommodate diverse chemotypes than ATP-bound conformer. These results prove that using an ensemble of diverse conformations could give a better performance. Applying this approach, we were able to screen a commercially available library of half a million compounds against three conformers to discover three novel inhibitors of MELK, one from each template. Among the three compounds validated via experimental enzyme inhibition assays, one is relatively potent (15; K(d) = 0.37 μM), one moderately active (12; K(d) = 3.2 μM), and one weak but very selective (9; K(d) = 18 μM). These novel hits may be utilized to assist in the development of small molecule therapeutic agents useful in diseases caused by deregulated MELK, and perhaps more importantly, the approach demonstrates the advantages of choosing an appropriate ensemble of a few conformers in pursuing compound potency, selectivity, and novel chemotypes over using single target conformation for structure-based drug design in general.

[1]  Erin S. Bolstad,et al.  In pursuit of virtual lead optimization: The role of the receptor structure and ensembles in accurate docking , 2008, Proteins.

[2]  Tomonaga Ozawa,et al.  The importance of CH/pi hydrogen bonds in rational drug design: An ab initio fragment molecular orbital study to leukocyte-specific protein tyrosine (LCK) kinase. , 2008, Bioorganic & medicinal chemistry.

[3]  Yusuke Nakamura,et al.  Involvement of maternal embryonic leucine zipper kinase (MELK) in mammary carcinogenesis through interaction with Bcl-G, a pro-apoptotic member of the Bcl-2 family , 2007, Breast Cancer Research.

[4]  J Andrew McCammon,et al.  Target flexibility in molecular recognition. , 2005, Biochimica et biophysica acta.

[5]  David P. Davis,et al.  Maternal embryonic leucine zipper kinase/murine protein serine-threonine kinase 38 is a promising therapeutic target for multiple cancers. , 2005, Cancer research.

[6]  Mindy I. Davis,et al.  A quantitative analysis of kinase inhibitor selectivity , 2008, Nature Biotechnology.

[7]  Cristiano R. W. Guimaraes,et al.  Understanding the Impact of the P-loop Conformation on Kinase Selectivity , 2011, J. Chem. Inf. Model..

[8]  Woody Sherman,et al.  ConfGen: A Conformational Search Method for Efficient Generation of Bioactive Conformers , 2010, J. Chem. Inf. Model..

[9]  N. Gray,et al.  Targeting cancer with small molecule kinase inhibitors , 2009, Nature Reviews Cancer.

[10]  P. Cohen,et al.  The selectivity of protein kinase inhibitors: a further update. , 2007, The Biochemical journal.

[11]  Yan Li,et al.  Discovery of Novel Checkpoint Kinase 1 Inhibitors by Virtual Screening Based on Multiple Crystal Structures , 2011, J. Chem. Inf. Model..

[12]  S. Dimitrov,et al.  Benzo[e]pyridoindoles, novel inhibitors of the Aurora kinases , 2009, Cell cycle.

[13]  Somesh D. Sharma,et al.  Managing protein flexibility in docking and its applications. , 2009, Drug discovery today.

[14]  Christopher I. Bayly,et al.  Evaluating Virtual Screening Methods: Good and Bad Metrics for the "Early Recognition" Problem , 2007, J. Chem. Inf. Model..

[15]  Matthew P. Repasky,et al.  Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. , 2004, Journal of medicinal chemistry.

[16]  Lisa Yan,et al.  Fully Automated Molecular Mechanics Based Induced Fit Protein-Ligand Docking Method , 2008, J. Chem. Inf. Model..

[17]  Jung-Hsin Lin,et al.  The relaxed complex method: Accommodating receptor flexibility for drug design with an improved scoring scheme. , 2003, Biopolymers.

[18]  E. Jaeger,et al.  Comparison of automated docking programs as virtual screening tools. , 2005, Journal of Medicinal Chemistry.

[19]  I. Bahar,et al.  The intrinsic dynamics of enzymes plays a dominant role in determining the structural changes induced upon inhibitor binding , 2009, Proceedings of the National Academy of Sciences.

[20]  Evan Bolton,et al.  An overview of the PubChem BioAssay resource , 2009, Nucleic Acids Res..

[21]  Ben M. Webb,et al.  Comparative Protein Structure Modeling Using Modeller , 2006, Current protocols in bioinformatics.

[22]  J. Irwin,et al.  Benchmarking sets for molecular docking. , 2006, Journal of medicinal chemistry.

[23]  C. Chennubhotla,et al.  Intrinsic dynamics of enzymes in the unbound state and relation to allosteric regulation. , 2007, Current opinion in structural biology.

[24]  Richard A. Lewis,et al.  Lessons in molecular recognition: the effects of ligand and protein flexibility on molecular docking accuracy. , 2004, Journal of medicinal chemistry.

[25]  S. Teague Implications of protein flexibility for drug discovery , 2003, Nature Reviews Drug Discovery.

[26]  Simona Distinto,et al.  Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection—What can we learn from earlier mistakes? , 2008, J. Comput. Aided Mol. Des..

[27]  D. Rognan,et al.  Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. , 2000, Journal of medicinal chemistry.

[28]  Mengang Xu,et al.  Utilizing Experimental Data for Reducing Ensemble Size in Flexible-Protein Docking , 2012, J. Chem. Inf. Model..

[29]  Elizabeth Yuriev,et al.  Challenges and advances in computational docking: 2009 in review , 2011, Journal of molecular recognition : JMR.

[30]  William L. Jorgensen,et al.  Journal of Chemical Information and Modeling , 2005, J. Chem. Inf. Model..

[31]  Ajay N. Jain,et al.  Parameter estimation for scoring protein-ligand interactions using negative training data. , 2006, Journal of medicinal chemistry.

[32]  Stefan Knapp,et al.  Synthesis, kinase inhibitory potencies, and in vitro antiproliferative evaluation of new Pim kinase inhibitors. , 2009, Journal of medicinal chemistry.

[33]  Gerhard Klebe,et al.  Molecular Docking Screens Using Comparative Models of Proteins , 2009, J. Chem. Inf. Model..

[34]  N. Gray,et al.  Rational design of inhibitors that bind to inactive kinase conformations , 2006, Nature chemical biology.

[35]  R. Friesner,et al.  Novel procedure for modeling ligand/receptor induced fit effects. , 2006, Journal of medicinal chemistry.

[36]  J. Bajorath,et al.  Quo vadis, virtual screening? A comprehensive survey of prospective applications. , 2010, Journal of medicinal chemistry.

[37]  Michele Parrinello,et al.  Locating binding poses in protein-ligand systems using reconnaissance metadynamics , 2012, Proceedings of the National Academy of Sciences.

[38]  Woody Sherman,et al.  Improving database enrichment through ensemble docking , 2008, J. Comput. Aided Mol. Des..

[39]  Xiang-Qun Xie,et al.  Exploiting PubChem for virtual screening , 2010, Expert opinion on drug discovery.

[40]  J. Mccammon,et al.  Computational drug design accommodating receptor flexibility: the relaxed complex scheme. , 2002, Journal of the American Chemical Society.

[41]  Paul S Mischel,et al.  Maternal embryonic leucine zipper kinase is a key regulator of the proliferation of malignant brain tumors, including brain tumor stem cells , 2008, Journal of neuroscience research.

[42]  S. Bryant,et al.  PubChem as a public resource for drug discovery. , 2010, Drug discovery today.

[43]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[44]  Jonathan W. Essex,et al.  Ensemble Docking into Multiple Crystallographically Derived Protein Structures: An Evaluation Based on the Statistical Analysis of Enrichments , 2010, J. Chem. Inf. Model..

[45]  In-Hee Park,et al.  Dynamic ligand-induced-fit simulation via enhanced conformational samplings and ensemble dockings: a survivin example. , 2010, The journal of physical chemistry. B.

[46]  A. Camargo,et al.  Maternal embryonic leucine zipper kinase transcript abundance correlates with malignancy grade in human astrocytomas , 2008, International journal of cancer.

[47]  Thierry Langer,et al.  The Protein Data Bank (PDB), its related services and software tools as key components for in silico guided drug discovery. , 2008, Journal of medicinal chemistry.

[48]  Albert C. Pan,et al.  Pathway and mechanism of drug binding to G-protein-coupled receptors , 2011, Proceedings of the National Academy of Sciences.

[49]  Bernd Wendt,et al.  Capturing Structure-Activity Relationships from Chemogenomic Spaces , 2011, J. Chem. Inf. Model..