Exploring c-Met kinase flexibility by sampling and clustering its conformational space
Yasmine Asses
Nancy Université, LORIA/UMR 7503, Équipe-projet Orpailleur, Campus Scientifique, BP 239, 54506 Vandœuvre-lès-Nancy Cedex, France
Yasmine Asses and Vishwesh Venkatraman contributed equally to this work.
Search for more papers by this authorVishwesh Venkatraman
Nancy Université, LORIA/UMR 7503, Équipe-projet Orpailleur, Campus Scientifique, BP 239, 54506 Vandœuvre-lès-Nancy Cedex, France
Yasmine Asses and Vishwesh Venkatraman contributed equally to this work.
Search for more papers by this authorVincent Leroux
Nancy Université, LORIA/UMR 7503, Équipe-projet Orpailleur, Campus Scientifique, BP 239, 54506 Vandœuvre-lès-Nancy Cedex, France
University of Oslo, Chemistry Department, P.O. Box 1033 Blindern, 0315 Oslo, Norway
Search for more papers by this authorDavid W. Ritchie
Nancy Université, LORIA/UMR 7503, Équipe-projet Orpailleur, Campus Scientifique, BP 239, 54506 Vandœuvre-lès-Nancy Cedex, France
Search for more papers by this authorCorresponding Author
Bernard Maigret
Nancy Université, LORIA/UMR 7503, Équipe-projet Orpailleur, Campus Scientifique, BP 239, 54506 Vandœuvre-lès-Nancy Cedex, France
Nancy Université, LORIA/UMR 7503, Équipe-projet Orpailleur, Campus Scientifique, BP 239, 54506 Vandœuvre-lès-Nancy Cedex, France===Search for more papers by this authorYasmine Asses
Nancy Université, LORIA/UMR 7503, Équipe-projet Orpailleur, Campus Scientifique, BP 239, 54506 Vandœuvre-lès-Nancy Cedex, France
Yasmine Asses and Vishwesh Venkatraman contributed equally to this work.
Search for more papers by this authorVishwesh Venkatraman
Nancy Université, LORIA/UMR 7503, Équipe-projet Orpailleur, Campus Scientifique, BP 239, 54506 Vandœuvre-lès-Nancy Cedex, France
Yasmine Asses and Vishwesh Venkatraman contributed equally to this work.
Search for more papers by this authorVincent Leroux
Nancy Université, LORIA/UMR 7503, Équipe-projet Orpailleur, Campus Scientifique, BP 239, 54506 Vandœuvre-lès-Nancy Cedex, France
University of Oslo, Chemistry Department, P.O. Box 1033 Blindern, 0315 Oslo, Norway
Search for more papers by this authorDavid W. Ritchie
Nancy Université, LORIA/UMR 7503, Équipe-projet Orpailleur, Campus Scientifique, BP 239, 54506 Vandœuvre-lès-Nancy Cedex, France
Search for more papers by this authorCorresponding Author
Bernard Maigret
Nancy Université, LORIA/UMR 7503, Équipe-projet Orpailleur, Campus Scientifique, BP 239, 54506 Vandœuvre-lès-Nancy Cedex, France
Nancy Université, LORIA/UMR 7503, Équipe-projet Orpailleur, Campus Scientifique, BP 239, 54506 Vandœuvre-lès-Nancy Cedex, France===Search for more papers by this authorAbstract
It is now widely recognized that the flexibility of both partners has to be considered in molecular docking studies. However, the question how to handle the best the huge computational complexity of exploring the protein binding site landscape is still a matter of debate. Here we investigate the flexibility of c-Met kinase as a test case for comparing several simulation methods. The c-Met kinase catalytic site is an interesting target for anticancer drug design. In particular, it harbors an unusual plasticity compared with other kinases ATP binding sites. Exploiting this feature may eventually lead to the discovery of new anticancer agents with exquisite specificity. We present in this article an extensive investigation of c-Met kinase conformational space using large-scale computational simulations in order to extend the knowledge already gathered from available X-ray structures. In the process, we compare the relevance of different strategies for modeling and injecting receptor flexibility information into early stage in silico structure-based drug discovery pipeline. The results presented here are currently being exploited in on-going virtual screening investigations on c-Met. Proteins 2012;. © 2011 Wiley Periodicals, Inc.
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REFERENCES
- 1 Mittag T,Kay LE,Forman-Kay JD. Protein dynamics and conformational disorder in molecular recognition. J Mol Recognit 2010; 23: 105–116.
- 2 Teilum K,Olsen JG,Kragelund BB. Functional aspects of protein flexibility. Cell Mol Life Sci 2009; 66: 2231–2247.
- 3 Ohkura K. Exploring unique structures: flexibility is a significant factor in biological activity. Biol Pharm Bull 2007: 30; 1025–1036.
- 4 Durrant JD,de Oliveira CA,McCammon JA. Including receptor flexibility and induced fit effects into the design of MMP-2 inhibitors. J Mol Recognit 2010; 23: 173–182.
- 5 Mishra N,Basu A,Jayaprakash V,Sharon A,Basu M,Patnaik KK. Structure based virtual screening of GSK-3β: importance of protein flexibility and induced fit. Bioorg Med Chem Lett 2009; 19: 5582–5585.
- 6 Miller M. The importance of being flexible: the case of basic region leucine zipper transcriptional regulators. Curr Protein Pept Sci 2009; 10: 244–269.
- 7 Wong CF,McCammon JA. Protein flexibility and computer-aided drug design. Annu Rev Pharmacol Toxicol 2003; 43: 31–45.
- 8 Teague SJ. Implications of protein flexibility for drug discovery. Nat Rev Drug Discov 2003; 2: 527–541.
- 9 Carlson HA. Protein flexibility is an important component of structure-based drug discovery. Curr Pharm Des 2002; 8: 1571–1578.
- 10 B-Rao C,Subramanian J,Sharma SD. Managing protein flexibility in docking and its applications. Drug Discov Today 2009; 14: 394–400.
- 11 Fischer B,Merlitz H,Wenzel W. Receptor flexibility for large-scale in silico ligand screens: chances and challenges. Methods Mol Biol 2008; 443: 353–564.
- 12 Alonso H,Bliznyuk AA,Gready JE. Combining docking and molecular dynamic simulations in drug design. Med Res Rev 2006; 26: 531–568.
- 13 Armen RS,Chen J,Brooks CL. An evaluation of explicit receptor flexibility in molecular docking using molecular dynamics and torsion angle molecular dynamics. J Chem Theory Comput 2009; 5: 2909–2923.
- 14 Shaw DE,Dror RO;Salmon JK,Grossman JP,Mackenzie KM,Bank JA,Young C,Deneroff MM,Batson B,Bowers KJ,Chow, E,Eastwood MP,Ierardi DJ,Klepeis JL,Kuskin JS,Larson RH,Kresten LL,Maragakis P,Moraes MA,Piana S,Shan Y,Towles B. Millisecond-scale molecular dynamics simulations on Anton. Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis 2009, 1–11.
- 15 May A,Zacharias M. Protein-ligand docking accounting for receptor side chain and global flexibility in normal modes: evaluation on kinase inhibitor cross docking. J Med Chem 2008; 51: 3499–3506.
- 16 Cavasotto CN,Kovacs JA,Abagyan RA. Representing receptor flexibility in ligand docking through relevant normal modes. J Am Chem Soc 2005; 127: 9632–9960.
- 17 Heath AP,Kavraki LE,Clementi C. From coarse-grain to all-atom: toward multiscale analysis of protein landscapes. Proteins 2007; 68: 646–661.
- 18 Craig IR,Essex JW,Spiegel K. Ensemble docking into multiple crystallographically derived protein structures: an evaluation based on the statistical analysis of enrichments. J Chem Inf Model 2010; 50: 511–524.
- 19 Paulsen JL,Anderson AC. Scoring ensembles of docked protein:ligand interactions for virtual lead optimization. J Chem Inf Model 2009; 49: 2813–2189.
- 20 Bolstad ES,Anderson AC. In pursuit of virtual lead optimization: pruning ensembles of receptor structures for increased efficiency and accuracy during docking. Proteins 2009; 75: 62–74.
- 21 Totrov M,Abagyan R. Flexible ligand docking to multiple receptor conformations: a practical alternative. Curr Opin Struct Biol 2008; 18: 178–184.
- 22 Huang SY,Zou X. Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking. Proteins 2007; 66: 399–421.
- 23 Sperandio O,Mouawad L,Pinto E,Villoutreix BO,Perahia D,Miteva MA. How to choose relevant multiple receptor conformations for virtual screening: a test case of Cdk2 and normal mode analysis. Eur Biophys J 2010; 39: 1365–1372.
- 24 Yap TA,de Bono JS. Targeting the HGF/c-Met axis: state of play. Mol Cancer Ther 2010; 9: 1077–1079.
- 25 Liu X,Newton RC,Scherle PA. Developing c-Met pathway inhibitors for cancer therapy: progress and challenges. Trends Mol Med 2010; 16: 37–45.
- 26 Cañadas I,Rojo F,Arumi-Uría M,Rovira A,Albanell J,Arriola E. C-Met as a new therapeutic target for the development of novel anticancer drugs. Clin Transl Oncol 2010; 12: 253–260.
- 27 Asses Y,Leroux V,Tairi-Kellou S,Dono R,Maina F,Maigret B. Analysis of c-Met kinase domain complexes: a new specific catalytic site receptor model for defining binding modes of ATP-competitive ligands. Chem Biol Drug Des 2009; 74: 560–570.
- 28Available at: http://accelrys.com/
- 29 Buck M,Bouguet-Bonnet S,Pastor RW,MacKerell AD,Jr. Importance of the CMAP correction to the CHARMM22 protein force field: dynamics of hen lysozyme. Biophys J 2006; 90: L36–L38.
- 30 Phillips JC,Braun R,Wang W,Gumbart J,Tajkhorshid E,Villa E,Chipot C,Skeel RD,Kale L,Schulten K. Scalable molecular dynamics with NAMD. J Comput Chem 2005; 26: 1781–1802.
- 31Available at: http://lorentz.dynstr.pasteur.fr/nomad-ref.php.
- 32 Bouvier G,Evrard-Todeschi N,Girault JP,Bertho G. Automatic clustering of docking poses in virtual screening process using self-organizing map. Bioinformatics 2010; 26: 53–60.
- 33 Priestle JP. 3-D clustering: a tool for high throughput docking. J Mol Model 2009; 15: 551–560.
- 34 Frickenhaus S,Kannan S,Zacharias M. Efficient evaluation of sampling quality of molecular dynamics simulations by clustering of dihedral torsion angles and Sammon mapping. J Comput Chem 2009; 30: 479–492.
- 35 Steindl TM,Crump CE,Hayden FG,Langer T. Pharmacophore modeling, docking, and principal component analysis based clustering: combined computer-assisted approaches to identify new inhibitors of the human rhinovirus coat protein. J Med Chem 2005; 48: 6250–6260.
- 36 Kozakov D,Clodfelter KH,Vajda S,Camacho CJ. Optimal clustering for detecting near-native conformations in protein docking. Biophys J 2005; 89: 867–875.
- 37 Laboulais C,Ouali M,Le Bret M,Gabarro-Arpa J. Hamming distance geometry of a protein conformational space: application to the clustering of a 4-ns molecular dynamics trajectory of the HIV-1 integrase catalytic core. Proteins 2002; 47: 169–179.
- 38 Seeber M,Cecchini M,Rao F,Settanni G,Caflisch A. Wordom: a program for efficient analysis of molecular dynamics simulations. Bioinformatics 2007; 23: 2625–2627.
- 39 Feig M,Karanicolas J,Brooks CL,III. MMTSB Tool Set: enhanced sampling and multiscale modeling methods for applications in structural biology. J Mol Graph Model 2004; 22: 377–395.
- 40 Zhao Y;Karypis G. Empirical and theoretical comparisons of selected criterion functions for document clustering. Machine Learn 2004; 55: 311–331.
- 41Available at: www.r-project.org.
- 42 Humphrey W,Dalke A,Schulten K. VMD: visual molecular dynamics. J Mol Graph 1996; 14: 33–38,27–28.
- 43 Rickert KW,Patel SB,Allison TJ,Byrne NJ,Darke PL,Ford RE,Guerin DJ,Hall DL,Kornienko M,Lu J,Munshi S,Reid JC,Shipman JM,Stanton EF,Wilson KJ,Young JR,Soisson SM,Lumb KJ. Structural basis selective small-molecule kinase inhibition of activated c-Met. J Biol Chem 2011; 286: 11218–11225.
- 44 Bellon SF,Kaplan-Lefko P,Yang Y,Zhang Y,Moriguchi J,Rex K,Johnson CW,Rose PE,Long AM,O'Connor AB,Gu Y,Coxon A,Kim TS,Tasker A,Burgess TL,Dussault I. c-Met inhibitors with novel binding mode show activity against several hereditary papillary renal cell carcinoma-related mutations. J Biol Chem 2008; 283: 2675–2683.
- 45 Eathiraj S,Palma R,Volckova E,Hirschi M,France DS,Ashwell MA,Chan TC. Discovery of a novel mode of protein kinase inhibition characterized by the mechanism of inhibition of human mesenchymal-epithelial transition factor (c-Met) protein autophosphorylation by ARQ-197. J Biol Chem 2011; 286: 20666–20676.
- 46 Aleksandronov A,Simonson T. Molecular dynamics simulations show that conformational selection governs the binding preferences of imatinib for several tyrosine kinases. J Biol Sci 2010; 285: 13807–13815.
- 47 Vogtherr M,Saxena K,Hoelder S,Grimme S,Betz M,Schieborr U,Pescatore B,Robin M,Delarbre L,Langer T,Wendt KU,Schwalbe H. NMR characterization of kinase p38 dynamics in free and ligand-bound forms. Angew Chem Int Ed Engl 2006; 45: 993–997.
- 48 Csermely P,Palotai R,Nussinov R. Induced fit, conformational selection and independent dynamic segments: an extended view of binding events. Trends Biochem Sci 2010; 35: 539–546.
- 49 Zhou HX. From induced fit to conformational selection: a continuum of binding mechanism controlled by the timescale of conformational transitions. Biophys J 2010; 98: L15–L17.
- 50 Hammes GG,Chang YC,Oas TG. Conformational selection or induced fit: a flux description of reaction mechanism. Proc Natl Acad Sci USA 2009; 106: 13737–13741.
- 51 Silva DA,Bowman GR,Sosa-Peinado A,Huang X. A role for both conformational selection and induced fit in ligand binding by the LAO protein. PLoS Comput Biol 2011; 7: e1002054.
- 52 Xu M,Lill MA. Significant enhancement of docking sensitivity using implicit ligand sampling. J Chem Inform Model 2011; 51: 693–706.
- 53 Asses Y,Leroux V,Furlan A,Maina F,Dono R,Maigret B. Investigation of the ensemble docking strategy to improve docking relevance in virtual screening against the c-Met kinase; manuscript in preparation.