Signaling pathways: Interplay between microscopic changes and global behavior of biological systems
- Luba Tchertanov (Ecole Normale Supérieure-Cachan, France)
- Isabelle Demachy (University Paris-Sud, Orsay, France)
- Aurélien de la Lande (CNRS-University of Paris-Sud, Orsay , France)
- Elodie Laine (Université Pierre et Marie Curie Paris 6 et Laboratoire de Génomique de Microorganismes, France)
Biological cells are very complex and organized systems. Physiological functions are governed by finely tuned dynamical networks of interacting species, going from individual molecules to large stable or meta-stable macromolecular complexes or assemblies. A great number of proteins act as ligand/substrate-dependent activators that contribute to cell signaling pathways (Manning et al., 2002, Olsen et al., 2006) regulating crucial physiological or physiopathological processes. The integration of these processes covers a wide range - from pico-seconds to seconds and beyond - of time scales. The problems addressed in this proposal mainly deal with multiscale simulation approaches that can be used or developed in order to integrate the different scales (in spatio-temporal terms) of complexity in the biological systems, from microscopic or local changes at the level of individual biomolecules, through conformational dynamics of individual proteins and mesoscopic effects involving molecular recognition of interacting objects, to macroscopic or global behavior of cell communication networks (Rual et al., 2005).
Microscopic level – from local perturbation effects to large conformational changes:
Proteins involved in cellular signaling, such as protein kinases, nuclear receptors or NMDA complex, often display a remarkable conformational plasticity. In solution, the population equilibrium between different conformational states is determined by their energetic landscape. Typically, this equilibrium can be displaced by ligand binding, phosphorylation events or point mutations. Internal modifications such as point mutations or insertion/deletion of amino acids can represent very subtle structural changes and yet have a great impact on the dynamics of the protein or induce large conformational changes at remote sites (Grant et al., 2010). It is becoming more and more clear that signal propagation occurs within individual proteins themselves through allosteric communication routes. Studies have begun to elucidate the mechanistic and thermodynamic impact of cancer mutations on protein kinases at the atomic level (Banavali & Roux, 2005, Dixit & Verkhivker, 2009, Laine et al., 2011). Recent advances in the field have permitted new strategies to emerge that target protein conformational transitions to modulate their function (Laine et al., 2010). Efforts have also been engaged to quantitatively relate equilibrium fluctuations to communication propensity or coupling between distant sites (Bahar et al., 2007, Hilser & Thompson, 2007). However a unifying framework for understanding signal transformation and noise propagation in terms of conformational fluctuations, intrinsic disorder, enthalpy/entropy balance, is still lacking.
Functional networks identification and description:
The second challenge addressed here concerns functional networks construction (Levy et al., 2009, Yosef et al., 2009) and global dynamics. According to the energy landscape theory proteins structural ensemble is modified upon changes in the environment (pH, ionic strength and temperature) (Kumar et al., 2000a, Kumar et al., 2000b). Such changes affect tens or more of proteins binding to the same “hub” that can assume a broad range of different conformations, each of which able to bind to a different partner (Dunker et al., 2005, Haynes et al., 2006) via possibly distinct binding sites (Keskin et al., 2005, Keskin et al., 2008). A largely unsolved question relates to what are the molecular specificity determinants that govern such a broad range of protein-protein associations (Tsai et al., 2009) and how diverse mutations and alterations of molecular signaling pathways resulting in the deregulation of important physiological processes and in physiopathologies (Santos et al., 2010, Schramm et al., 2010). Regarding this matter, a major challenge is to discriminate between the normal physiological processes and those corresponding to pathological functioning and understand how the subsequent changes in structure and dynamics manifest downstream.
Interplay between the challenges:
The main focus of our proposal addresses the problem of how to deal with these different organizational levels in order to link the microscopic characteristics to global behavior of a multi-component biological system. With the recent developments of multiscale representations/approaches, linking microscopic dynamical changes to macroscopic spatio-temporal dynamics is becoming possible. Several methods based on the modelling of the dynamical recognition processes taking place at protein-protein interfaces – with the aid of the already existing molecular dynamics simulations and collective movement analysis (normal modes, elastic modes or consensus modes) - can be extended to reach this goal and establish how these interactions relate to the various signalling pathways.
The methodological approaches as well as the applications in this domain may have a great impact in the medical field, and the interplay can also be foreseen between simulations and experiments.
 Bahar, I., Chennubhotla, C., & Erman, B. (2007). Reply to 'Comment on elastic network models and proteins'. Physical Biology 4, 64-65, doi:DOI 10.1088/1478-3975/4/1/N02.
 Banavali, N. K. & Roux, B. (2005). The N-terminal end of the catalytic domain of Src kinase Hck is a conformational switch implicated in long-range allosteric regulation. Structure 13, 1715-1723, doi:DOI 10.1016/j.str.2005.09.005.
 Dixit, A. & Verkhivker, G. M. (2009). Hierarchical Modeling of Activation Mechanisms in the ABL and EGFR Kinase Domains: Thermodynamic and Mechanistic Catalysts of Kinase Activation by Cancer Mutations. Plos Computational Biology 5, doi:ARTN e1000487;DOI 10.1371/journal.pcbi.1000487.
 Dunker, A. K., Cortese, M. S., Romero, P., Iakoucheva, L. M., & Uversky, V. N. (2005). Flexible nets - The roles of intrinsic disorder in protein interaction networks. Febs Journal 272, 5129-5148, doi:DOI 10.1111/j.1742-4658.2005.04948.x.
 Grant, B. J., Gorfe, A. A., & McCammon, J. A. (2010). Large conformational changes in proteins: signaling and other functions. Current Opinion in Structural Biology 20, 142-147, doi:DOI 10.1016/j.sbi.2009.12.004.
 Haynes, C., Oldfield, C. J., Ji, F., Klitgord, N., Cusick, M. E., Radivojac, P., Uversky, V. N., Vidal, M., & Iakoucheva, L. M. (2006). Intrinsic disorder is a common feature of hub proteins from four eukaryotic interactomes. Plos Computational Biology 2, 890-901, doi:ARTN e100;DOI 10.1371/journal.pcbi.0020100.
 Hilser, V. J. & Thompson, E. B. (2007). Intrinsic disorder as a mechanism to optimize allosteric coupling in proteins. Proc. Natl. Acad. Sci. U. S A 104, 8311-8315, doi:0700329104 [pii];10.1073/pnas.0700329104 [doi].
 Keskin, O., Gursoy, A., Ma, B., & Nussinov, R. (2008). Principles of protein-protein interactions: What are the preferred ways for proteins to interact? Chemical Reviews 108, 1225-1244, doi:DOI 10.1021/cr040409x.
 Keskin, O., Ma, B. Y., Rogale, K., Gunasekaran, K., & Nussinov, R. (2005). Protein-protein interactions: organization, cooperativity and mapping in a bottom-up Systems Biology approach. Physical Biology 2, S24-S35, doi:DOI 10.1088/1478-3975/2/2/S03.
[10 Kumar, S., Ma, B. Y., Tsai, C. J., Sinha, N., & Nussinov, R. (2000a). Folding and binding cascades: Dynamic landscapes and population shifts. Protein Science 9, 10-19.
 Kumar, S., Tsai, C. J., & Nussinov, R. (2000b). Factors enhancing protein thermostability. Protein Engineering 13, 179-191.
 Laine, E., Chauvot, d. B., I, Perahia, D., Auclair, C., & Tchertanov, L. (2011). Mutation D816V Alters the Internal Structure and Dynamics of c-KIT Receptor Cytoplasmic Region: Implications for Dimerization and Activation Mechanisms. PLoS Comput. Biol. 7, e1002068, doi:10.1371/journal.pcbi.1002068 [doi];PCOMPBIOL-D-10-00316 [pii].
 Laine, E., Goncalves, C., Karst, J. C., Lesnard, A., Rault, S., Tang, W. J., Malliavin, T. E., Ladant, D., & Blondel, A. (2010). Use of allostery to identify inhibitors of calmodulin-induced activation of Bacillus anthracis edema factor. Proceedings of the National Academy of Sciences of the United States of America 107, 11277-11282, doi:DOI 10.1073/pnas.0914611107.
 Levy, E. D., Landry, C. R., & Michnick, S. W. (2009). How Perfect Can Protein Interactomes Be? Science Signaling 2, doi:ARTN pe11;DOI 10.1126/scisignal.260pe11.
Manning, G., Whyte, D. B., Martinez, R., Hunter, T., & Sudarsanam, S. (2002). The protein kinase complement of the human genome. Science 298, 1912-+.
 Olsen, J. V., Blagoev, B., Gnad, F., Macek, B., Kumar, C., Mortensen, P., & Mann, M. (2006). Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 127, 635-648, doi:DOI 10.1016/j.cell.2006.09.026.
 Rual, J. F., Venkatesan, K., Hao, T., Hirozane-Kishikawa, T., Dricot, A., Li, N., Berriz, G. F., Gibbons, F. D., Dreze, M., Ayivi-Guedehoussou, N., Klitgord, N., Simon, C., Boxem, M., Milstein, S., Rosenberg, J., Goldberg, D. S., Zhang, L. V., Wong, S. L., Franklin, G., Li, S. M., Albala, J. S., Lim, J. H., Fraughton, C., Llamosas, E., Cevik, S., Bex, C., Lamesch, P., Sikorski, R. S., Vandenhaute, J., Zoghbi, H. Y., Smolyar, A., Bosak, S., Sequerra, R., Doucette-Stamm, L., Cusick, M. E., Hill, D. E., Roth, F. P., & Vidal, M. (2005). Towards a proteome-scale map of the human protein-protein interaction network. Nature 437, 1173-1178, doi:DOI 10.1038/nature04209.
 Santos, S. F., Pierrot, N., & Octave, J. N. (2010). Network Excitability Dysfunction in Alzheimer's Disease: Insights from In Vitro and In Vivo Models. Reviews in the Neurosciences 21, 153-171.
 Schramm, G., Kannabiran, N., & Konig, R. (2010). Regulation patterns in signaling networks of cancer. Bmc Systems Biology 4, doi:10.1186/1752-0509-4-162.
 Tsai, C. J., Ma, B. Y., & Nussinov, R. (2009). Protein-protein interaction networks: how can a hub protein bind so many different partners? Trends in Biochemical Sciences 34, 594-600, doi:DOI 10.1016/j.tibs.2009.07.007.
 Yosef, N., Ungar, L., Zalckvar, E., Kimchi, A., Kupiec, M., Ruppin, E., & Sharan, R. (2009). Toward accurate reconstruction of functional protein networks. Molecular Systems Biology 5, doi:ARTN 248;DOI 10.1038/msb.2009.3.