Download Dynamic analysis of ErbB signal transduction pathways

DYNAMIC ANALYSIS OF ERBB SIGNAL TRANSDUCTION PATHWAYS
Mariko Hatakeyama1*, Yoshiki Yamaguchi1, Shuhei Kimura2, Takashi Naka3,
Atsushi Suenaga1, Makoto Taiji1 and Akihiko Konagaya1
1. Bioinformatics Group, RIKEN Genomic Sciences Center, Yokohama, Japan. 2.
Department of Information and Knowledge Engineering, Tottori University, Tottori,
Japan. 3. Department of Intelligent Informatics, Kyushu Sangyo University, Fukuoka,
Japan. *[email protected]
INTRODUCTION. ErbB receptors instantly induce diverse signaling network and
define the cell behavior corresponding to the different cellular conditions. To reveal the
regulation mechanism of ErbB signaling, we performed the experimental data-based
modeling of ErbB signaling network and molecular dynamics (MD) simulation of the
receptor-protein interactions in the signaling pathway.
METHOD. ErbB1 and ErbB4 receptors were stably expressed in CHO cells and
stimulated with growth hormones such as epidermal growth factor (EGF) or heregulin
(HRG). Phosphorylated forms of the receptors, MEK, ERK and Akt were examined by
anti-phospho antibodies for the corresponding proteins. Mathematical modeling and
computer simulation of the signaling network were performed by an in-house simulator
“YAGNS”[1]. For the MD simulation, affinity constants (KD) between the receptor
phosphotyrosyl peptides and SH2 domains of Grb2 and p85 PI3K were obtained from
surface plasma resonance (SPR) analysis and calculated binding free energies (ΔG)
were compared with the ones obtained from MD simulation [2,3].
RESULTS. Computer simulation of the ErbB signaling and the following experimental
results showed a possible role of PP2A for the cell decision [4]. In addition,
EGF-induced activity of B-Raf, where PP2A acts as a positive regulator, was
specifically stimulated in the cell coexpressing ErbB1 and ErbB4 receptors, and not in
ErbB1 or ErbB4 expressing cells. Furthermore, this B-Raf activity was accompanied
with higher ERK activity and cellular transformation [5]. This data suggests that
magnitude of ERK activity is defined by architectures of the pathways and the
following network dynamics. We further confirm this hypothesis by the mathematical
modeling of ErbB1-4 signaling.
On the other hand, signaling network dynamics is also regulated by kinetics of
protein-protein interaction (PPI). And if we can perform the prediction of the PPI
kinetics, it is an advantage for design of pharmaceuticals that inhibit specific PPI and
eventually control the cell decision. ΔG given by MD simulation of the interaction
between the ErbB1 and ErbB4-derived phosphotyrosyl peptides (P-peptides) and SH2
domain of Grb2 and the one between the ErbB3-derived P-peptides and SH2 domain of
PI3K p85 showed good correlations withΔG obtained from SPR analysis (correlation
coefficients 0.91 and 0.92, respectively) [2,3]. Furthermore, we could predict the novel
binding pattern of p85 PI3K and C-terminal region of the ErbB3 receptor. Thus
prediction of PPI is applicable to the construction of the network model and to reveal
the role of the proteins in signaling dynamics.
DISCUSSION. Specificity of ErbB signaling seemed to be defined due to its specific
protein-protein interaction and dynamics of the reactions. Network analysis is important
to predict network dynamics and to find the key regulators or architectures in the
network that have a critical effect on the cell decision. On the other hand, molecular
basis study such as MD simulation can predict the regulatory mechanism of the proteins
at atomic level. It is understood that cellular signaling dynamics is regulated at network
and molecular levels, therefore analyses at both levels are necessary.
REFERENCES.
1. Kimura, S., Kawasaki, T., Hatakeyama, M., Naka, T., Konishi, F., and Konagaya, A.
(2004) Bioinformatics 20, 1646-1648.
2. Suenaga, A., Takada, N, Hatakeyama, M., Ichikawa, M., Yu, X., Tomii, K., Okimoto,
N., Futatsugi, N., Narumi, T., Shirouzu, M., Yokoyama, S., Konagaya, A., and Taiji, M.
(2004) J. Biol. Chem. In press
3. Suenaga, A., Hatakeyama, M., Ichikawa, M., Yu, X., Futatsugi, N., Narumi, T., Fukui,
K., Terada, T., Taiji, M., Shirouzu, M., Yokoyama, S., and Konagaya, A. (2003)
Biochemistry (U.S), 42, 5195-5200.
4. Hatakeyama, M., Kimura, S., Naka, T., Kawasaki, T., Yumoto, N., Ichikawa, M., Kim,
J-H., Saito, K., Saeki, M., Shirouzu, M., Yokoyama, S., and Konagaya, A. (2003)
Biochem. J., 373, 451-463.
5. Hatakeyama, M., Yumoto, N., Yu, X., Shirouzu, M., Yokoyama, S., and Konagaya, A.
(2004) Oncogene 23, 5023-5031.