O08_04
Integrating Mathematical Modeling and Molecular Dynamics Simulations to study the effect of EGFR Mutations in Lung Cancer
Ai SHINOBU *2, Hayate TAKAGISHI1, Noriaki OKIMOTO3, Makoto TAIJI3, Mariko OKADA1
1Institute for Protein Research, Osaka University
2WPI-PRIMe, Osaka University
3Center for Biosystems Dynamics Research, RIKEN
( * E-mail: shinobu.ai.prime@osaka-u.ac.jp )
Understanding disease mechanisms at a molecular level is crucial for the development of targeted therapies. Molecular dynamics (MD) simulations offer atomic-level insights into these mechanisms. However, the interconnected nature of proteins necessitates a comprehensive approach through systems biology and network modeling. Here, we propose a methodology that integrates mathematical modeling, experimental data, and MD simulations to investigate the effects of mutations in the EGFR signaling system, which was identified as a key component in lung cancer pathogenesis.
Using six variants of lung cancer-derived H1299 cell lines (WT, L858R, E709G, G719S, S768I, L861Q), we conducted time-series experiments to capture molecular activity data within the EGFR pathway. Our findings indicate that the phosphorylation intensity of the Shc adaptor protein, which binds directly to EGFR did not correlate with EGFR expression levels and was reduced in mutants compared to the wild type (WT). We thus determined the relative affinity and rate coefficients for the binding of EGFR to Shc peptide with both molecular dynamics simulations and by parameter fitting to experimental data using a system of ODEs.
We observed a trend in the data that can be explained by structural and mechanistic insights from the simulations. However, to fully understand these trends, further refinement is needed in both the mathematical modeling and the methods used for affinity calculation in MD simulations.