Integrating mathematical modeling and molecular dynamics simulations to study the effect of EGFR mutations in lung cancer
Understanding disease mechanisms at a molecular level is crucial for the development of targeted therapies. Molecular dynamics (MD) simulations provide atomic-level insights into these mechanisms, but capturing their effects on cellular behavior requires a multiscale approach. Here, we propose a methodology that combines MD simulations, mathematical modeling, and experimental data to investigate the effects of mutations in the EGFR signaling system, 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. These experiments revealed 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). To gain mechanistic insight into these observations, we determined the relative affinity and rate coefficients for EGFR-Shc binding using parameter fitting to experimental data through a system of ODEs and calculations from MD simulations.
The results indicated the need to examine additional conformational states to fully capture the molecular mechanisms at play. Enhanced sampling MD simulations have revealed key conformational dynamics, highlighting the importance of incorporating states such as inactive or dimeric forms for a more complete understanding of EGFR function and its interactions.
Hayate Takagishi1, Ai Shinobu*2,3, Noriaki Okimoto2, Makoto Taiji2, Mariko Okada1
1 Osaka University, Institute for Protein Research, 2 RIKEN Center for Biosystems Dynamics Research, 3 Osaka University, WPI Premium Research Institute for Human Metaverse Medicine