Multifunnel Energy Landscapes for Phosphorylated Translation Repressor 4E-BP2 and Its Mutants

Wei Kang1,2,3, Fan Jiang1, Yun-Dong Wu1,2

  • 1Laboratory of Computational Chemistry and Drug Design, Laboratory of Chemical Genomics , Peking University Shenzhen Graduate School , Shenzhen 518055 , China.

Insights

Phosphorylation transforms the 4E-BP2 protein structure, altering its function. Energy landscape analysis reveals how this, and mutations, affect protein stability and interactions.

Area of Science:

  • Biochemistry
  • Structural Biology
  • Computational Biology

Background:

  • Eukaryotic translation initiation factor 4E (eIF4E) binding protein 2 (4E-BP2) undergoes significant structural changes upon phosphorylation.
  • These changes impact protein-protein interactions and cellular processes.
  • Understanding these dynamics is crucial for deciphering regulatory mechanisms.

Purpose of the Study:

  • To investigate the energy landscapes of doubly phosphorylated 4E-BP2 and its mutants.
  • To elucidate the role of phosphorylation and specific mutations in protein structural transitions.
  • To explain experimental observations regarding 4E-BP2 stability and binding affinity.

Main Methods:

  • High-temperature molecular dynamics simulations.
  • Discrete path sampling techniques.
  • Construction of potential and free energy landscapes.

Main Results:

  • The energy landscapes for the studied systems are multifunneled, indicating multiple stable and near-stable conformational states.
  • Hydrogen bonds involving phosphate groups are critical for stabilizing conformations and mediating transitions between states.
  • The study explains the observed low stability of doubly phosphorylated 4E-BP2 and the folding defect in the Y54A/L59A mutant.

Conclusions:

  • Protein phosphorylation can lead to complex energy landscapes, supporting multifunneled and multifunctional protein behavior.
  • The findings provide a molecular-level understanding of how post-translational modifications influence protein structure and function.
  • This work bridges computational modeling with experimental data to explain protein dynamics.