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Selecting effective collective variables (CVs) is crucial for enhanced sampling simulations. Deep-TDA successfully designed CVs for peptide-RNA binding, enabling detailed mechanism and free energy landscape analysis.

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Area of Science:

  • Computational chemistry and molecular dynamics
  • Biophysics and structural biology

Background:

  • Enhanced sampling (ES) simulations are vital for studying long-time scale biomolecular processes like molecular recognition.
  • A major hurdle in ES is selecting appropriate collective variables (CVs) to effectively sample system states, especially for complex interactions like peptide-RNA binding.
  • Traditional methods struggle with the high dimensionality required to describe flexible molecules and conformationally rich hosts.

Purpose of the Study:

  • To address the challenge of CV selection in enhanced sampling simulations.
  • To apply the Deep-TDA method for designing effective CVs for complex biomolecular recognition.
  • To investigate the binding mechanism and free energy landscape of a cyclic peptide (L22) to TAR RNA.

Main Methods:

  • Utilized the Deep-TDA method to generate nonlinear combinations of contact pairs as CVs.
  • Employed on-the-fly probability-based enhanced sampling (OPES) simulations.
  • Combined Deep-TDA-derived CVs with RNA apical loop RMSD for comprehensive sampling.

Main Results:

  • Successfully designed effective CVs using Deep-TDA for the L22 peptide-TAR RNA system.
  • The OPES simulation elucidated the reversible binding and unbinding mechanism of the peptide to the RNA.
  • Enabled the calculation of the free energy landscape governing the interaction.

Conclusions:

  • Deep-TDA is a powerful tool for designing collective variables in complex biomolecular recognition studies.
  • The developed CVs facilitate efficient enhanced sampling of peptide-RNA interactions.
  • This approach provides insights into molecular recognition mechanisms and free energy landscapes.