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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
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Data-driven classification of ligand unbinding pathways.

Dhiman Ray1, Michele Parrinello1

  • 1Simulations Research Line, Italian Institute of Technology, Via Enrico Melen 83, Genova GE 16152, Italy.

Proceedings of the National Academy of Sciences of the United States of America
|February 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method using dynamic time warping to analyze molecular trajectories, enabling precise classification of ligand dissociation pathways and calculation of exit-path-specific kinetics for drug discovery.

Keywords:
dynamic time warpingkineticsmachine learningmolecular dynamicsrare events

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Area of Science:

  • Computational chemistry and biophysics
  • Molecular dynamics simulations
  • Drug discovery and development

Background:

  • Understanding ligand-receptor binding mechanisms is crucial for small molecule target recognition.
  • Molecular dynamics (MD) simulations can compute binding free energy and kinetics but offer only qualitative pathway analysis.
  • Manual analysis of MD trajectories is labor-intensive and limits large-scale drug discovery applications.

Purpose of the Study:

  • To develop an automated approach for analyzing molecular transition paths, specifically protein-ligand dissociation.
  • To overcome the limitations of manual analysis in characterizing ligand binding/unbinding pathways.
  • To enable accurate classification and kinetic analysis of dissociation pathways for enhanced drug discovery.

Main Methods:

  • Introduction of an automated method based on the dynamic time-warping algorithm for molecular trajectory analysis.
  • Classification of molecular trajectories using generic descriptors such as contacts or distances.
  • Computation of exit-path-specific ligand-dissociation kinetics.

Main Results:

  • The automated approach accurately classifies molecular trajectories, outperforming manual classification.
  • The method successfully distinguishes parallel dissociation channels within identified pathways.
  • Calculated unbinding timescales along the fastest path show agreement with experimental residence times.

Conclusions:

  • The developed data-driven protocol provides a physically interpretable and accurate method for analyzing ligand dissociation.
  • This technique facilitates the exploration of ligand-dissociation pathways and the calculation of path-specific thermodynamic and kinetic properties.
  • Integration with enhanced sampling algorithms can significantly advance early-stage drug discovery and property calculation.