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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...

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Methodology for Studying Interactions of Vitamin A Membrane Receptors and Opsin Protein with their Ligands in Generating the Retinylidene Protein
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Interaction Persistence-Based Identification of Key Binding Residues in the Cellular Retinol-Binding Protein 1

Hyeona Kang1, Sun-Gu Lee1

  • 1Department of Chemical and Biomolecular Engineering, Pusan National University, Busan 46241, Korea (Republic of).

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|June 29, 2026
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Summary

We developed a new computational method to identify persistent protein-ligand interactions using molecular dynamics simulations. This framework quantifies interaction stability over time, aiding in drug design.

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Pharmacology

Background:

  • Identifying stable protein-ligand interactions is crucial for understanding molecular mechanisms and drug design.
  • Current methods often fail to capture the temporal dynamics of these interactions.
  • Molecular dynamics (MD) simulations offer detailed temporal information but require advanced analysis techniques.

Purpose of the Study:

  • To develop and validate a novel computational framework for quantifying dynamically persistent residue-ligand interactions.
  • To identify key residues with long-lived interactions within a protein-ligand complex.
  • To provide a quantitative basis for residence time-oriented drug design.

Main Methods:

  • Utilized MD simulations to generate protein-ligand conformational trajectories.
  • Developed two normalized interaction descriptors: a distance-based proximity metric and a dipole-based alignment metric.
  • Applied autocorrelation function (ACF) analysis to quantify interaction memory persistence and decomposed decay profiles into fast and slow relaxation modes.
  • Implemented a three-stage hierarchical filtering protocol to identify dynamically persistent interactions based on interaction strength, fit quality, and slow-mode amplitude.

Main Results:

  • The framework successfully quantified interaction persistence, with slow relaxation times (τ slow) reaching 14.5 ns (proximity) and 10.3 ns (alignment), significantly longer than typical protein motions.
  • Identified 30 residues using the proximity metric and 19 using the alignment metric.
  • Discovered 8 structural-dynamical hotspots at the intersection of both metrics, including key residues I77 and R108, confirming their roles in binding.

Conclusions:

  • The developed dynamics-based framework provides a robust method for identifying dynamically persistent protein-ligand interactions.
  • The identified hotspots offer valuable insights into binding site stability and function.
  • This approach is modular, applicable to various protein-ligand systems, and supports residence time-focused drug discovery.