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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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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Memetic algorithms for ligand expulsion from protein cavities.

J Rydzewski1, W Nowak1

  • 1Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Grudziadzka 5, 87-100 Torun, Poland.

The Journal of Chemical Physics
|October 3, 2015
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Summary

Two novel memetic algorithms, Memory Enhanced Random Acceleration (MERA) Molecular Dynamics (MD) and Immune Algorithm (IA), efficiently model ligand diffusion pathways in proteins. These methods outperform traditional simulations for exploring protein channels and predicting ligand escape routes.

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Area of Science:

  • Computational Biology
  • Biophysics
  • Molecular Dynamics

Background:

  • Ligand diffusion within protein interiors is crucial for biological signaling and enzyme function.
  • Complex protein channel topologies challenge classical molecular dynamics simulations for modeling ligand escape pathways.

Purpose of the Study:

  • To introduce novel memetic algorithms for efficient ligand diffusion pathway searching and protein cavity exploration.
  • To enhance the modeling of ligand transport through complex protein channel systems.

Main Methods:

  • Development of Memory Enhanced Random Acceleration (MERA) Molecular Dynamics, incorporating a pheromone concept to optimize expulsion forces.
  • Implementation of an Immune Algorithm (IA) utilizing hybrid learning protocols for predicting ligand exit paths.
  • Testing and comparison of MERA and IA against simulated annealing and random acceleration molecular dynamics on diverse protein channel models.

Main Results:

  • MERA and IA demonstrated superior performance in modeling ligand escape pathways compared to existing methods.
  • The algorithms successfully navigated and explored complex protein channel structures, including M2 muscarinic G-protein-coupled receptor, nitrile hydratase, and cytochrome P450cam.
  • Both memetic methods proved effective in accelerating ligand transport through intricate channel networks.

Conclusions:

  • The proposed MERA and IA algorithms offer a significant advancement in simulating ligand diffusion and escape pathways in proteins.
  • These generalizable memetic approaches are suitable for studying accelerated transport phenomena in various channel network systems.
  • The findings provide powerful computational tools for understanding fundamental biological processes involving ligand-protein interactions.