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Ligand Binding Sites02:40

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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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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.
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The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
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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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PatchSurfers: Two methods for local molecular property-based binding ligand prediction.

Woong-Hee Shin1, Mark Gregory Bures2, Daisuke Kihara3

  • 1Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA.

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|October 3, 2015
PubMed
Summary

Predicting protein binding ligands aids gene function discovery. New methods, Patch-Surfer and PL-PatchSurfer, use 3D Zernike descriptors for efficient pocket-ligand comparisons in computational biology.

Keywords:
3D Zernike descriptorLigand binding pocketsLigand–protein interactionPatch-SurferProtein function predictionStructure–function relationship

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Area of Science:

  • Computational biology
  • Drug discovery
  • Structural bioinformatics

Background:

  • Protein function prediction is crucial for biological research and drug development.
  • Predicting binding ligands is a key aspect of function prediction, directly testable via assays.
  • Existing methods compare protein pockets or pockets to ligands, with varying efficiencies.

Purpose of the Study:

  • To introduce and emphasize the PL-PatchSurfer method for binding ligand prediction.
  • To present Patch-Surfer and PL-PatchSurfer, novel computational tools for ligand binding prediction.
  • To demonstrate the utility of surface patch-based descriptions and 3D Zernike descriptors (3DZD) in this domain.

Main Methods:

  • Developed Patch-Surfer (pocket-pocket comparison) and PL-PatchSurfer (pocket-ligand comparison) methods.
  • Utilized surface patch-based descriptions capturing shape, hydrophobicity, and electrostatics.
  • Employed 3D Zernike descriptors (3DZD) for rotationally invariant and fast comparison of molecular surfaces.

Main Results:

  • PL-PatchSurfer offers a more recent and effective approach for binding ligand prediction.
  • The methods demonstrate successful application in binding ligand prediction.
  • Performance examples showcase utility in virtual drug screening.

Conclusions:

  • PL-PatchSurfer is a valuable tool for predicting protein binding ligands.
  • Surface patch analysis with 3DZD provides an efficient framework for molecular interactions.
  • These methods facilitate hypothesis generation and experimental validation in computational biology.