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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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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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AutoPocket2CREST: Automating Binding Pocket Extraction for the CREST Conformer Generation Pipeline.

Christian Fellinger1,2,3, Marion Sappl4,5, András Szabadi4

  • 1Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, Josef-Holaubek-Platz 2, Vienna 1090, Austria.

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|March 23, 2026
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Summary
This summary is machine-generated.

AutoPocket2CREST automates protein-ligand pocket preparation for CREST conformational sampling. This workflow enables reproducible, semiempirical analysis of protein-bound ligands using open-source tools.

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Protein-ligand interactions are crucial in biological processes and drug development.
  • Accurate conformational sampling of ligands within protein binding pockets is essential for understanding binding mechanisms and designing new therapeutics.
  • Existing methods for preparing protein-ligand complexes for conformational analysis can be labor-intensive and lack reproducibility.

Purpose of the Study:

  • To develop an automated workflow, AutoPocket2CREST, for preparing protein-ligand binding pockets for conformational sampling.
  • To enable reproducible semiempirical conformational sampling of protein-bound ligands.
  • To integrate common open-source tools into a cohesive and efficient pipeline.

Main Methods:

  • The AutoPocket2CREST workflow takes protein and ligand structures as input.
  • It automatically identifies the ligand, constructs a chemically consistent binding pocket, and applies optional backbone constraints.
  • The method also postprocesses the resulting CREST conformers to restore structural annotations.

Main Results:

  • AutoPocket2CREST successfully automates the preparation of protein-ligand complexes for CREST conformational sampling.
  • The workflow ensures chemical consistency of the constructed pockets.
  • It facilitates reproducible semiempirical conformational sampling of protein-bound ligands.

Conclusions:

  • AutoPocket2CREST provides an efficient and reproducible automated solution for preparing protein-ligand systems for conformational analysis.
  • The integration of open-source tools makes the method accessible and adaptable.
  • This workflow can significantly aid in the study of protein-ligand interactions and accelerate drug discovery efforts.