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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.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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ClusPro LigTBM: Automated Template-based Small Molecule Docking.

Andrey Alekseenko1, Sergei Kotelnikov2, Mikhail Ignatov3

  • 1Department of Applied Mathematics and Statistics, Stony Brook University, 11794 Stony Brook, NY, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, 11794 Stony Brook, NY, USA.

Journal of Molecular Biology
|December 22, 2019
PubMed
Summary
This summary is machine-generated.

We developed a novel template-based algorithm for automated protein-small molecule docking, ClusPro LigTBM server, to predict binding poses. This method enhances drug discovery by leveraging known ligand-protein complexes for accurate modeling.

Keywords:
homology modelsprotein-ligand dockingstructure refinementtemplate-based docking

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Template-based methods excel in protein-protein docking (e.g., Critical Assessment of Predicted Interactions - CAPRI).
  • Automated template-based approaches for protein-small molecule docking are scarce.
  • Accurate prediction of small molecule binding poses is crucial for drug design.

Purpose of the Study:

  • To introduce a novel algorithm and automated server, ClusPro LigTBM, for template-based protein-small molecule docking.
  • To provide a user-friendly tool for predicting small molecule binding poses using known complex templates.
  • To evaluate the performance of the developed method on benchmark datasets.

Main Methods:

  • The algorithm identifies known complexes with partial ligand overlap to serve as templates.
  • It employs conformational sampling and template-guided energy refinement to generate potential binding poses.
  • The generated poses are then scored to rank their likelihood.

Main Results:

  • The ClusPro LigTBM server was developed, accepting protein (PDB) and ligand (SMILES) inputs.
  • The server automatically searches for templates, performs docking, and presents top-scoring poses with confidence scores.
  • The method demonstrated effectiveness on the Astex Diverse benchmark and Drug Design Data Resource (D3R) Grand Challenge targets.

Conclusions:

  • The ClusPro LigTBM server offers an automated, template-based solution for protein-small molecule docking.
  • This tool can aid researchers in predicting ligand binding poses and accelerate drug design.
  • The publicly available server is integrated into the ClusPro docking suite.