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GalaxyPepDock: a protein-peptide docking tool based on interaction similarity and energy optimization.

Lee H, Heo L, Lee MS, Seok C - Nucleic Acids Res. (2015)

Bottom Line: Therefore, effective protein-peptide docking techniques can provide the basis for potential therapeutic applications by enabling an atomic-level understanding of protein interactions.With the increasing number of protein-peptide structures deposited in the protein data bank, the prediction accuracy of protein-peptide docking can be enhanced by utilizing the information provided by the database.The performance of GalaxyPepDock is superior to those of the other currently available web servers when tested on the PeptiDB set and on recently released complex structures.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry, Seoul National University, Seoul 151-747, Korea.

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Related in: MedlinePlus

Flowchart of the GalaxyPepDock algorithm. Given a protein structure and a peptide sequence, template complex structures are first selected from the PepBind database based on protein structure similarity and protein–peptide interaction similarity. Models are then built with the model-building tool of GalaxyTBM, and the 10 models that are selected based on energy are returned after further optimization by the GalaxyRefine flexible refinement method.
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Figure 1: Flowchart of the GalaxyPepDock algorithm. Given a protein structure and a peptide sequence, template complex structures are first selected from the PepBind database based on protein structure similarity and protein–peptide interaction similarity. Models are then built with the model-building tool of GalaxyTBM, and the 10 models that are selected based on energy are returned after further optimization by the GalaxyRefine flexible refinement method.

Mentions: The overall procedure of template selection and model-building is illustrated in Figure 1 and is described in more detail below.


GalaxyPepDock: a protein-peptide docking tool based on interaction similarity and energy optimization.

Lee H, Heo L, Lee MS, Seok C - Nucleic Acids Res. (2015)

Flowchart of the GalaxyPepDock algorithm. Given a protein structure and a peptide sequence, template complex structures are first selected from the PepBind database based on protein structure similarity and protein–peptide interaction similarity. Models are then built with the model-building tool of GalaxyTBM, and the 10 models that are selected based on energy are returned after further optimization by the GalaxyRefine flexible refinement method.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4489314&req=5

Figure 1: Flowchart of the GalaxyPepDock algorithm. Given a protein structure and a peptide sequence, template complex structures are first selected from the PepBind database based on protein structure similarity and protein–peptide interaction similarity. Models are then built with the model-building tool of GalaxyTBM, and the 10 models that are selected based on energy are returned after further optimization by the GalaxyRefine flexible refinement method.
Mentions: The overall procedure of template selection and model-building is illustrated in Figure 1 and is described in more detail below.

Bottom Line: Therefore, effective protein-peptide docking techniques can provide the basis for potential therapeutic applications by enabling an atomic-level understanding of protein interactions.With the increasing number of protein-peptide structures deposited in the protein data bank, the prediction accuracy of protein-peptide docking can be enhanced by utilizing the information provided by the database.The performance of GalaxyPepDock is superior to those of the other currently available web servers when tested on the PeptiDB set and on recently released complex structures.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry, Seoul National University, Seoul 151-747, Korea.

Show MeSH
Related in: MedlinePlus