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Conserved Binding Sites01:49

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Cooperative Allosteric Transitions01:58

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Updated: Jul 2, 2026

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Docking and scoring with alternative side-chain conformations.

Christoph Hartmann1, Iris Antes, Thomas Lengauer

  • 1Department of Computational Biology and Applied Algorithmics, Max-Planck-Institut für Informatik, Saarbrücken, Germany.

Proteins
|August 16, 2008
PubMed
Summary
This summary is machine-generated.

We developed a new scoring method for drug docking into protein models. This approach accurately estimates binding affinity and speeds up virtual screening for drug discovery.

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Last Updated: Jul 2, 2026

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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Structure-Guided Design and Development of Novel Cyclophilin A Inhibitors and Ganoderiol-F Derivatives: An In-Silico Approach
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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Drug Discovery

Background:

  • Accurate protein-ligand binding prediction is crucial for drug discovery.
  • Protein models with flexible or modeled side chains present challenges for docking accuracy.

Purpose of the Study:

  • To present a scoring and modeling procedure for docking ligands into protein models.
  • To improve the accuracy of virtual screening using protein models.

Main Methods:

  • Generated new potentials of mean force for the ROTA scoring function.
  • Utilized the IRECS tool for side-chain conformation optimization.
  • Employed FlexX and FlexE docking programs with ROTA scoring.
  • Validated the approach on the DUD database.

Main Results:

  • ROTA potentials effectively estimate ligand-protein binding affinity.
  • The procedure compensates for performance loss from using rotamer libraries.
  • Achieved an average runtime of 168 seconds per ligand, enabling virtual screening.

Conclusions:

  • The developed method enhances ligand docking accuracy with protein models.
  • This approach facilitates efficient virtual screening for identifying drug candidates.