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Related Experiment Video

Updated: Sep 2, 2025

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Consensus combining outcomes of multiple ensemble dockings: examples using dDAT crystalized complexes.

Fabiani Triches¹, Francieli Triches², Cilene Lino de Oliveira¹

¹Department of Physiological Sciences, Center of Biological Sciences, Federal University of Santa Catarina, University Campus, Trindade, Florianópolis, SC Brazil.

|August 8, 2022

Summary

Combining multiple molecular docking programs using exponential consensus ranking (ECR) provides reliable ligand-macromolecule interaction data. This ensemble docking approach accurately predicts relative ligand affinities, as demonstrated with the Drosophila melanogaster dopamine transporter (dDAT).

Keywords:

Autodock Vina Consensus docking DockThor Ensemble docking Gold Molecular docking Monoamines Monoamines transporters Redocking

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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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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

Computational chemistry
Molecular modeling
Drug discovery

Background:

Single molecular docking programs yield limited reliability for predicting ligand-macromolecule interactions.
Combining results from multiple docking tools enhances prediction accuracy and robustness.

Purpose of the Study:

To develop and validate an Exponential Consensus Ranking (ECR) method for integrating ensemble docking results.
To create a reliable protocol for re-docking and cross-docking using diverse docking software.

Main Methods:

Adapted ECR to merge re- and cross-docking results from multiple programs (Autodock Vina, Gold, DockThor).
Implemented a four-step process: scoring function determination, per-macromolecule ranking, cross-program ranking combination, and final averaged ranking.
Incorporated macromolecule conformational heterogeneity into the consensus score.

Main Results:

Generated a final ranking of average relative ligand affinities to the Drosophila melanogaster dopamine transporter (dDAT).
The adapted ECR method produced rankings consistent with experimentally determined affinity constants from literature.
Demonstrated the efficacy of ensemble docking for predicting ligand-target interactions.

Conclusions:

The adapted ECR method offers a robust consensus approach for combining ensemble docking data.
This protocol provides a reliable means to assess relative ligand affinities for molecular targets.
The method is applicable for ranking diverse ligands against specific macromolecules, aiding drug discovery efforts.