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Updated: Mar 7, 2026

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

Information-Driven, Ensemble Flexible Peptide Docking Using HADDOCK.

Cunliang Geng¹, Siddarth Narasimhan¹, João P G L M Rodrigues^1,2

¹Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands.

Methods in Molecular Biology (Clifton, N.J.)

|February 26, 2017

Summary

This summary is machine-generated.

This study introduces a new protocol for protein-peptide docking that combines common peptide structures with molecular dynamics simulations. This hybrid approach improves the accuracy of predicting how proteins and peptides bind together.

Keywords:

Ensemble docking Flexibility HADDOCK Information-driven docking Molecular dynamics simulations Protein-peptide docking

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Published on: July 25, 2013

Area of Science:

Computational Biology
Structural Biology
Biochemistry

Background:

Protein-peptide interactions are crucial in biological processes.
Modeling these interactions is challenging due to peptide flexibility.
Existing docking methods struggle to capture diverse peptide conformations.

Purpose of the Study:

To develop an improved protocol for modeling protein-peptide interactions.
To enhance the sampling of peptide conformations during docking.
To increase the accuracy of protein-peptide complex prediction.

Main Methods:

A hybrid approach combining known peptide conformations with molecular dynamics (MD) simulations.
Generating an ensemble of peptide conformations for docking.
Utilizing information-driven flexible docking with HADDOCK.
Employing knowledge of the receptor's binding site to guide docking.

Main Results:

The developed protocol was tested on six protein-peptide complexes of varying difficulty.
Incorporating MD-derived peptide conformations generally improved docking model quality.
The approach successfully broadened the conformational space sampled for docking.

Conclusions:

The hybrid protocol offers a more robust method for modeling protein-peptide interactions.
This strategy enhances the accuracy of predicting peptide binding modes.
The findings suggest a promising direction for computational drug discovery targeting protein-peptide interactions.