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Related Experiment Video

Updated: Mar 15, 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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Pushing the Backbone in Protein-Protein Docking.

Daisuke Kuroda¹, Jeffrey J Gray²

¹Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Analytical and Physical Chemistry, Showa University School of Pharmacy, Tokyo 142-8555, Japan.

Structure (London, England : 1993)

|August 30, 2016

Summary

This summary is machine-generated.

Computational docking struggles with protein flexibility. Current methods capture only a fraction of protein backbone motion during binding, leaving a significant gap in accurately predicting molecular interactions.

Keywords:

backbone flexibility conformer selection induced-fit molecular recognition protein-protein docking

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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 biology
Structural biology
Biophysics

Background:

Protein conformational changes upon binding present a major challenge for computational docking algorithms.
Accurately modeling these dynamic transitions is crucial for understanding molecular recognition and drug design.

Purpose of the Study:

To evaluate the ability of existing computational methods to capture protein backbone conformational changes related to ligand binding.
To identify limitations in current algorithms for sampling bound-like protein conformations from unbound states.

Main Methods:

Tested seven computational techniques, including Monte Carlo-based sampling, molecular dynamics, and normal mode analysis.
Assessed the sampling of near-bound states from unbound conformations.
Created docking energy landscapes by forcing unbound backbones towards bound conformations.

Main Results:

All tested methods rarely sampled near-bound states from unbound conformations.
Predicted motion directions generally overlapped with actual conformational changes.
Docking success rates improved significantly (70%) when unbound backbones were computationally guided towards bound states (within 0.6 Å).

Conclusions:

Current computational methods have limited ability to capture unbound-to-bound protein backbone transitions.
Conformer selection and induced-fit methods account for approximately 79% of transitions, highlighting a persistent 21% gap in modeling backbone motion.