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Updated: Jun 12, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Published on: July 25, 2013
A generalized approach to sampling backbone conformations with RosettaDock for CAPRI rounds 13-19
Aroop Sircar1, Sidhartha Chaudhury, Krishna Praneeth Kilambi
1Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.
Novel protein docking methods improved accuracy in challenging CAPRI rounds. New techniques like EnsembleDock and SnugDock enabled backbone sampling, leading to high-quality models for protein-protein interactions.
Area of Science:
- Computational Biology
- Structural Biology
- Biophysics
Background:
- The CAPRI (Critical Assessment of PRedicted Interactions) challenge assesses protein-protein docking accuracy.
- Recent CAPRI rounds presented difficulties due to unbound starting structures and homology models.
Purpose of the Study:
- To evaluate and enhance protein-protein docking strategies using RosettaDock and novel methods.
- To improve the accuracy of predicted protein complex structures in challenging scenarios.
Main Methods:
- Utilized RosettaDock for rigid body docking and introduced EnsembleDock and SnugDock for backbone conformational sampling.
- Applied experimental data to filter docking decoys for specific targets.
- Employed EnsembleDock to sample NMR ensembles for flexible protein targets.
Main Results:
- Achieved two high, one medium, and one acceptable accuracy model out of 13 targets in CAPRI rounds 13-19.
- Generated five high-quality models for Target 32 (α-amylase/subtilisin inhibitor-subtilisin savinase), including the top-ranked structure.
- Produced a medium accuracy structure for Target 41 (colicin-immunity protein) by sampling NMR ensembles.
- Determined high accuracy predictions for Target 40 (trypsin-inhibitor) by integrating experimental binding site data.
Conclusions:
- Novel docking methods incorporating backbone sampling enhance model accuracy, particularly for challenging targets.
- The developed toolset shows robustness against homology modeling errors but requires further improvements for larger backbone uncertainties and global sampling.
- A generalized approach to selecting docking methods based on target characteristics is discussed.
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