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Structural assembly of two-domain proteins by rigid-body docking
Tammy M K Cheng1, Tom L Blundell, Juan Fernandez-Recio
1Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK. tammy@cryst.bioc.cam.ac.uk
BMC Bioinformatics
|October 18, 2008
Summary
Predicting protein structures with multiple domains is challenging. A new rigid-body docking method, pyDockTET, accurately models domain-domain interactions using linker restraints, improving structural biology predictions.
Area of Science:
- Structural Biology
- Computational Biology
- Biophysics
Background:
- Accurate protein structure prediction, especially for multi-domain proteins, remains a significant challenge in structural biology.
- Homology modeling often fails to capture inter-domain interactions, necessitating ab initio approaches.
- Predicting the relative orientation of protein domains is crucial for understanding protein function.
Purpose of the Study:
- To develop and evaluate a novel computational approach for predicting the structure of two-domain proteins.
- To model domain-domain interactions using rigid-body docking and linker-based restraints.
- To improve the accuracy of protein structural predictions for multi-domain proteins.
Main Methods:
- Developed pyDockTET (tethered-docking), a method employing rigid-body docking for domain-domain pose generation.
- Scored docked poses using binding energy and a pseudo-energy term derived from linker end-to-end distances.
- Benchmarked the method on 77 non-redundant domain pairs with known X-ray structures.
Main Results:
- The ZDOCK docking method generated acceptable domain-domain orientations in 51 out of 77 cases.
- pyDockTET successfully identified the correct domain assembly within the top 10 solutions in over 60% of cases.
- For homology-modeled domains, pyDockTET ranked the correct assembly within the top 10 in approximately 70% of cases.
Conclusions:
- Rigid-body docking combined with energy scoring and linker restraints effectively models domain-domain interactions.
- The findings support the utility of this approach for predicting the structures of multi-domain proteins.
- This work encourages the development of advanced methods for predicting structures of proteins with more than two domains.
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