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Defining the limits of homology modeling in information-driven protein docking
J P G L M Rodrigues1, A S J Melquiond, E Karaca
1Faculty of Science/Chemistry, Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, 3584CH, The Netherlands.
Proteins
|August 6, 2013
Summary
Global sequence identity predicts protein-protein docking model quality. Reliable interface data enables accurate predictions even with low sequence identity, defining docking limits.
Area of Science:
- Structural Biology
- Computational Biology
- Bioinformatics
Background:
- Information-driven docking is a leading method for modeling protein interactions, as seen in CAPRI.
- Limited availability of 3D structures for interacting proteins hinders docking.
- Homology modeling, aided by large-scale structural data, can model many proteins.
Purpose of the Study:
- To define the limits of information-driven docking using homology models.
- To identify reliable predictors for docking model quality.
- To guide users on the trustworthiness of docking predictions.
Main Methods:
- Analysis of previous CAPRI targets.
- Evaluation of various measures to predict docking model quality.
- Assessment of the impact of interface information quality on prediction success.
Main Results:
- Global sequence identity between template and target reliably predicts docking model quality.
- A well-defined protein fold is crucial for successful interaction modeling.
- High-quality interface data allows for acceptable predictions even at low sequence identity.
Conclusions:
- Global sequence identity is a simple yet effective predictor for homology model-based docking.
- The quality of experimental or computational interface data is critical for docking success.
- These findings establish boundaries for reliable protein-protein docking predictions.
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