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StoneHinge: hinge prediction by network analysis of individual protein structures.
Kevin S Keating1, Samuel C Flores, Mark B Gerstein
1Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA.
Protein Science : a Publication of the Protein Society
|January 31, 2009
Summary
StoneHinge accurately predicts protein hinges, crucial for molecular recognition and flexibility. This novel method combines two analyses to identify domain-hinge-domain structures, improving specificity and reducing false positives.
Area of Science:
- Structural biology
- Computational biophysics
Background:
- Protein hinge motions are vital for molecular recognition and understanding protein flexibility.
- Accurate prediction of hinges aids in protein structure analysis and mutation impact assessment.
Purpose of the Study:
- To introduce StoneHinge, a novel computational approach for predicting hinges between protein domains.
- To evaluate the performance of StoneHinge by comparing its predictions with existing literature and visual inspection.
Main Methods:
- StoneHinge integrates two analyses of noncovalent bond networks: StoneHingeP (using ProFlex) and StoneHingeD (using DomDecomp Gaussian network).
- Predictions were validated against literature-defined hinges and visual inspection of interpolated protein motions.
Main Results:
- StoneHingeP accurately identified known hinges, sometimes including extra residues, and correctly identified hinge-free proteins.
- StoneHingeD precisely located hinges but tended to overpredict their number.
- The consensus of StoneHinge predicted 11 of 13 known hinges in nine open protein structures with no false positives.
Conclusions:
- StoneHinge offers a robust and specific method for predicting protein hinges.
- The consensus approach significantly enhances hinge prediction accuracy and reliability compared to other methods.
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