Prediction of calcium-binding sites by combining loop-modeling with machine learning.

ABSTRACT:

Background

Protein ligand-binding sites in the apo state exhibit structural flexibility. This flexibility often frustrates methods for structure-based recognition of these sites because it leads to the absence of electron density for these critical regions, particularly when they are in surface loops. Methods for recognizing functional sites in these missing loops would be useful for recovering additional functional information.

Results

We report a hybrid approach for recognizing calcium-binding sites in disordered regions. Our approach combines loop modeling with a machine learning method (FEATURE) for structure-based site recognition. For validation, we compared the performance of our method on known calcium-binding sites for which there are both holo and apo structures. W

SUBMITTER: Liu T 

PROVIDER: S-EPMC2808310 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature