Predicting zinc binding at the proteome level.

ABSTRACT:

Background

Metalloproteins are proteins capable of binding one or more metal ions, which may be required for their biological function, for regulation of their activities or for structural purposes. Metal-binding properties remain difficult to predict as well as to investigate experimentally at the whole-proteome level. Consequently, the current knowledge about metalloproteins is only partial.

Results

The present work reports on the development of a machine learning method for the prediction of the zinc-binding state of pairs of nearby amino-acids, using predictors based on support vector machines. The predictor was trained using chains containing zinc-binding sites and non-metalloproteins in order to provide positive and negative examples. Results based on strong non-redund

SUBMITTER: Passerini A 

PROVIDER: S-EPMC1800866 | biostudies-literature | 2007 Feb

REPOSITORIES: biostudies-literature