ATPsite: sequence-based prediction of ATP-binding residues.

ABSTRACT:

Background

ATP is a ubiquitous nucleotide that provides energy for cellular activities, catalyzes chemical reactions, and is involved in cellular signalling. The knowledge of the ATP-protein interactions helps with annotation of protein functions and finds applications in drug design. The sequence to structure annotation gap motivates development of high-throughput sequence-based predictors of the ATP-binding residues. Moreover, our empirical tests show that the only existing predictor, ATPint, is characterized by relatively low predictive quality.

Methods

We propose a novel, high-throughput machine learning-based predictor, ATPsite, which identifies ATP-binding residues from protein sequences. Our predictor utilizes Support Vector Machine classifier and a comprehensive set

SUBMITTER: Chen K 

PROVIDER: S-EPMC3289083 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature