title: A computational approach for detecting peptidases and their specific inhibitors at the genome level.
abstract: BACKGROUND
  Peptidases are proteolytic enzymes responsible for fundamental cellular activities in all organisms. Apparently about 2-5% of the genes encode for peptidases, irrespectively of the organism source. The basic peptidase function is "protein digestion" and this can be potentially dangerous in living organisms when it is not strictly controlled by specific inhibitors. In genome annotation a basic question is to predict gene function. Here we describe a computational approach that can filter peptidases and their inhibitors out of a given proteome. Furthermore and as an added value to MEROPS, a specific database for peptidases already available in the public domain, our method can predict whether a pair of peptidase/inhibitor can interact, eventually listing all possible predicted ligands (peptidases and/or inhibitors).
  
  
  RESULTS
  We show that by adopting a decision-tree approach the accuracy of PROSITE and HMMER in detecting separately the four major peptidase types (Serine, Aspartic, Cysteine and Metallo- Peptidase) and their inhibitors among a non redundant set of globular proteins can be improved by some percentage points with respect to that obtained with each method separately. More importantly, our method can then predict pairs of peptidases and interacting inhibitors, scoring a joint global accuracy of 99% with coverage for the positive cases (peptidase/inhibitor) close to 100% and a correlation coefficient of 0.91%. In this task the decision-tree approach outperforms the single methods.
  
  
  CONCLUSIONS
  The decision-tree can reliably classify protein sequences as peptidases or inhibitors, belonging to a certain class, and can provide a comprehensive list of possible interacting pairs of peptidase/inhibitor. This information can help the design of experiments to detect interacting peptidase/inhibitor complexes and can speed up the selection of possible interacting candidates, without searching for them separately and manually combining the obtained results. A web server specifically developed for annotating peptidases and their inhibitors (HIPPIE) is available at http://gpcr.biocomp.unibo.it/cgi/predictors/hippie/pred_hippie.cgi.
authors: Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, Bologna, Italy. lisa@biocomp.unibo.it
authors: Lisa
authors: L
authors: Bartoli
authors: Remo
authors: R
authors: Calabrese
authors: Piero
authors: P
authors: Fariselli
authors: Damiano G
authors: DG
authors: Mita
authors: Rita
authors: R
authors: Casadio
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001665:Binding Sites; D023281:Genomics / Q000379:methods; D008969:Molecular Sequence Data; D010447:Peptide Hydrolases / Q000737:chemistry*; D011480:Protease Inhibitors / Q000737:chemistry*; D011485:Protein Binding; D020543:Proteome / Q000037:antagonists & inhibitors / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011480:Protease Inhibitors; D020543:Proteome; D010447:Peptide Hydrolases