A computational approach for detecting peptidases and their specific inhibitors at the genome level

<p>Abstract</p> <p>Background</p> <p>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 &qu...

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Main Authors: Bartoli Lisa, Calabrese Remo, Fariselli Piero, Mita Damiano G, Casadio Rita
Format: Article
Language:English
Published: BMC 2007-03-01
Series:BMC Bioinformatics
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spelling doaj-6e6397b8e17c4dc583b1a2b64eff79232020-11-25T00:36:58ZengBMCBMC Bioinformatics1471-21052007-03-018Suppl 1S310.1186/1471-2105-8-S1-S3A computational approach for detecting peptidases and their specific inhibitors at the genome levelBartoli LisaCalabrese RemoFariselli PieroMita Damiano GCasadio Rita<p>Abstract</p> <p>Background</p> <p>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).</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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 <url>http://gpcr.biocomp.unibo.it/cgi/predictors/hippie/pred_hippie.cgi</url></p>
collection DOAJ
language English
format Article
sources DOAJ
author Bartoli Lisa
Calabrese Remo
Fariselli Piero
Mita Damiano G
Casadio Rita
spellingShingle Bartoli Lisa
Calabrese Remo
Fariselli Piero
Mita Damiano G
Casadio Rita
A computational approach for detecting peptidases and their specific inhibitors at the genome level
BMC Bioinformatics
author_facet Bartoli Lisa
Calabrese Remo
Fariselli Piero
Mita Damiano G
Casadio Rita
author_sort Bartoli Lisa
title A computational approach for detecting peptidases and their specific inhibitors at the genome level
title_short A computational approach for detecting peptidases and their specific inhibitors at the genome level
title_full A computational approach for detecting peptidases and their specific inhibitors at the genome level
title_fullStr A computational approach for detecting peptidases and their specific inhibitors at the genome level
title_full_unstemmed A computational approach for detecting peptidases and their specific inhibitors at the genome level
title_sort computational approach for detecting peptidases and their specific inhibitors at the genome level
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2007-03-01
description <p>Abstract</p> <p>Background</p> <p>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).</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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 <url>http://gpcr.biocomp.unibo.it/cgi/predictors/hippie/pred_hippie.cgi</url></p>
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