Literature DB >> 16448030

PoPS: a computational tool for modeling and predicting protease specificity.

Sarah E Boyd1, Maria Garcia de la Banda, Robert N Pike, James C Whisstock, George B Rudy.   

Abstract

Proteases play a fundamental role in the control of intra- and extracellular processes by binding and cleaving specific amino acid sequences. Identifying these targets is extremely challenging. Current computational attempts to predict cleavage sites are limited, representing these amino acid sequences as patterns or frequency matrices. Here we present PoPS, a publicly accessible bioinformatics tool (http://pops.csse.monash.edu.au/) which provides a novel method for building computational models of protease specificity that, while still being based on these amino acid sequences, can be built from any experimental data or expert knowledge available to the user. PoPS specificity models can be used to predict and rank likely cleavages within a single substrate, and within entire proteomes. Other factors, such as the secondary or tertiary structure of the substrate, can be used to screen unlikely sites. Furthermore, the tool also provides facilities to infer, compare and test models, and to store them in a publicly accessible database.

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Substances:

Year:  2004        PMID: 16448030     DOI: 10.1109/csb.2004.1332450

Source DB:  PubMed          Journal:  Proc IEEE Comput Syst Bioinform Conf        ISSN: 1551-7497


  5 in total

1.  Global identification of multiple substrates for Plasmodium falciparum SUB1, an essential malarial processing protease.

Authors:  Natalie C Silmon de Monerri; Helen R Flynn; Marta G Campos; Fiona Hackett; Konstantinos Koussis; Chrislaine Withers-Martinez; J Mark Skehel; Michael J Blackman
Journal:  Infect Immun       Date:  2011-01-10       Impact factor: 3.441

2.  GPS-CCD: a novel computational program for the prediction of calpain cleavage sites.

Authors:  Zexian Liu; Jun Cao; Xinjiao Gao; Qian Ma; Jian Ren; Yu Xue
Journal:  PLoS One       Date:  2011-04-20       Impact factor: 3.240

3.  Analysis of the minimal specificity of caspase-2 and identification of Ac-VDTTD-AFC as a caspase-2-selective peptide substrate.

Authors:  Tanja Kitevska; Sarah J Roberts; Delara Pantaki-Eimany; Sarah E Boyd; Fiona L Scott; Christine J Hawkins
Journal:  Biosci Rep       Date:  2014-04-01       Impact factor: 3.840

4.  Software-aided workflow for predicting protease-specific cleavage sites using physicochemical properties of the natural and unnatural amino acids in peptide-based drug discovery.

Authors:  Tatiana Radchenko; Fabien Fontaine; Luca Morettoni; Ismael Zamora
Journal:  PLoS One       Date:  2019-01-08       Impact factor: 3.240

5.  Knowledge-transfer learning for prediction of matrix metalloprotease substrate-cleavage sites.

Authors:  Yanan Wang; Jiangning Song; Tatiana T Marquez-Lago; André Leier; Chen Li; Trevor Lithgow; Geoffrey I Webb; Hong-Bin Shen
Journal:  Sci Rep       Date:  2017-07-18       Impact factor: 4.379

  5 in total

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