Literature DB >> 29069280

PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy.

Jiangning Song1,2,3, Fuyi Li2, André Leier4,5, Tatiana T Marquez-Lago4,5, Tatsuya Akutsu6, Gholamreza Haffari1, Kuo-Chen Chou7,8,9, Geoffrey I Webb1, Robert N Pike3,10, John Hancock.   

Abstract

Summary: Proteases are enzymes that specifically cleave the peptide backbone of their target proteins. As an important type of irreversible post-translational modification, protein cleavage underlies many key physiological processes. When dysregulated, proteases' actions are associated with numerous diseases. Many proteases are highly specific, cleaving only those target substrates that present certain particular amino acid sequence patterns. Therefore, tools that successfully identify potential target substrates for proteases may also identify previously unknown, physiologically relevant cleavage sites, thus providing insights into biological processes and guiding hypothesis-driven experiments aimed at verifying protease-substrate interaction. In this work, we present PROSPERous, a tool for rapid in silico prediction of protease-specific cleavage sites in substrate sequences. Our tool is based on logistic regression models and uses different scoring functions and their pairwise combinations to subsequently predict potential cleavage sites. PROSPERous represents a state-of-the-art tool that enables fast, accurate and high-throughput prediction of substrate cleavage sites for 90 proteases. Availability and implementation: http://prosperous.erc.monash.edu/. Contact: jiangning.song@monash.edu or geoff.webb@monash.edu or r.pike@latrobe.edu.au. Supplementary information: Supplementary data are available at Bioinformatics online.
© The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

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Year:  2018        PMID: 29069280      PMCID: PMC5860617          DOI: 10.1093/bioinformatics/btx670

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  24 in total

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Authors:  David A duVerle; Hiroshi Mamitsuka
Journal:  Brief Bioinform       Date:  2011-12-02       Impact factor: 11.622

2.  CaSPredictor: a new computer-based tool for caspase substrate prediction.

Authors:  H M Garay-Malpartida; J M Occhiucci; J Alves; J E Belizário
Journal:  Bioinformatics       Date:  2005-06       Impact factor: 6.937

3.  Identification of proteases and their types.

Authors:  Hong-Bin Shen; Kuo-Chen Chou
Journal:  Anal Biochem       Date:  2008-11-01       Impact factor: 3.365

4.  ProtIdent: a web server for identifying proteases and their types by fusing functional domain and sequential evolution information.

Authors:  Kuo-Chen Chou; Hong-Bin Shen
Journal:  Biochem Biophys Res Commun       Date:  2008-09-05       Impact factor: 3.575

5.  Proteome-derived, database-searchable peptide libraries for identifying protease cleavage sites.

Authors:  Oliver Schilling; Christopher M Overall
Journal:  Nat Biotechnol       Date:  2008-05-25       Impact factor: 54.908

6.  Pripper: prediction of caspase cleavage sites from whole proteomes.

Authors:  Mirva Piippo; Niina Lietzén; Olli S Nevalainen; Jussi Salmi; Tuula A Nyman
Journal:  BMC Bioinformatics       Date:  2010-06-15       Impact factor: 3.169

7.  Cascleave 2.0, a new approach for predicting caspase and granzyme cleavage targets.

Authors:  Mingjun Wang; Xing-Ming Zhao; Hao Tan; Tatsuya Akutsu; James C Whisstock; Jiangning Song
Journal:  Bioinformatics       Date:  2013-10-21       Impact factor: 6.937

8.  Global mapping of the topography and magnitude of proteolytic events in apoptosis.

Authors:  Melissa M Dix; Gabriel M Simon; Benjamin F Cravatt
Journal:  Cell       Date:  2008-08-22       Impact factor: 41.582

9.  CD-HIT: accelerated for clustering the next-generation sequencing data.

Authors:  Limin Fu; Beifang Niu; Zhengwei Zhu; Sitao Wu; Weizhong Li
Journal:  Bioinformatics       Date:  2012-10-11       Impact factor: 6.937

10.  Twenty years of the MEROPS database of proteolytic enzymes, their substrates and inhibitors.

Authors:  Neil D Rawlings; Alan J Barrett; Robert Finn
Journal:  Nucleic Acids Res       Date:  2015-11-02       Impact factor: 16.971

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  32 in total

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Authors:  Meng Zhang; Fuyi Li; Tatiana T Marquez-Lago; André Leier; Cunshuo Fan; Chee Keong Kwoh; Kuo-Chen Chou; Jiangning Song; Cangzhi Jia
Journal:  Bioinformatics       Date:  2019-09-01       Impact factor: 6.937

2.  Predicting membrane proteins and their types by extracting various sequence features into Chou's general PseAAC.

Authors:  Ahmad Hassan Butt; Nouman Rasool; Yaser Daanial Khan
Journal:  Mol Biol Rep       Date:  2018-09-20       Impact factor: 2.316

3.  Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods.

Authors:  Fuyi Li; Yanan Wang; Chen Li; Tatiana T Marquez-Lago; André Leier; Neil D Rawlings; Gholamreza Haffari; Jerico Revote; Tatsuya Akutsu; Kuo-Chen Chou; Anthony W Purcell; Robert N Pike; Geoffrey I Webb; A Ian Smith; Trevor Lithgow; Roger J Daly; James C Whisstock; Jiangning Song
Journal:  Brief Bioinform       Date:  2019-11-27       Impact factor: 11.622

4.  Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework.

Authors:  Fuyi Li; Jinxiang Chen; Zongyuan Ge; Ya Wen; Yanwei Yue; Morihiro Hayashida; Abdelkader Baggag; Halima Bensmail; Jiangning Song
Journal:  Brief Bioinform       Date:  2021-03-22       Impact factor: 11.622

5.  Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome.

Authors:  Fuyi Li; Chen Li; Tatiana T Marquez-Lago; André Leier; Tatsuya Akutsu; Anthony W Purcell; A Ian Smith; Trevor Lithgow; Roger J Daly; Jiangning Song; Kuo-Chen Chou
Journal:  Bioinformatics       Date:  2018-12-15       Impact factor: 6.937

6.  DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites.

Authors:  Fuyi Li; Jinxiang Chen; André Leier; Tatiana Marquez-Lago; Quanzhong Liu; Yanze Wang; Jerico Revote; A Ian Smith; Tatsuya Akutsu; Geoffrey I Webb; Lukasz Kurgan; Jiangning Song
Journal:  Bioinformatics       Date:  2020-02-15       Impact factor: 6.937

7.  Predicting Proteolysis in Complex Proteomes Using Deep Learning.

Authors:  Matiss Ozols; Alexander Eckersley; Christopher I Platt; Callum Stewart-McGuinness; Sarah A Hibbert; Jerico Revote; Fuyi Li; Christopher E M Griffiths; Rachel E B Watson; Jiangning Song; Mike Bell; Michael J Sherratt
Journal:  Int J Mol Sci       Date:  2021-03-17       Impact factor: 5.923

8.  Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework.

Authors:  Yanju Zhang; Ruopeng Xie; Jiawei Wang; André Leier; Tatiana T Marquez-Lago; Tatsuya Akutsu; Geoffrey I Webb; Kuo-Chen Chou; Jiangning Song
Journal:  Brief Bioinform       Date:  2019-11-27       Impact factor: 11.622

9.  Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.

Authors:  Zhen Chen; Xuhan Liu; Fuyi Li; Chen Li; Tatiana Marquez-Lago; André Leier; Tatsuya Akutsu; Geoffrey I Webb; Dakang Xu; Alexander Ian Smith; Lei Li; Kuo-Chen Chou; Jiangning Song
Journal:  Brief Bioinform       Date:  2019-11-27       Impact factor: 11.622

10.  Characterization of a Stable Form of Carboxypeptidase G2 (Glucarpidase), a Potential Biobetter Variant, From Acinetobacter sp. 263903-1.

Authors:  Issa Sadeghian; Shiva Hemmati
Journal:  Mol Biotechnol       Date:  2021-07-15       Impact factor: 2.695

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