Literature DB >> 34236664

Protein Secretion Prediction Tools and Extracellular Vesicles Databases.

Daniela Cecconi1, Claudia Di Carlo1, Jessica Brandi2.   

Abstract

Secreted proteins play important roles in several biological processes such as growth, proliferation differentiation, cell-cell communication, migration, and apoptosis; moreover, these extracellular molecules mediate homeostasis by influencing the cross-talking within the surrounding tissues. Currently, the research area of cell secretome has become of great interest since the profiling of secreted proteins could be essential for the biomarker discovery and for the identification of new therapeutic strategies. Several bioinformatic platforms have been implemented for the in silico characterization of secreted proteins: this chapter describes a typical workflow for the analysis of proteins secreted by cultured cells through bioinformatic approaches. Central issue is related to discrimination between proteins secreted by classical and non-classical pathways. Therefore, specific prediction tools for the classification of candidate secreted proteins are here presented.

Entities:  

Keywords:  Bioinformatic tools; Classical and non-classical secretion; Secretome

Mesh:

Substances:

Year:  2021        PMID: 34236664     DOI: 10.1007/978-1-0716-1641-3_13

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  26 in total

1.  A combined transmembrane topology and signal peptide prediction method.

Authors:  Lukas Käll; Anders Krogh; Erik L L Sonnhammer
Journal:  J Mol Biol       Date:  2004-05-14       Impact factor: 5.469

2.  SPOCTOPUS: a combined predictor of signal peptides and membrane protein topology.

Authors:  Håkan Viklund; Andreas Bernsel; Marcin Skwark; Arne Elofsson
Journal:  Bioinformatics       Date:  2008-10-22       Impact factor: 6.937

3.  High-performance signal peptide prediction based on sequence alignment techniques.

Authors:  Karl Frank; Manfred J Sippl
Journal:  Bioinformatics       Date:  2008-08-12       Impact factor: 6.937

Review 4.  Proteomic approaches to decipher cancer cell secretome.

Authors:  Jessica Brandi; Marcello Manfredi; Giulia Speziali; Fabio Gosetti; Emilio Marengo; Daniela Cecconi
Journal:  Semin Cell Dev Biol       Date:  2017-07-03       Impact factor: 7.727

Review 5.  Bioinformatics tools for secretome analysis.

Authors:  Dario Caccia; Matteo Dugo; Maurizio Callari; Italia Bongarzone
Journal:  Biochim Biophys Acta       Date:  2013-02-05

6.  Analysis of secreted proteins.

Authors:  Valeria Severino; Annarita Farina; Angela Chambery
Journal:  Methods Mol Biol       Date:  2013

7.  SignalP 5.0 improves signal peptide predictions using deep neural networks.

Authors:  José Juan Almagro Armenteros; Konstantinos D Tsirigos; Casper Kaae Sønderby; Thomas Nordahl Petersen; Ole Winther; Søren Brunak; Gunnar von Heijne; Henrik Nielsen
Journal:  Nat Biotechnol       Date:  2019-02-18       Impact factor: 54.908

Review 8.  Signal peptide-dependent protein transport in Bacillus subtilis: a genome-based survey of the secretome.

Authors:  H Tjalsma; A Bolhuis; J D Jongbloed; S Bron; J M van Dijl
Journal:  Microbiol Mol Biol Rev       Date:  2000-09       Impact factor: 11.056

9.  Transmembrane topology and signal peptide prediction using dynamic bayesian networks.

Authors:  Sheila M Reynolds; Lukas Käll; Michael E Riffle; Jeff A Bilmes; William Stafford Noble
Journal:  PLoS Comput Biol       Date:  2008-11-07       Impact factor: 4.475

10.  Computational comparative study of tuberculosis proteomes using a model learned from signal peptide structures.

Authors:  Jhih-Siang Lai; Cheng-Wei Cheng; Ting-Yi Sung; Wen-Lian Hsu
Journal:  PLoS One       Date:  2012-04-09       Impact factor: 3.240

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  1 in total

Review 1.  Factors Affecting the Expression of Recombinant Protein and Improvement Strategies in Chinese Hamster Ovary Cells.

Authors:  Zheng-Mei Li; Zhen-Lin Fan; Xiao-Yin Wang; Tian-Yun Wang
Journal:  Front Bioeng Biotechnol       Date:  2022-07-04
  1 in total

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