Literature DB >> 28667600

Protein Sorting Prediction.

Henrik Nielsen1.   

Abstract

Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global-property-based and homology-based prediction. In this chapter, the strengths and drawbacks of each of these approaches is described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.

Keywords:  Machine learning; Prediction; Protein sorting; Secretion; Subcellular location; Transmembrane proteins

Mesh:

Substances:

Year:  2017        PMID: 28667600     DOI: 10.1007/978-1-4939-7033-9_2

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


  3 in total

1.  GP4: an integrated Gram-Positive Protein Prediction Pipeline for subcellular localization mimicking bacterial sorting.

Authors:  Stefano Grasso; Tjeerd van Rij; Jan Maarten van Dijl
Journal:  Brief Bioinform       Date:  2021-07-20       Impact factor: 11.622

2.  Genome wide identification and experimental validation of Pseudomonas aeruginosa Tat substrates.

Authors:  Maxime Rémi Gimenez; Govind Chandra; Perrine Van Overvelt; Romé Voulhoux; Sophie Bleves; Bérengère Ize
Journal:  Sci Rep       Date:  2018-08-09       Impact factor: 4.379

Review 3.  Tools for the Recognition of Sorting Signals and the Prediction of Subcellular Localization of Proteins From Their Amino Acid Sequences.

Authors:  Kenichiro Imai; Kenta Nakai
Journal:  Front Genet       Date:  2020-11-25       Impact factor: 4.599

  3 in total

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