Predicting Subcellular Localization of Proteins by Bioinformatic Algorithms

Henrik Nielsen

doi:10.1007/82_2015_5006

Protein and Sugar Export and Assembly in Gram-positive Bacteria pp 129-158

Part of the Current Topics in Microbiology and Immunology book series (CT MICROBIOLOGY, volume 404)

Predicting Subcellular Localization of Proteins by Bioinformatic Algorithms

Authors
Authors and affiliations

Henrik NielsenEmail author

Chapter

First Online:: 05 January 2016

Abstract

When predicting the subcellular localization of proteins from their amino acid sequences, there are basically three approaches: signal-based, global property-based, and homology-based. Each of these has its advantages and drawbacks, and it is important when comparing methods to know which approach was used. Various statistical and machine learning algorithms are used with all three approaches, and various measures and standards are employed when reporting the performances of the developed methods. This chapter presents a number of available methods for prediction of sorting signals and subcellular localization, but rather than providing a checklist of which predictors to use, it aims to function as a guide for critical assessment of prediction methods.

Abbreviations

ANN: Artificial neural network
BLAST: Basic local alignment search tool
GO: Gene Ontology
HMM: Hidden Markov model
MCC: Matthews correlation coefficient
PWM: Position-weight matrix
SP: Signal peptide
SCL: Subcellular localization
SVM: Support vector machine
TMH: Transmembrane α-helix

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Henrik Nielsen
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1.Department of Systems Biology, Center for Biological Sequence AnalysisTechnical University of DenmarkLyngbyDenmark

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Cite this chapter as:: Nielsen H. (2015) Predicting Subcellular Localization of Proteins by Bioinformatic Algorithms. In: Bagnoli F., Rappuoli R. (eds) Protein and Sugar Export and Assembly in Gram-positive Bacteria. Current Topics in Microbiology and Immunology, vol 404. Springer, Cham

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Predicting Subcellular Localization of Proteins by Bioinformatic Algorithms

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