A Brief History of Protein Sorting Prediction

Henrik Nielsen*, Konstantinos D. Tsirigos, Søren Brunak, Gunnar von Heijne

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Ever since the signal hypothesis was proposed in 1971, the exact nature of signal peptides has been a focus point of research. The prediction of signal peptides and protein subcellular location from amino acid sequences has been an important problem in bioinformatics since the dawn of this research field, involving many statistical and machine learning technologies. In this review, we provide a historical account of how position-weight matrices, artificial neural networks, hidden Markov models, support vector machines and, lately, deep learning techniques have been used in the attempts to predict where proteins go. Because the secretory pathway was the first one to be studied both experimentally and through bioinformatics, our main focus is on the historical development of prediction methods for signal peptides that target proteins for secretion; prediction methods to identify targeting signals for other cellular compartments are treated in less detail.
Original languageEnglish
JournalProtein Journal
Volume38
Issue number3
Pages (from-to)200-216
ISSN1572-3887
DOIs
Publication statusPublished - 2019

Keywords

  • Signal peptides
  • Protein sorting
  • Bioinformatics
  • Prediction

Cite this

Nielsen, Henrik ; Tsirigos, Konstantinos D. ; Brunak, Søren ; von Heijne, Gunnar. / A Brief History of Protein Sorting Prediction. In: Protein Journal. 2019 ; Vol. 38, No. 3. pp. 200-216.
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A Brief History of Protein Sorting Prediction. / Nielsen, Henrik; Tsirigos, Konstantinos D.; Brunak, Søren; von Heijne, Gunnar.

In: Protein Journal, Vol. 38, No. 3, 2019, p. 200-216.

Research output: Contribution to journalJournal articleResearchpeer-review

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AU - Nielsen, Henrik

AU - Tsirigos, Konstantinos D.

AU - Brunak, Søren

AU - von Heijne, Gunnar

PY - 2019

Y1 - 2019

N2 - Ever since the signal hypothesis was proposed in 1971, the exact nature of signal peptides has been a focus point of research. The prediction of signal peptides and protein subcellular location from amino acid sequences has been an important problem in bioinformatics since the dawn of this research field, involving many statistical and machine learning technologies. In this review, we provide a historical account of how position-weight matrices, artificial neural networks, hidden Markov models, support vector machines and, lately, deep learning techniques have been used in the attempts to predict where proteins go. Because the secretory pathway was the first one to be studied both experimentally and through bioinformatics, our main focus is on the historical development of prediction methods for signal peptides that target proteins for secretion; prediction methods to identify targeting signals for other cellular compartments are treated in less detail.

AB - Ever since the signal hypothesis was proposed in 1971, the exact nature of signal peptides has been a focus point of research. The prediction of signal peptides and protein subcellular location from amino acid sequences has been an important problem in bioinformatics since the dawn of this research field, involving many statistical and machine learning technologies. In this review, we provide a historical account of how position-weight matrices, artificial neural networks, hidden Markov models, support vector machines and, lately, deep learning techniques have been used in the attempts to predict where proteins go. Because the secretory pathway was the first one to be studied both experimentally and through bioinformatics, our main focus is on the historical development of prediction methods for signal peptides that target proteins for secretion; prediction methods to identify targeting signals for other cellular compartments are treated in less detail.

KW - Signal peptides

KW - Protein sorting

KW - Bioinformatics

KW - Prediction

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