Journal article
A Brief History of Protein Sorting Prediction
Department of Health Technology, Technical University of Denmark1
Bioinformatics, Department of Health Technology, Technical University of Denmark2
Bayesian modeling, Machine learning, Molecular Evolution, and Metagenomics, Bioinformatics, Department of Health Technology, Technical University of Denmark3
Integrative Systems Biology, Bioinformatics, Department of Health Technology, Technical University of Denmark4
Stockholm University5
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.
Language: | English |
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Publisher: | Springer US |
Year: | 2019 |
Pages: | 200-216 |
ISSN: | 18758355 , 15723887 , 15734943 and 02778033 |
Types: | Journal article |
DOI: | 10.1007/s10930-019-09838-3 |
ORCIDs: | Nielsen, Henrik , Tsirigos, Konstantinos D. and 0000-0003-0316-5866 |