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A Brief History of Protein Sorting Prediction
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Number of Authors: 42019 (English)In: The Protein Journal, ISSN 1572-3887, E-ISSN 1875-8355, Vol. 38, no 3, p. 200-216Article, review/survey (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2019. Vol. 38, no 3, p. 200-216
Keywords [en]
Signal peptides, Protein sorting, Bioinformatics, Prediction
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:su:diva-171134DOI: 10.1007/s10930-019-09838-3ISI: 000472539900002PubMedID: 31119599OAI: oai:DiVA.org:su-171134DiVA, id: diva2:1343569
Available from: 2019-08-17 Created: 2019-08-17 Last updated: 2019-08-17Bibliographically approved

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