SignalP 6.0 predicts all five types of signal peptides using protein language models

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Signal peptides (SPs) are short amino acid sequences that control protein secretion and translocation in all living organisms. SPs can be predicted from sequence data, but existing algorithms are unable to detect all known types of SPs. We introduce SignalP 6.0, a machine learning model that detects all five SP types and is applicable to metagenomic data.

OriginalsprogEngelsk
TidsskriftNature Biotechnology
Vol/bind40
Sider (fra-til)1023-1025
ISSN1087-0156
DOI
StatusUdgivet - 2022

Bibliografisk note

Funding Information:
J.J.A.A. and S.B. were supported by the Novo Nordisk Foundation (grants NNF14CC0001 and NNF17OC0027594). O.W. was funded in part by the Novo Nordisk Foundation through the Center for Basic Machine Learning Research in Life Science (grant NNF20OC0062606). G.v.H. was supported by the Knut and Alice Wallenberg Foundation (grant 2017.0323), the Novo Nordisk Foundation (grant NNF18OC0032828) and the Swedish Research Council (grant 621-2014-3713).

Publisher Copyright:
© 2022, The Author(s).

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