@article{2fcc25c377ff482881c1cb2fc74fe4cb,
title = "SignalP 6.0 predicts all five types of signal peptides using protein language models",
abstract = "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.",
keywords = "Algorithms, Amino Acid Sequence, Language, Protein Sorting Signals/genetics, Proteins",
author = "Felix Teufel and {Almagro Armenteros}, {Jos{\'e} Juan} and Johansen, {Alexander Rosenberg} and G{\'i}slason, {Magn{\'u}s Halld{\'o}r} and Pihl, {Silas Irby} and Tsirigos, {Konstantinos D} and Ole Winther and S{\o}ren Brunak and {von Heijne}, Gunnar and Henrik Nielsen",
note = "{\textcopyright} 2022. The Author(s).",
year = "2022",
month = jul,
doi = "10.1038/s41587-021-01156-3",
language = "English",
volume = "40",
pages = "1023--1025",
journal = "Nature Biotechnology",
issn = "1087-0156",
publisher = "Nature Research",
number = "7",
}