SignalP 6.0 predicts all five types of signal peptides using protein language models
Felix Teufel, José Juan Almagro Armenteros, Alexander Rosenberg Johansen, Magnús Halldór Gíslason, Silas Irby Pihl, Konstantinos D. Tsirigos, Ole Winther, Søren Brunak, Gunnar von Heijne, Henrik Nielsen
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.
Topics & Concepts
Signal peptideSIGNAL (programming language)Computational biologyChemistryCell biologyBiochemistryBiologyPeptide sequenceComputer scienceGeneProgramming languageMachine Learning in BioinformaticsGenomics and Phylogenetic StudiesRNA and protein synthesis mechanisms