title: SPRED: A machine learning approach for the identification of classical and non-classical secretory proteins in mammalian genomes.
abstract: Eukaryotic protein secretion generally occurs via the classical secretory pathway that traverses the ER and Golgi apparatus. Secreted proteins usually contain a signal sequence with all the essential information required to target them for secretion. However, some proteins like fibroblast growth factors (FGF-1, FGF-2), interleukins (IL-1 alpha, IL-1 beta), galectins and thioredoxin are exported by an alternative pathway. This is known as leaderless or non-classical secretion and works without a signal sequence. Most computational methods for the identification of secretory proteins use the signal peptide as indicator and are therefore not able to identify substrates of non-classical secretion. In this work, we report a random forest method, SPRED, to identify secretory proteins from protein sequences irrespective of N-terminal signal peptides, thus allowing also correct classification of non-classical secretory proteins. Training was performed on a dataset containing 600 extracellular proteins and 600 cytoplasmic and/or nuclear proteins. The algorithm was tested on 180 extracellular proteins and 1380 cytoplasmic and/or nuclear proteins. We obtained 85.92% accuracy from training and 82.18% accuracy from testing. Since SPRED does not use N-terminal signals, it can detect non-classical secreted proteins by filtering those secreted proteins with an N-terminal signal by using SignalP. SPRED predicted 15 out of 19 experimentally verified non-classical secretory proteins. By scanning the entire human proteome we identified 566 protein sequences potentially undergoing non-classical secretion. The dataset and standalone version of the SPRED software is available at http://www.inb.uni-luebeck.de/tools-demos/spred/spred.
authors.affiliation: Institute for Neuro- and Bioinformatics, University of Lübeck, 23538 Lübeck, Germany.
authors.forename: Krishna Kumar
authors.initials: KK
authors.lastname: Kandaswamy
authors.forename: Ganesan
authors.initials: G
authors.lastname: Pugalenthi
authors.forename: Enno
authors.initials: E
authors.lastname: Hartmann
authors.forename: Kai-Uwe
authors.initials: KU
authors.lastname: Kalies
authors.forename: Steffen
authors.initials: S
authors.lastname: Möller
authors.forename: P N
authors.initials: PN
authors.lastname: Suganthan
authors.forename: Thomas
authors.initials: T
authors.lastname: Martinetz
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D015894:Genome, Human; D006801:Humans; D011506:Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*; D020543:Proteome; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins; D020543:Proteome