%0 Journal Article %1 orfanoudaki2017maturep %A Orfanoudaki, Georgia %A Markaki, Maria %A Chatzi, Katerina %A Tsamardinos, Ioannis %A Economou, Anastassios %D 2017 %J Nature Scientific Reports %K mxmcausalpath %N 1 %P 3263 %R 10.1038/s41598-017-03557-4 %T MatureP: prediction of secreted proteins with exclusive information from their mature regions %U https://doi.org/10.1038/s41598-017-03557-4 %V 7 %X More than a third of the cellular proteome is non-cytoplasmic. Most secretory proteins use the Sec system for export and are targeted to membranes using signal peptides and mature domains. To specifically analyze bacterial mature domain features, we developed MatureP, a classifier that predicts secretory sequences through features exclusively computed from their mature domains. MatureP was trained using Just Add Data Bio, an automated machine learning tool. Mature domains are predicted efficiently with ~92% success, as measured by the Area Under the Receiver Operating Characteristic Curve (AUC). Predictions were validated using experimental datasets of mutated secretory proteins. The features selected by MatureP reveal prominent differences in amino acid content between secreted and cytoplasmic proteins. Amino-terminal mature domain sequences have enhanced disorder, more hydroxyl and polar residues and less hydrophobics. Cytoplasmic proteins have prominent amino-terminal hydrophobic stretches and charged regions downstream. Presumably, secretory mature domains comprise a distinct protein class. They balance properties that promote the necessary flexibility required for the maintenance of non-folded states during targeting and secretion with the ability of post-secretion folding. These findings provide novel insight in protein trafficking, sorting and folding mechanisms and may benefit protein secretion biotechnology.

@article{orfanoudaki2017maturep, abstract = {More than a third of the cellular proteome is non-cytoplasmic. Most secretory proteins use the Sec system for export and are targeted to membranes using signal peptides and mature domains. To specifically analyze bacterial mature domain features, we developed MatureP, a classifier that predicts secretory sequences through features exclusively computed from their mature domains. MatureP was trained using Just Add Data Bio, an automated machine learning tool. Mature domains are predicted efficiently with ~92% success, as measured by the Area Under the Receiver Operating Characteristic Curve (AUC). Predictions were validated using experimental datasets of mutated secretory proteins. The features selected by MatureP reveal prominent differences in amino acid content between secreted and cytoplasmic proteins. Amino-terminal mature domain sequences have enhanced disorder, more hydroxyl and polar residues and less hydrophobics. Cytoplasmic proteins have prominent amino-terminal hydrophobic stretches and charged regions downstream. Presumably, secretory mature domains comprise a distinct protein class. They balance properties that promote the necessary flexibility required for the maintenance of non-folded states during targeting and secretion with the ability of post-secretion folding. These findings provide novel insight in protein trafficking, sorting and folding mechanisms and may benefit protein secretion biotechnology.}, added-at = {2019-02-01T14:01:30.000+0100}, author = {Orfanoudaki, Georgia and Markaki, Maria and Chatzi, Katerina and Tsamardinos, Ioannis and Economou, Anastassios}, biburl = {https://www.bibsonomy.org/bibtex/2951371b60898141f3bcdfc2141b70336/mensxmachina}, doi = {10.1038/s41598-017-03557-4}, interhash = {45976adff24808a1e1f78e330732b5ff}, intrahash = {951371b60898141f3bcdfc2141b70336}, issn = {20452322}, journal = {Nature Scientific Reports}, keywords = {mxmcausalpath}, month = {June}, number = 1, pages = 3263, refid = {Orfanoudaki2017}, timestamp = {2021-03-10T09:25:44.000+0100}, title = {MatureP: prediction of secreted proteins with exclusive information from their mature regions}, url = {https://doi.org/10.1038/s41598-017-03557-4}, volume = 7, year = 2017 }