OutCyte

OutCyte predicts unconventional protein secretion (UPS) from amino acid sequences to identify proteins secreted via non-classical pathways.

Key Features:

• Unconventional secretion prediction: Predicts UPS for proteins lacking canonical N-terminal signal peptides using information derived from amino acid sequences.
• Two-step classification pipeline: First filters out proteins with N-terminal signal peptides, then classifies remaining proteins as UPS or intracellular.
• Sequence-derived physicochemical features: Uses physicochemical properties computed from amino acid sequences as classification features.
• Training on experimental UPS data: Developed using experimentally determined UPS proteins as the basis for prediction models.
• Performance: Provides rapid and accurate classification of proteins as UPS or intracellular.

Scientific Applications:

• Proteome annotation: Assists annotation of experimental proteomics datasets by identifying proteins secreted via unconventional pathways.
• Extracellular proteome characterization: Aids characterization of extracellular proteins that bypass the endoplasmic reticulum–Golgi secretory pathway.
• Protein localization and function studies: Supports studies of protein localization and function in contexts where conventional secretion signals are absent.

Methodology:

Operates in two computational steps: filters proteins with N-terminal signal peptides, then classifies proteins lacking N-terminal signals as UPS or intracellular using physicochemical features derived from amino acid sequences; developed using experimentally determined UPS proteins.