SUMOsp

SUMOsp predicts protein sumoylation sites to systematically identify putative SUMO (small ubiquitin-related modifier)-modified residues across organisms.

Key Features:

• Prediction of sumoylation sites: Predicts putative sumoylation sites in protein sequences based on computational analysis.
• Manually curated dataset: Uses a manually curated dataset compiled for model training and evaluation.
• Integration of GPS and MotifX: Integrates results from GPS and MotifX (methods originally developed for phosphorylation site prediction) to enhance site identification.
• Motif-based genome-wide scanning: Employs genome-wide analysis based on the putative motif psi-K-X-E for identifying candidate sites.
• Evolutionary conservation analysis: Incorporates evolutionary conservation principles to support prediction specificity.
• Benchmarking and validation: Validates predictions using a large test set and benchmarks performance against SUMOplot.
• Cross-organism prediction: Applies the prediction approach to individual organisms for proteome-level analyses.

Scientific Applications:

• Proteome-wide sumoylation mapping: Identification of candidate SUMO-modified residues across proteomes using motif and conservation signals.
• Substrate prioritization: Prioritizes putative sumoylation substrates for experimental validation.
• Comparative analysis: Enables comparative and evolutionary studies of sumoylation sites across organisms.
• Experimental design support: Provides computational predictions to guide targeted experimental verification of sumoylation.

Methodology:

Integrates a manually curated dataset and combines GPS and MotifX outputs, performs motif scanning for psi-K-X-E, incorporates evolutionary conservation analysis, and validates predictions using a large test set with benchmarking against SUMOplot.