※ GPS-SUMO 2.0 Online Service (Penalized Logistic Regression) 

For prediction based on transformer neural network, please click here.

For comprehensive prediction with all models and annotations of secondary structure and surface accessibility, please click here.

For species-specific prediction, please click here.

For prediction according to protein identiter, please click here.

     Please enter sequence(s) in FASTA format

     Or, upload file (<70K) 

Sumoylation: SUMO interaction:
Motif Filter

 All the spaces, line breaks will be automatically removed. You could input one primary sequence or multiple proteins'  sequences in FASTA format. And please don't input any special characters.

After GPS-SUMO 2.0 predictor model was well-trained, we performed an evaluation on this model. From the evaluation, three thresholds with High, Medium and Low stringency were chosen for GPS-SUMO 2.0. The performance under these three thresholds was presented as follow:

The performance of GPS-SUMO 2.0 in different thresholds
Threshold Sumoylation SUMO interaction
Ac Sn Sp MCC Pr Ac Sn Sp MCC Pr
High 76.16% 57.24% 95.08% 0.5652 92.08% 85.83% 67.50% 95.00% 0.6731 87.10%
Medium 78.75% 67.34% 90.16% 0.5906 87.25% 92.50% 97.50% 90.00% 0.8450 82.98%
Low 80.38% 75.76% 85.00% 0.6102 80.38% 90.00% 98.75% 85.62% 0.8046 77.45%