Position-specific analysis and prediction for protein lysine

  acetylation based on multiple features

  Input the sequences in Fasta format:




In the prediction model PSKAcePred, the sequences fragments are firstly extracted in window size -10 to +10 (acetylation lysine centered in position of 0). Then, 13 optimization positions are chosen by using position-specific method. Users can submit protein sequence(s) in FASTA format to this web interface. The system efficiently returns the prediction results, including protein name, the position of site, flanking amino acid sequences and SVM probability. In the output of flanking amino acid sequences, the amino acids in green are those selected by using information gain and the red amino acids (K) are predicted as acetylation sites.

There is no upper bound for the protein sequence length, input protein sequences have to: (i) contain minimum 21 amino acids; (ii) contain only characters that present amino acids.

Maximum 20 protein sequences is recommend to be submitted in the textbox. Too many sequences for prediction can cause the system to crash. To carry out large-scale predictions, the researchers can download the Matlab codes about the PSKAcePred below.


The training data can be downloaded at here. The independent data can be downloaded at here. The Matlab codes can be downloaded at here.

Suo S-B, Qiu J-D, Shi S-P, Sun X-Y, Huang S-Y, et al. (2012) Position-Specific Analysis and Prediction for Protein Lysine Acetylation Based on Multiple Features. PLoS ONE 7(11): e49108.