Computational prediction of species-specific malonylation sites
  Predict Malonylation Site Last update: Sept. 2017  
Lysine malonylation (Kmal) is a novel and covalent post-translational modifications (PTMs) which presents in organisms from eukaryotic to prokaryotic cells. Malonylation was originally detected by mass spectrometry and protein sequence-database searching in 2011. Following study showed its possile roles in regulating chromatin structure and function, and suggested that malonylation was associated with pathophysiological processes and disease. Cells with increased Kmal displayed impaired mitochondrial function and fatty acid oxidation, which supported a potential role of protein lysine malonylation in type 2 diabetes with possible implications for its therapy in the future. Indeed, validating sites of malonylation in proteomic is the first and important step to understand how malonyaltion affects the activity of its targets. Comparing to the extensive laboratory work and considerable expense, MaloPred, the novel malonylation site online prediction for H.sapiens, M.musculus and E.coli, can provide more instructive guidance for further experimental investigation of protein malonyaltion. We suggest users pay more attention to the lysine sites with stringency setting higher than 0.7.
Input comma-separated accessions.
 Sequence: Input protein sequence(s) in FASTA format.
Modification species:
Stringency setting:
Result format
    In the predicted result table, the first column displays the name of the malonylated protein. The second column indicates the position of potential malonylation site. The third column indicates the lysine site and its surrounding amino acids in orginal sequence (The window length of protein sequence is 25, if sequence is not enough, we add vector ‘O’ to represent it.). The fourth column indicates the SVM scores of malonylation sites.
Predicted result

Lina Wang, Shaoping Shi, Haodong Xu, Pingping Wen, Jianding Qiu*. Computational prediction of species-specific malonylation sites via enhanced characteristic strategy, Bioinformatics, (2017) 33 (10):1457-1463.
Copyright © 2016 Jian Ding Qiu's Lab. NanChang University.