General Information
| Description | Identifying multiple lysine PTM sites and their different types |
| Homepage | http://www.jci-bioinfo.cn/iPTM-mLys |
Publications
iPTM-mLys: identifying multiple lysine PTM sites and their different types
Motivation:
Post-translational modification, abbreviated as PTM, refers to the change of the amino acid side chains of a protein after its biosynthesis. Owing to its significance for in-depth understanding various biological processes and developing effective drugs, prediction of PTM sites in proteins have currently become a hot topic in bioinformatics. Although many computational methods were established to identify various single-label PTM types and their occurrence sites in proteins, no method has ever been developed for multi-label PTM types. As one of the most frequently observed PTMs, the K-PTM, namely, the modification occurring at lysine (K), can be usually accommodated with many different types, such as ‘acetylation’, ‘crotonylation’, ‘methylation’ and ‘succinylation’. Now we are facing an interesting challenge: given an uncharacterized protein sequence containing many K residues, which ones can accommodate two or more types of PTM, which ones only one, and which ones none?
Results:
To address this problem, a multi-label predictor called has been developed. It represents the first multi-label PTM predictor ever established. The novel predictor is featured by incorporating the sequence-coupled effects into the general PseAAC, and by fusing an array of basic random forest classifiers into an ensemble system. Rigorous cross-validations via a set of multi-label metrics indicate that the first multi-label PTM predictor is very promising and encouraging.
Availability and Implementation:
For the convenience of most experimental scientists, a user-friendly web-server for has been established at http://www.jci-bioinfo.cn/iPTM-mLys, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved.
Contact:
wqiu@gordonlifescience.org, xxiao@gordonlifescience.org, kcchou@gordonlifescience.org
Supplementary information:
Supplementary data are available at Bioinformatics online.
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