Prediction of post-translational modification sites using multiple kernel support vector machine

Protein post-translational modification (PTM) is an important mechanism that is involved in the regulation of protein function. Considering the high-cost and labor-intensive of experimental identification, many computational prediction methods are currently available for the prediction of PTM sites...

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Bibliographic Details
Published in:PeerJ (San Francisco, CA) CA), 2017-04, Vol.5, p.e3261-e3261, Article e3261
Main Authors: Wang, BingHua, Wang, Minghui, Li, Ao
Format: Article
Language:English
Subjects:
Acetylation
Amino acid sequence
Bioinformatics
Biology
Computational Biology
Computer applications
Conserved sequence
Datasets
Decision theory
Gaussian interaction profile kernel
Gene expression
Glycosylation
Kernels
Kinases
Lipids
Methods
Multiple kernels
Nitration
Phosphorylation
Post-translation
Post-translational modification
Proteins
Signal transduction
Translation
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Summary:Protein post-translational modification (PTM) is an important mechanism that is involved in the regulation of protein function. Considering the high-cost and labor-intensive of experimental identification, many computational prediction methods are currently available for the prediction of PTM sites by using protein local sequence information in the context of conserved motif. Here we proposed a novel computational method by using the combination of multiple kernel support vector machines (SVM) for predicting PTM sites including phosphorylation, O-linked glycosylation, acetylation, sulfation and nitration. To largely make use of local sequence information and site-modification relationships, we developed a local sequence kernel and Gaussian interaction profile kernel, respectively. Multiple kernels were further combined to train SVM for efficiently leveraging kernel information to boost predictive performance. We compared the proposed method with existing PTM prediction methods. The experimental results revealed that the proposed method performed comparable or better performance than the existing prediction methods, suggesting the feasibility of the developed kernels and the usefulness of the proposed method in PTM sites prediction.
ISSN:2167-8359
2167-8359
DOI:10.7717/peerj.3261