Abstract
Background: Proteins through post translational modifications perform their biological process and cellular functions. Post-translational modifications play important roles in various biological process and cell functions. Identifying the PTMs sites in proteins is very significant to basic research and drug design. Experimental technique to identify post translational modifications is laborious. Computational identification of post translational modifications is a complementary way for its convenience.
Conclusion: This review gives the processing to predict post-translational modification sites in proteins including feature construction, algorithms, evaluation measurement, and online webserver. There are two types of post translational modification in proteins. In the prediction of single PTM sites, we transformed it into binary classification learning. While in the prediction of crosstalk PTM sites, we transformed it into multi-label learning. This review summarized the steps on the two issues.
Keywords: Post translational modifications, machine learning, web-server, feature construction, algorithm, binary classification learning.
Graphical Abstract
Related Journals
Current Protein & Peptide Science
Related Books
Frontiers in Protein and Peptide Sciences
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