Prediction of lysine post-translational modifications using bioinformatic tools.
Our understanding of the importance of lysine post-translational modifications in mediating protein function has led to a significant improvement in the experimental tools aimed at characterizing their existence. Nevertheless, it remains likely that at present we have only experimentally detected a small fraction of all lysine modification sites across the commonly studied proteomes. As a result, online computational tools aimed at predicting lysine modification sites have the potential to provide valuable insight to researchers developing hypotheses regarding these modifications. This chapter discusses the metrics and procedures used to assess predictive tools and surveys 11 online computational tools aimed at the prediction of the four most widely studied lysine post-translational modifications (acetylation, methylation, SUMOylation and ubiquitination). Analyses using unbiased testing data sets suggest that nine of the 11 lysine post-translational modification tools perform no better than random, or have false-positive rates which make them unusable by the experimental biologist, despite self-reported sensitivity and specificity values to the contrary. The implications of these findings for those using and creating lysine post-translational modification software are discussed.
Department of Physiology and Neurobiology, University of Connecticut, Storrs, CT 06269-3156, U.S.A. daniel.schwartz@uconn.edu
Daniel
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Schwartz
D000818:Animals; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D008239:Lysine / Q000378:metabolism*; D011499:Protein Processing, Post-Translational
D008239:Lysine