title: Mal-Lys: prediction of lysine malonylation sites in proteins integrated sequence-based features with mRMR feature selection.
abstract: Lysine malonylation is an important post-translational modification (PTM) in proteins, and has been characterized to be associated with diseases. However, identifying malonyllysine sites still remains to be a great challenge due to the labor-intensive and time-consuming experiments. In view of this situation, the establishment of a useful computational method and the development of an efficient predictor are highly desired. In this study, a predictor Mal-Lys which incorporated residue sequence order information, position-specific amino acid propensity and physicochemical properties was proposed. A feature selection method of minimum Redundancy Maximum Relevance (mRMR) was used to select optimal ones from the whole features. With the leave-one-out validation, the value of the area under the curve (AUC) was calculated as 0.8143, whereas 6-, 8- and 10-fold cross-validations had similar AUC values which showed the robustness of the predictor Mal-Lys. The predictor also showed satisfying performance in the experimental data from the UniProt database. Meanwhile, a user-friendly web-server for Mal-Lys is accessible at http://app.aporc.org/Mal-Lys/.
authors.affiliation: Department of Information and Computer Science, University of Science and Technology Beijing, Beijing 100083, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Xu
authors.affiliation: Department of Information and Computer Science, University of Science and Technology Beijing, Beijing 100083, China.
authors.forename: Ya-Xin
authors.initials: YX
authors.lastname: Ding
authors.affiliation: Department of Information and Computer Science, University of Science and Technology Beijing, Beijing 100083, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Ding
authors.affiliation: Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Ling-Yun
authors.initials: LY
authors.lastname: Wu
authors.affiliation: Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Xue
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D002256:Carbonic Anhydrases / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D020407:Internet; D008239:Lysine / Q000378:metabolism*; D008314:Malonates / Q000378:metabolism*; D051379:Mice; D011336:Probability; D011499:Protein Processing, Post-Translational
chemical_list: D008314:Malonates; C030290:malonic acid; D002256:Carbonic Anhydrases; D008239:Lysine