title: Improved Species-Specific Lysine Acetylation Site Prediction Based on a Large Variety of Features Set.
abstract: Lysine acetylation is a major post-translational modification. It plays a vital role in numerous essential biological processes, such as gene expression and metabolism, and is related to some human diseases. To fully understand the regulatory mechanism of acetylation, identification of acetylation sites is first and most important. However, experimental identification of protein acetylation sites is often time consuming and expensive. Therefore, the alternative computational methods are necessary. Here, we developed a novel tool, KA-predictor, to predict species-specific lysine acetylation sites based on support vector machine (SVM) classifier. We incorporated different types of features and employed an efficient feature selection on each type to form the final optimal feature set for model learning. And our predictor was highly competitive for the majority of species when compared with other methods. Feature contribution analysis indicated that HSE features, which were firstly introduced for lysine acetylation prediction, significantly improved the predictive performance. Particularly, we constructed a high-accurate structure dataset of H.sapiens from PDB to analyze the structural properties around lysine acetylation sites. Our datasets and a user-friendly local tool of KA-predictor can be freely available at http://sourceforge.net/p/ka-predictor.
authors.affiliation: School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China, 300071.
authors.forename: Qiqige
authors.initials: Q
authors.lastname: Wuyun
authors.affiliation: School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China, 300071.
authors.forename: Wei
authors.initials: W
authors.lastname: Zheng
authors.affiliation: Department of Mathematics, School of Science, Hebei University of Engineering, Handan, China, 056038.
authors.forename: Yanping
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China, 300071.|State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, China, 300071.
authors.forename: Jishou
authors.initials: J
authors.lastname: Ruan
authors.affiliation: School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China, 300071.
authors.forename: Gang
authors.initials: G
authors.lastname: Hu
mesh_terms: D000107:Acetylation; D000465:Algorithms; D000818:Animals; D066264:Datasets as Topic; D006801:Humans; D008239:Lysine / Q000737:chemistry / Q000378:metabolism*; D051379:Mice; D008954:Models, Biological; D008958:Models, Molecular; D056510:Position-Specific Scoring Matrices; D011487:Protein Conformation; D011499:Protein Processing, Post-Translational; D011506:Proteins / Q000737:chemistry / Q000378:metabolism; D015203:Reproducibility of Results; D050796:STAT3 Transcription Factor / Q000378:metabolism; D013045:Species Specificity; D013329:Structure-Activity Relationship; D060388:Support Vector Machine
chemical_list: D011506:Proteins; D050796:STAT3 Transcription Factor; D008239:Lysine