4.5 Article

iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset

期刊

ANALYTICAL BIOCHEMISTRY
卷 497, 期 -, 页码 48-56

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ab.2015.12.009

关键词

Lysine succinylation; Sequence-coupling model; PseAAC; Optimize training dataset; Target cross-validation

资金

  1. National Nature Science Foundation of China [61261027, 31260273, 31560316]
  2. Natural Science Foundation of Jiangxi Province [20122BAB211033, 20122BAB201044, 20132BAB201053]
  3. Scientific Research plan of the Department of Education of Jiangxi Province [GJJ14640]
  4. National Nature Science Foundation of China [61261027, 31260273, 31560316]
  5. Natural Science Foundation of Jiangxi Province [20122BAB211033, 20122BAB201044, 20132BAB201053]
  6. Scientific Research plan of the Department of Education of Jiangxi Province [GJJ14640]

向作者/读者索取更多资源

Succinylation is a posttranslational modification (PTM) where a succinyl group is added to a Lys (K) residue of a protein molecule. Lysine succinylation plays an important role in orchestrating various biological processes, but it is also associated with some diseases. Therefore, we are challenged by the following problem from both basic research and drug development: given an uncharacterized protein sequence containing many Lys residues, which one of them can be succinylated, and which one cannot? With the avalanche of protein sequences generated in the postgenomic age, the answer to the problem has become even more urgent. Fortunately, the statistical significance experimental data for succinylated sites in proteins have become available very recently, an indispensable prerequisite for developing a computational method to address this problem. By incorporating the sequence-coupling effects into the general pseudo amino acid composition and using KNNC (K-nearest neighbors cleaning) treatment and IHTS (inserting hypothetical training samples) treatment to optimize the training dataset, a predictor called iSuc-PseOpt has been developed. Rigorous cross-validations indicated that it remarkably outperformed the existing method. A user-friendly web-server for iSuc-PseOpt has been established at http://www.jci-bioinfo.cnfiSuc-PseOpt, where users can easily get their desired results without needing to go through the complicated mathematical equations involved. (C) 2015 Elsevier Inc. All rights reserved.

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