Journal article
SucStruct: Prediction of succinylated lysine residues by using structural properties of amino acids
Analytical biochemistry, Vol.527, pp.24-32
06/15/2017
DOI: 10.1016/j.ab.2017.03.021
PMID: 28363440
Abstract
Post-Translational Modification (PTM) is a biological reaction which contributes to diversify the proteome. Despite many modifications with important roles in cellular activity, lysine succinylation has recently emerged as an important PTM mark. It alters the chemical structure of lysines, leading to remarkable changes in the structure and function of proteins. In contrast to the huge amount of proteins being sequenced in the post-genome era, the experimental detection of succinylated residues remains expensive, inefficient and time-consuming. Therefore, the development of computational tools for accurately predicting succinylated lysines is an urgent necessity. To date, several approaches have been proposed but their sensitivity has been reportedly poor. In this paper, we propose an approach that utilizes structural features of amino acids to improve lysine succinylation prediction. Succinylated and non-succinylated lysines were first retrieved from 670 proteins and characteristics such as accessible surface area, backbone torsion angles and local structure conformations were incorporated. We used the k-nearest neighbors cleaning treatment for dealing with class imbalance and designed a pruned decision tree for classification. Our predictor, referred to as SucStruct (Succinylation using Structural features), proved to significantly improve performance when compared to previous predictors, with sensitivity, accuracy and Mathew's correlation coefficient equal to 0.7334–0.7946, 0.7444–0.7608 and 0.4884–0.5240, respectively.
Details
- Title: Subtitle
- SucStruct: Prediction of succinylated lysine residues by using structural properties of amino acids
- Creators
- Yosvany López - Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, JapanAbdollah Dehzangi - Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa, USASunil Pranit Lal - School of Engineering & Advanced Technology, Massey University, New ZealandGhazaleh Taherzadeh - School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, Queensland 4215, AustraliaJacob Michaelson - Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa, USAAbdul Sattar - School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, Queensland 4215, AustraliaTatsuhiko Tsunoda - Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, JapanAlok Sharma - Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
- Resource Type
- Journal article
- Publication Details
- Analytical biochemistry, Vol.527, pp.24-32
- Publisher
- Elsevier Inc
- DOI
- 10.1016/j.ab.2017.03.021
- PMID
- 28363440
- ISSN
- 0003-2697
- eISSN
- 1096-0309
- Grant note
- DOI: 10.13039/501100001691, name: Japan Society for the Promotion of Science, award: 15F15385
- Language
- English
- Date published
- 06/15/2017
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Communication Sciences and Disorders; Psychiatry; Iowa Neuroscience Institute
- Record Identifier
- 9984070446002771
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