López, Yosvany and Dehzangi, Abdollah and Lal, Sunil Pranit and Taherzadeh, Ghazaleh and Michaelson, Jacob and Sattar, Abdul and Tsunoda, Tatsuhiko and Sharma, Alokanand (2017) SucStruct: Prediction of succinylated lysine residues by using structural properties of amino acids. Analytical Biochemistry, 527 . 24 - 32. ISSN 0003-2697
Full text not available from this repository. (Request a copy)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
| Item Type: | Journal Article |
|---|---|
| Uncontrolled Keywords: | Lysine succinylation, Structural features, Protein sequences, Amino acids, Prediction |
| Subjects: | Q Science > Q Science (General) |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics |
| Depositing User: | Fulori Nainoca - Waqairagata |
| Date Deposited: | 07 Jun 2018 23:45 |
| Last Modified: | 07 Jun 2018 23:46 |
| URI: | https://repository.usp.ac.fj/id/eprint/10825 |
Actions (login required)
 |
View Item |