2018
1.
Uddin, Md. Raihan; Sharma, Alok; Farid, Dewan Md; Rahman, Md. Mahmudur; Dehzangi, Abdollah; Shatabda, Swakkhar
EvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural features Journal Article
In: Journal of Theoretical Biology, vol. 443, pp. 138-146, 2018, ISSN: 0022-5193.
Abstract | Links | BibTeX | Tags: Classification, Evolutionary-based features, Feature selection, Proteins subcellular localization, Structural-based features, Support vector machine
@article{UDDIN2018138,
title = {EvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural features},
author = {Md. Raihan Uddin and Alok Sharma and Dewan Md Farid and Md. Mahmudur Rahman and Abdollah Dehzangi and Swakkhar Shatabda},
url = {https://www.sciencedirect.com/science/article/pii/S0022519318300559},
doi = {https://doi.org/10.1016/j.jtbi.2018.02.002},
issn = {0022-5193},
year = {2018},
date = {2018-01-01},
journal = {Journal of Theoretical Biology},
volume = {443},
pages = {138-146},
abstract = {Determining subcellular localization of proteins is considered as an important step towards understanding their functions. Previous studies have mainly focused solely on Gene Ontology (GO) as the main feature to tackle this problem. However, it was shown that features extracted based on GO is hard to be used for new proteins with unknown GO. At the same time, evolutionary information extracted from Position Specific Scoring Matrix (PSSM) have been shown as another effective features to tackle this problem. Despite tremendous advancement using these sources for feature extraction, this problem still remains unsolved. In this study we propose EvoStruct-Sub which employs predicted structural information in conjunction with evolutionary information extracted directly from the protein sequence to tackle this problem. To do this we use several different feature extraction method that have been shown promising in subcellular localization as well as similar studies to extract effective local and global discriminatory information. We then use Support Vector Machine (SVM) as our classification technique to build EvoStruct-Sub. As a result, we are able to enhance Gram-positive subcellular localization prediction accuracies by up to 5.6% better than previous studies including the studies that used GO for feature extraction.},
keywords = {Classification, Evolutionary-based features, Feature selection, Proteins subcellular localization, Structural-based features, Support vector machine},
pubstate = {published},
tppubtype = {article}
}
Determining subcellular localization of proteins is considered as an important step towards understanding their functions. Previous studies have mainly focused solely on Gene Ontology (GO) as the main feature to tackle this problem. However, it was shown that features extracted based on GO is hard to be used for new proteins with unknown GO. At the same time, evolutionary information extracted from Position Specific Scoring Matrix (PSSM) have been shown as another effective features to tackle this problem. Despite tremendous advancement using these sources for feature extraction, this problem still remains unsolved. In this study we propose EvoStruct-Sub which employs predicted structural information in conjunction with evolutionary information extracted directly from the protein sequence to tackle this problem. To do this we use several different feature extraction method that have been shown promising in subcellular localization as well as similar studies to extract effective local and global discriminatory information. We then use Support Vector Machine (SVM) as our classification technique to build EvoStruct-Sub. As a result, we are able to enhance Gram-positive subcellular localization prediction accuracies by up to 5.6% better than previous studies including the studies that used GO for feature extraction.