Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites
Lysine succinylation is a form of posttranslational modification of the proteins that play an essential functional role in every aspect of cell metabolism in both prokaryotes and eukaryotes. Aside from experimental identification of succinylation sites, there has been an intense effort geared toward...
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doaj-9ed64874cf6f49cd814b63c62254381d2020-11-25T01:57:12ZengMDPI AGCells2073-44092019-01-01829510.3390/cells8020095cells8020095Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation SitesMd. Mehedi Hasan0Mst. Shamima Khatun1Hiroyuki Kurata2Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680–4 Kawazu, Iizuka, Fukuoka 820-8502, JapanDepartment of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680–4 Kawazu, Iizuka, Fukuoka 820-8502, JapanDepartment of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680–4 Kawazu, Iizuka, Fukuoka 820-8502, JapanLysine succinylation is a form of posttranslational modification of the proteins that play an essential functional role in every aspect of cell metabolism in both prokaryotes and eukaryotes. Aside from experimental identification of succinylation sites, there has been an intense effort geared towards the development of sequence-based prediction through machine learning, due to its promising and essential properties of being highly accurate, robust and cost-effective. In spite of these advantages, there are several problems that are in need of attention in the design and development of succinylation site predictors. Notwithstanding of many studies on the employment of machine learning approaches, few articles have examined this bioinformatics field in a systematic manner. Thus, we review the advancements regarding the current state-of-the-art prediction models, datasets, and online resources and illustrate the challenges and limitations to present a useful guideline for developing powerful succinylation site prediction tools.https://www.mdpi.com/2073-4409/8/2/95lysine succinylationsequence analysismachine learningtool developmentfeature descriptor |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Md. Mehedi Hasan Mst. Shamima Khatun Hiroyuki Kurata |
spellingShingle |
Md. Mehedi Hasan Mst. Shamima Khatun Hiroyuki Kurata Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites Cells lysine succinylation sequence analysis machine learning tool development feature descriptor |
author_facet |
Md. Mehedi Hasan Mst. Shamima Khatun Hiroyuki Kurata |
author_sort |
Md. Mehedi Hasan |
title |
Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites |
title_short |
Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites |
title_full |
Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites |
title_fullStr |
Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites |
title_full_unstemmed |
Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites |
title_sort |
large-scale assessment of bioinformatics tools for lysine succinylation sites |
publisher |
MDPI AG |
series |
Cells |
issn |
2073-4409 |
publishDate |
2019-01-01 |
description |
Lysine succinylation is a form of posttranslational modification of the proteins that play an essential functional role in every aspect of cell metabolism in both prokaryotes and eukaryotes. Aside from experimental identification of succinylation sites, there has been an intense effort geared towards the development of sequence-based prediction through machine learning, due to its promising and essential properties of being highly accurate, robust and cost-effective. In spite of these advantages, there are several problems that are in need of attention in the design and development of succinylation site predictors. Notwithstanding of many studies on the employment of machine learning approaches, few articles have examined this bioinformatics field in a systematic manner. Thus, we review the advancements regarding the current state-of-the-art prediction models, datasets, and online resources and illustrate the challenges and limitations to present a useful guideline for developing powerful succinylation site prediction tools. |
topic |
lysine succinylation sequence analysis machine learning tool development feature descriptor |
url |
https://www.mdpi.com/2073-4409/8/2/95 |
work_keys_str_mv |
AT mdmehedihasan largescaleassessmentofbioinformaticstoolsforlysinesuccinylationsites AT mstshamimakhatun largescaleassessmentofbioinformaticstoolsforlysinesuccinylationsites AT hiroyukikurata largescaleassessmentofbioinformaticstoolsforlysinesuccinylationsites |
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