An Improved Computational Prediction Model for Lysine Succinylation Sites Mapping on

Samme Amena Tasmia, Fee Faysal Ahmed, Parvez Mosharaf, Mehedi Hasan, Nurul Haque Mollah

Abstract read

In one paragraph

Article in Current genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

Protein post-translational modification by lysine succinylation: Biochemistry, biological implications, and therapeutic opportunities.Genes & diseases · 2023
Review
Prediction of serine phosphorylation sites mapping on Schizosaccharomyces Pombe by fusing three encoding schemes with the random forest classifier.Scientific reports · 2022
Article
Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites.Frontiers in genetics · 2022
Article
PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features.International journal of molecular sciences · 2021
Article

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Samme Amena Tasmia1Bioinformatics Lab., Department of Statistics, Rajshahi University, Rajshahi-6205, Bangladesh; 2Department of Mathematics, Jashore University of Science and Technology, Jashore, Bangladesh; 3Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka, Japan.

Fee Faysal Ahmed1Bioinformatics Lab., Department of Statistics, Rajshahi University, Rajshahi-6205, Bangladesh; 2Department of Mathematics, Jashore University of Science and Technology, Jashore, Bangladesh; 3Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka, Japan.

Parvez Mosharaf1Bioinformatics Lab., Department of Statistics, Rajshahi University, Rajshahi-6205, Bangladesh; 2Department of Mathematics, Jashore University of Science and Technology, Jashore, Bangladesh; 3Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka, Japan.

Mehedi Hasan1Bioinformatics Lab., Department of Statistics, Rajshahi University, Rajshahi-6205, Bangladesh; 2Department of Mathematics, Jashore University of Science and Technology, Jashore, Bangladesh; 3Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka, Japan.

Nurul Haque Mollah1Bioinformatics Lab., Department of Statistics, Rajshahi University, Rajshahi-6205, Bangladesh; 2Department of Mathematics, Jashore University of Science and Technology, Jashore, Bangladesh; 3Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLysine succinylation is one of the reversible protein post-translational modifications (PTMs), which regulate the structure and function of proteins. It plays a significant role in various cellular physiologies including some diseases of human as well as many other organisms. The accurate identification of succinylation site is essential to understand the various biological functions and drug development.

methodsIn this study, we developed an improved method to predict lysine succinylation sites mapping on

resultsThe CV results showed that the proposed predictor achieves the highest scores of sensitivity (SN) as 0.800, specificity (SP) as 0.902, accuracy (ACC) as 0.919, Mathew correlation coefficient (MCC) as 0.766 and partial AUC (pAUC) as 0.163 at a false-positive rate (FPR) = 0.10 and area under the ROC curve (AUC) as 0.958. It achieved the highest performance scores of SN as 0.811, SP as 0.902, ACC as 0.891, MCC as 0.629 and pAUC as 0.139 and AUC as 0.921 for the independent test protein set-1 and SN as 0.772, SP as 0.901, ACC as 0.836, MCC as 0.677 and pAUC as 0.141 at FPR = 0.10 and AUC as 0.923 for the independent test protein set-2. It also outperformed all the other existing prediction models.

conclusionThe prediction performances as discussed in this article recommend that the proposed method might be a useful and encouraging computational resource for lysine succinylation site prediction in the case of human population.

Indexed as

encoding schemesfeature selectionfusion modellysine succinylation sitepredictionProtein sequencesrandom forest

Identifiers

PMID34220299

DOI10.2174/1389202922666210219114211

PMCPMC8188582

What Socratic holds

Textmetadata

LicenceCC BY-NC

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