pSuc-Lys statistics

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pSuc-Lys specifications

Information


Unique identifier OMICS_11162
Name pSuc-Lys
Interface Web user interface
Restrictions to use None
Input data Protein sequence(s)
Input format FASTA
Computer skills Basic
Stability Stable
Maintained Yes

Maintainer


  • person_outline Xuan Xiao

Publication for pSuc-Lys

pSuc-Lys citations

 (3)
library_books

Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams

2018
PLoS One
PMCID: 5809022
PMID: 29432431
DOI: 10.1371/journal.pone.0191900

[…] l-Suc was calculated for 6-, 8- and 10-fold cross-validations.As shown in , SSEvol-Suc represents a significant improvement over the four predictors: iSuc-PseAAC [], iSuc-PseOpt [], SuccinSite [] and pSuc-Lys []. SSEvol-Suc outperformed the previous predictors in statistics such as sensitivity, accuracy and MCC. For instance, sensitivity, accuracy and MCC significantly improved by 47.8%, 21.7% and […]

library_books

Success: evolutionary and structural properties of amino acids prove effective for succinylation site prediction

2018
BMC Genomics
PMCID: 5781056
PMID: 29363424
DOI: 10.1186/s12864-017-4336-8

[…] We compared the Success predictor with three recently proposed predictors, namely, iSuc-PseOpt [], SuccinSite [] and pSuc-Lys []. These predictors are available as active web servers to which any protein sequence can be uploaded for succinylation site identification. It is worth noting that many of our query protein […]

library_books

A systematic identification of species specific protein succinylation sites using joint element features information

2017
PMCID: 5584904
PMID: 28894368
DOI: 10.2147/IJN.S140875

[…] and MCC =0.199) also outperformed the other three predictors including iSuc-PseAAC (Sp =0.887, Sn =0.122, Ac =0.827, and MCC=0.013), iSuc-PseOpt (Sp =0.758, Sn =0.303, Ac =0.722, and MCC =0.038), and pSuc-Lys (Sp =0.826, Sn =0.224, Ac =0.779, and MCC =0.036). Thus, it is anticipated that SuccinSite2.0 is a much more concise and powerful predictor for predicting succinylation sites. […]

Citations

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pSuc-Lys institution(s)
Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China; Computer science, University of Birmingham, UK; Gordon Life Science Institute, Boston, MA, USA; Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
pSuc-Lys funding source(s)
This work was partially supported by the National Natural Science Foundation of China (Nos. 61261027, 31260273, 31560316, 31560316), the Natural Science Foundation of Jiangxi Province, China (No. 20122BAB211033, 20122BAB201044, 20132BAB201053), the Scientific Research plan of the Department of Education of JiangXi Province (GJJ14640).

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