↓ Skip to main content

Prediction of redox-sensitive cysteines using sequential distance and other sequence-based features

Overview of attention for article published in BMC Bioinformatics, August 2016
Altmetric Badge

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
14 Dimensions

Readers on

mendeley
42 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Prediction of redox-sensitive cysteines using sequential distance and other sequence-based features
Published in
BMC Bioinformatics, August 2016
DOI 10.1186/s12859-016-1185-4
Pubmed ID
Authors

Ming-an Sun, Qing Zhang, Yejun Wang, Wei Ge, Dianjing Guo

Abstract

Reactive oxygen species can modify the structure and function of proteins and may also act as important signaling molecules in various cellular processes. Cysteine thiol groups of proteins are particularly susceptible to oxidation. Meanwhile, their reversible oxidation is of critical roles for redox regulation and signaling. Recently, several computational tools have been developed for predicting redox-sensitive cysteines; however, those methods either only focus on catalytic redox-sensitive cysteines in thiol oxidoreductases, or heavily depend on protein structural data, thus cannot be widely used. In this study, we analyzed various sequence-based features potentially related to cysteine redox-sensitivity, and identified three types of features for efficient computational prediction of redox-sensitive cysteines. These features are: sequential distance to the nearby cysteines, PSSM profile and predicted secondary structure of flanking residues. After further feature selection using SVM-RFE, we developed Redox-Sensitive Cysteine Predictor (RSCP), a SVM based classifier for redox-sensitive cysteine prediction using primary sequence only. Using 10-fold cross-validation on RSC758 dataset, the accuracy, sensitivity, specificity, MCC and AUC were estimated as 0.679, 0.602, 0.756, 0.362 and 0.727, respectively. When evaluated using 10-fold cross-validation with BALOSCTdb dataset which has structure information, the model achieved performance comparable to current structure-based method. Further validation using an independent dataset indicates it is robust and of relatively better accuracy for predicting redox-sensitive cysteines from non-enzyme proteins. In this study, we developed a sequence-based classifier for predicting redox-sensitive cysteines. The major advantage of this method is that it does not rely on protein structure data, which ensures more extensive application compared to other current implementations. Accurate prediction of redox-sensitive cysteines not only enhances our understanding about the redox sensitivity of cysteine, it may also complement the proteomics approach and facilitate further experimental investigation of important redox-sensitive cysteines.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 29%
Researcher 6 14%
Student > Bachelor 5 12%
Student > Master 4 10%
Other 3 7%
Other 6 14%
Unknown 6 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 31%
Agricultural and Biological Sciences 9 21%
Chemistry 6 14%
Computer Science 4 10%
Pharmacology, Toxicology and Pharmaceutical Science 1 2%
Other 3 7%
Unknown 6 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 August 2016.
All research outputs
#6,279,598
of 8,276,988 outputs
Outputs from BMC Bioinformatics
#3,011
of 3,607 outputs
Outputs of similar age
#180,272
of 253,588 outputs
Outputs of similar age from BMC Bioinformatics
#106
of 134 outputs
Altmetric has tracked 8,276,988 research outputs across all sources so far. This one is in the 13th percentile – i.e., 13% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,607 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 7th percentile – i.e., 7% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 253,588 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 134 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.