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MDD-carb: a combinatorial model for the identification of protein carbonylation sites with substrate motifs

Overview of attention for article published in BMC Systems Biology, December 2017
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Title
MDD-carb: a combinatorial model for the identification of protein carbonylation sites with substrate motifs
Published in
BMC Systems Biology, December 2017
DOI 10.1186/s12918-017-0511-4
Pubmed ID
Authors

Hui-Ju Kao, Shun-Long Weng, Kai-Yao Huang, Fergie Joanda Kaunang, Justin Bo-Kai Hsu, Chien-Hsun Huang, Tzong-Yi Lee

Abstract

Carbonylation, which takes place through oxidation of reactive oxygen species (ROS) on specific residues, is an irreversibly oxidative modification of proteins. It has been reported that the carbonylation is related to a number of metabolic or aging diseases including diabetes, chronic lung disease, Parkinson's disease, and Alzheimer's disease. Due to the lack of computational methods dedicated to exploring motif signatures of protein carbonylation sites, we were motivated to exploit an iterative statistical method to characterize and identify carbonylated sites with motif signatures. By manually curating experimental data from research articles, we obtained 332, 144, 135, and 140 verified substrate sites for K (lysine), R (arginine), T (threonine), and P (proline) residues, respectively, from 241 carbonylated proteins. In order to examine the informative attributes for classifying between carbonylated and non-carbonylated sites, multifarious features including composition of twenty amino acids (AAC), composition of amino acid pairs (AAPC), position-specific scoring matrix (PSSM), and positional weighted matrix (PWM) were investigated in this study. Additionally, in an attempt to explore the motif signatures of carbonylation sites, an iterative statistical method was adopted to detect statistically significant dependencies of amino acid compositions between specific positions around substrate sites. Profile hidden Markov model (HMM) was then utilized to train a predictive model from each motif signature. Moreover, based on the method of support vector machine (SVM), we adopted it to construct an integrative model by combining the values of bit scores obtained from profile HMMs. The combinatorial model could provide an enhanced performance with evenly predictive sensitivity and specificity in the evaluation of cross-validation and independent testing. This study provides a new scheme for exploring potential motif signatures at substrate sites of protein carbonylation. The usefulness of the revealed motifs in the identification of carbonylated sites is demonstrated by their effective performance in cross-validation and independent testing. Finally, these substrate motifs were adopted to build an available online resource (MDD-Carb, http://csb.cse.yzu.edu.tw/MDDCarb/ ) and are also anticipated to facilitate the study of large-scale carbonylated proteomes.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 5 19%
Student > Doctoral Student 3 12%
Other 3 12%
Lecturer 2 8%
Researcher 2 8%
Other 3 12%
Unknown 8 31%
Readers by discipline Count As %
Medicine and Dentistry 4 15%
Engineering 2 8%
Pharmacology, Toxicology and Pharmaceutical Science 2 8%
Biochemistry, Genetics and Molecular Biology 2 8%
Computer Science 2 8%
Other 6 23%
Unknown 8 31%

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 13 January 2018.
All research outputs
#9,895,005
of 12,358,022 outputs
Outputs from BMC Systems Biology
#750
of 1,047 outputs
Outputs of similar age
#255,531
of 354,939 outputs
Outputs of similar age from BMC Systems Biology
#34
of 50 outputs
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So far Altmetric has tracked 1,047 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 13th percentile – i.e., 13% of its peers scored the same or lower than it.
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