Evidence map›Paper›PMID 21829559›Full record

ArticlePloS one2011

Prediction of ubiquitination sites by using the composition of k-spaced amino acid pairs.

Zhen Chen, Yong-Zi Chen, Xiao-Feng Wang, Chuan Wang, Ren-Xiang Yan, Ziding Zhang

Abstract read
In one paragraph

Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers.

0numbers the graph read from it
0cells of the map it votes in
102citing 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

The trial behind it

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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

102 citing papers in PubMed.

  1. Article
  2. Article
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  4. Computational Pipelines for Protein Ubiquitylation Analysis and Prediction.Methods in molecular biology (Clifton, N.J.) · 2026
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42 more citing papers are in PubMed but not listed here.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Zhen ChenState Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China.
Yong-Zi Chen
Xiao-Feng Wang
Chuan Wang
Ren-Xiang Yan
Ziding Zhang

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As one of the most important reversible protein post-translation modifications, ubiquitination has been reported to be involved in lots of biological processes and closely implicated with various diseases. To fully decipher the molecular mechanisms of ubiquitination-related biological processes, an initial but crucial step is the recognition of ubiquitylated substrates and the corresponding ubiquitination sites. Here, a new bioinformatics tool named CKSAAP_UbSite was developed to predict ubiquitination sites from protein sequences. With the assistance of Support Vector Machine (SVM), the highlight of CKSAAP_UbSite is to employ the composition of k-spaced amino acid pairs surrounding a query site (i.e. any lysine in a query sequence) as input. When trained and tested in the dataset of yeast ubiquitination sites (Radivojac et al, Proteins, 2010, 78: 365-380), a 100-fold cross-validation on a 1∶1 ratio of positive and negative samples revealed that the accuracy and MCC of CKSAAP_UbSite reached 73.40% and 0.4694, respectively. The proposed CKSAAP_UbSite has also been intensively benchmarked to exhibit better performance than some existing predictors, suggesting that it can be served as a useful tool to the community. Currently, CKSAAP_UbSite is freely accessible at http://protein.cau.edu.cn/cksaap_ubsite/. Moreover, we also found that the sequence patterns around ubiquitination sites are not conserved across different species. To ensure a reasonable prediction performance, the application of the current CKSAAP_UbSite should be limited to the proteome of yeast.

Indexed as

Computational BiologyPosition-Specific Scoring MatricesUbiquitinationAmino AcidsHumansProtein Processing, Post-TranslationalProteinsSequence Analysis, ProteinSupport Vector MachineAmino AcidsProteins

Identifiers

PMID21829559
PMCPMC3146527

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.