ArticleISRN bioinformatics2013
SUMOhunt: Combining Spatial Staging between Lysine and SUMO with Random Forests to Predict SUMOylation.
Article in ISRN bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- Screening gene signatures for clinical response subtypes of lung transplantation.Molecular genetics and genomics : MGG · 2022Article
- Wrestling and Wrapping: A Perspective on SUMO Proteins in Schwann Cells.Biomolecules · 2021Review
- iSUMOK-PseAAC: prediction of lysine sumoylation sites using statistical moments and Chou's PseAAC.PeerJ · 2021Article
- Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.Briefings in bioinformatics · 2019Review
- SUMOgo: Prediction of sumoylation sites on lysines by motif screening models and the effects of various post-translational modifications.Scientific reports · 2018Article
- Computational Prediction and Analysis of Associations between Small Molecules and Binding-Associated S-Nitrosylation Sites.Molecules (Basel, Switzerland) · 2018Article
- Protein SUMOylation modification and its associations with disease.Open biology · 2017Review
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modification with SUMO protein has many key roles in eukaryotic systems which renders the identification of its target proteins and sites of considerable importance. Information regarding the SUMOylation of a protein may tell us about its subcellular localization, function, and spatial orientation. This modification occurs at particular and not all lysine residues in a given protein. In competition with biochemical means of modified-site recognition, computational methods are strong contenders in the prediction of SUMOylation-undergoing sites on proteins. In this research, physicochemical properties of amino acids retrieved from AAIndex, especially those involved in docking of modifier and target proteins and optimal presentation of target lysine, in combination with sequence information and random forest-based classifier presented in WEKA have been used to develop a prediction model, SUMOhunt, with statistics significantly better than all previous predictors. In this model 97.56% accuracy, 100% sensitivity, 94% specificity, and 0.95 MCC have been achieved which shows that proposed amino acid properties have a significant role in SUMO attachment. SUMOhunt will hence bring great reliability and efficiency in SUMOylation prediction.
Identifiers
What OpenQuestion holds
Registered trials
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.