Predicting protein sumoylation sites from sequence features

  title={Predicting protein sumoylation sites from sequence features},
  author={Shaolei Teng and Hong Luo and Liangjiang Wang},
  journal={Amino Acids},
Protein sumoylation is a post-translational modification that plays an important role in a wide range of cellular processes. Small ubiquitin-related modifier (SUMO) can be covalently and reversibly conjugated to the sumoylation sites of target proteins, many of which are implicated in various human genetic disorders. The accurate prediction of protein sumoylation sites may help biomedical researchers to design their experiments and understand the molecular mechanism of protein sumoylation. In… 
SUMOgo: Prediction of sumoylation sites on lysines by motif screening models and the effects of various post-translational modifications
The Random Forest machine learning method was applied, as well as motif screening models and a feature selection combination mechanism, to develop a SUMOylation prediction system, referred to as SUMOgo, which verified the important role of PTM in SUMO go and includes a case study on CREB binding protein.
SUMOhydro: A Novel Method for the Prediction of Sumoylation Sites Based on Hydrophobic Properties
This study introduced amino acid hydrophobicity as a parameter into a traditional binary encoding scheme and developed a novel sumoylation site prediction tool termed SUMOhydro, which has been benchmarked against previously described predictors based on an independent dataset, thereby suggesting that the introduction of hydrophOBicity as an additional parameter could assist in the prediction ofSumoylation sites.
iSUMO - integrative prediction of functionally relevant SUMOylation events
iSUMO is trained on a total of 24 large-scale datasets, and it predicts 2,291 and 706 SUMO targets in human and yeast, respectively, five times higher than what existing sequence-based tools predict at the same 5% false positive rate.
Predicting sumoylation sites using support vector machines based on various sequence features, conformational flexibility and disorder
The effectiveness of using various sequence properties in predicting sumoylation sites was investigated with statistical analyses and machine learning approach employing support vector machines and showed that it compares favorably to the existing prediction methods and basic regular expressions scanner.
JASSA: a comprehensive tool for prediction of SUMOylation sites and SIMs
JASSA is a predictor that uses a scoring system based on a Position Frequency Matrix derived from the alignment of experimental SUMOylation sites or SIMs that displays on par or better performances with existing web-tools.
GPS-SUMO: a tool for the prediction of sumoylation sites and SUMO-interaction motifs
A new tool called GPS-SUMO was developed for the prediction of both sumoylation sites and SUMO-interaction motifs (SIMs) in proteins, and was demonstrated to be substantially superior against other existing tools and methods.
Discriminating between Lysine Sumoylation and Lysine Acetylation Using mRMR Feature Selection and Analysis
A method to discriminate between sumoylated lysine residues and acetylated residues was developed and supported the previous finding that there exist different consensus motifs for the two types of PTMs.
Computational Prediction of Proteins Sumoylation: A Review on the Methods and Databases
The current state-of-the-art in silico methods to predict SUMOylation as well as related databases are reviewed.
Systematic Analysis of the Genetic Variability That Impacts SUMO Conjugation and Their Involvement in Human Diseases
The statistical analysis demonstrates that the amino acid variations that directly create new potential lysine sumoylation sites are more likely to cause diseases and it can be anticipated that the method can provide more instructive guidance to identify the mechanisms of genetic diseases.
Protein SUMOylation modification and its associations with disease
The signal crosstalk between SUMOylation and ubiquitination of proteins, protein SUMOolation relations with several diseases, and the identification approaches for SUMOYLation site are discussed.


Systematic study of protein sumoylation: Development of a site‐specific predictor of SUMOsp 2.0
This work developed SUMOsp 2.0, an accurate computing program with an improved group‐based phosphorylation scoring algorithm that has greater prediction accuracy and provides a great resource for researchers and an outline for further mechanistic studies of sumoylation in cellular plasticity and dynamics.
A novel method for high accuracy sumoylation site prediction from protein sequences
By using a statistical method, a new SUMO site prediction method – SUMOpre is developed, which has shown its great accuracy with correlation coefficient, specificity, sensitivity and accuracy.
SUMOsp: a web server for sumoylation site prediction
A computational system SUMOsp—SUMOylation Sites Prediction, based on a manually curated dataset, integrating the results of two methods, GPS and MotifX, which were originally designed for phosphorylation site prediction that offers at least as good prediction performance as the only available method, SUMOplot, on a very large test set.
Prediction of RNA-Binding Residues in Protein Sequences Using Support Vector Machines
Support vector machines as well as artificial neural networks have been trained to predict RNA binding residues from five sequence-derived features, including the solvent accessible surface area, BLAST-based conservation score, hydrophobicity index, side chain pKa value and molecular mass of an amino acid.
Sumoylation regulates diverse biological processes
  • J. Zhao
  • Biology
    Cellular and Molecular Life Sciences
  • 2007
This paper reviews recent progress in the study of SUMO pathways, substrates, and cellular functions and highlights important findings that have accelerated advances in this study field and link sumoylation to human diseases.
BindN+ for accurate prediction of DNA and RNA-binding residues from protein sequence features
A web-based tool is developed to make the SVM classifiers accessible to the research community and suggest that predictions at this level of accuracy may provide useful information for modelling protein-nucleic acid interactions in systems biology studies.
PSSM-based prediction of DNA binding sites in proteins
A neural network based algorithm is implemented to utilize evolutionary information of amino acid sequences in terms of their position specific scoring matrices (PSSMs) for a better prediction of DNA-binding sites.
An extended consensus motif enhances the specificity of substrate modification by SUMO
The roles of clusters of acidic residues located downstream from the core SUMO modification sites in proteins such as the transcription factor Elk‐1 are probed and it is demonstrated that these are functionally important in SUMO‐dependent transcriptional repression of Elk-1 transcriptional activity.
PDSM, a motif for phosphorylation-dependent SUMO modification.
As the first recurrent sumoylation determinant beyond the consensus tetrapeptide, the PDSM provides a valuable tool in predicting new SUMO substrates.