ArticleBioMed research international2013
Position-specific analysis and prediction of protein pupylation sites based on multiple features.
Article in BioMed research international, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- PupStruct: Prediction of Pupylated Lysine Residues Using Structural Properties of Amino Acids.Genes · 2020Article
- An Information Entropy-Based Approach for Computationally Identifying Histone Lysine Butyrylation.Frontiers in genetics · 2019Article
- Recognition of Protein Pupylation Sites by Adopting Resampling Approach.Molecules (Basel, Switzerland) · 2018Article
- Decision-Tree Based Meta-Strategy Improved Accuracy of Disorder Prediction and Identified Novel Disordered Residues Inside Binding Motifs.International journal of molecular sciences · 2018Article
- SVM-SulfoSite: A support vector machine based predictor for sulfenylation sites.Scientific reports · 2018Article
- CIPPN: computational identification of protein pupylation sites by using neural network.Oncotarget · 2017Article
- EPuL: An Enhanced Positive-Unlabeled Learning Algorithm for the Prediction of Pupylation Sites.Molecules (Basel, Switzerland) · 2017Article
- Mal-Lys: prediction of lysine malonylation sites in proteins integrated sequence-based features with mRMR feature selection.Scientific reports · 2016Article
- iSulf-Cys: Prediction of S-sulfenylation Sites in Proteins with Physicochemical Properties of Amino Acids.PloS one · 2016Article
- Computational Identification of Protein Pupylation Sites by Using Profile-Based Composition of k-Spaced Amino Acid Pairs.PloS one · 2015Article
- JPPRED: Prediction of Types of J-Proteins from Imbalanced Data Using an Ensemble Learning Method.BioMed research international · 2015Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pupylation is one of the most important posttranslational modifications of proteins; accurate identification of pupylation sites will facilitate the understanding of the molecular mechanism of pupylation. Besides the conventional experimental approaches, computational prediction of pupylation sites is much desirable for their convenience and fast speed. In this study, we developed a novel predictor to predict the pupylation sites. First, the maximum relevance minimum redundancy (mRMR) and incremental feature selection methods were made on five kinds of features to select the optimal feature set. Then the prediction model was built based on the optimal feature set with the assistant of the support vector machine algorithm. As a result, the overall jackknife success rate by the new predictor on a newly constructed benchmark dataset was 0.764, and the Mathews correlation coefficient was 0.522, indicating a good prediction. Feature analysis showed that all features types contributed to the prediction of protein pupylation sites. Further site-specific features analysis revealed that the features of sites surrounding the central lysine contributed more to the determination of pupylation sites than the other sites.
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