ArticleScientific reports2016
Prediction of phosphothreonine sites in human proteins by fusing different features.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
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- Pretraining model for biological sequence data.Briefings in functional genomics · 2021Review
- Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework.Briefings in bioinformatics · 2019Article
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- Large-scale in-silico statistical mutagenesis analysis sheds light on the deleteriousness landscape of the human proteome.Scientific reports · 2018Article
- iRNA-3typeA: Identifying Three Types of Modification at RNA's Adenosine Sites.Molecular therapy. Nucleic acids · 2018Article
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- HBPred: a tool to identify growth hormone-binding proteins.International journal of biological sciences · 2018Article
- iRSpot-Pse6NC: Identifying recombination spots inInternational journal of biological sciences · 2018Article
- Glypre: In Silico Prediction of Protein Glycation Sites by Fusing Multiple Features and Support Vector Machine.Molecules (Basel, Switzerland) · 2017Article
- IonchanPred 2.0: A Tool to Predict Ion Channels and Their Types.International journal of molecular sciences · 2017Article
- SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots.Scientific reports · 2017Article
- Biosynthesis and genetic encoding of phosphothreonine through parallel selection and deep sequencing.Nature methods · 2017Article
- Recent Advances in Conotoxin Classification by Using Machine Learning Methods.Molecules (Basel, Switzerland) · 2017Review
- Sequence-based predictive modeling to identify cancerlectins.Oncotarget · 2017Article
- Multi-scale encoding of amino acid sequences for predicting protein interactions using gradient boosting decision tree.PloS one · 2017Article
- A novel feature ranking method for prediction of cancer stages using proteomics data.PloS one · 2017Article
- Article
- Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties.PloS one · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phosphorylation is one of the most important protein post-translation modifications. With the rapid development of high-throughput mass spectrometry, phosphorylation site data is rapidly accumulating, which provides us an opportunity to systematically investigate and predict phosphorylation in proteins. The phosphorylation of threonine is the addition of a phosphoryl group to its polar side chains group. In this work, we statistically analyzed the distribution of the different properties including position conservation, secondary structure, accessibility and some other physicochemical properties of the residues surrounding the phosphothreonine site and non-phosphothreonine site. We found that the distributions of those features are non-symmetrical. Based on the distribution of properties, we developed a new model by using optimal window size strategy and feature selection technique. The cross-validated results show that the area under receiver operating characteristic curve reaches to 0.847, suggesting that our model may play a complementary role to other existing methods for predicting phosphothreonine site in proteins.
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Registered trials
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