ArticlePloS one2010
Identifying human kinase-specific protein phosphorylation sites by integrating heterogeneous information from various sources.
Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches.Briefings in bioinformatics · 2019Pooled it
- Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery.Proteomes · 2023Review
- A Transfer-Learning-Based Deep Convolutional Neural Network for Predicting Leukemia-Related Phosphorylation Sites from Protein Primary Sequences.International journal of molecular sciences · 2022Article
- Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins.Journal of translational medicine · 2021Article
- Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks.BMC bioinformatics · 2021Article
- MHC Phosphopeptides: Promising Targets for Immunotherapy of Cancer and Other Chronic Diseases.Molecular & cellular proteomics : MCP · 2021Review
- GasPhos: Protein Phosphorylation Site Prediction Using a New Feature Selection Approach with a GA-Aided Ant Colony System.International journal of molecular sciences · 2020Article
- DeepKinZero: zero-shot learning for predicting kinase-phosphosite associations involving understudied kinases.Bioinformatics (Oxford, England) · 2020Article
- Common Functions of Disordered Proteins across Evolutionary Distant Organisms.International journal of molecular sciences · 2020Review
- LipoSVM: Prediction of Lysine Lipoylation in Proteins based on the Support Vector Machine.Current genomics · 2019Article
- PTM-ssMP: A Web Server for Predicting Different Types of Post-translational Modification Sites Using Novel Site-specific Modification Profile.International journal of biological sciences · 2018Article
- UltraPse: A Universal and Extensible Software Platform for Representing Biological Sequences.International journal of molecular sciences · 2017Article
- PhosphoPredict: A bioinformatics tool for prediction of human kinase-specific phosphorylation substrates and sites by integrating heterogeneous feature selection.Scientific reports · 2017Article
- ksrMKL: a novel method for identification of kinase-substrate relationships using multiple kernel learning.PeerJ · 2017Article
- Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes.BMC bioinformatics · 2016Article
- Article
- Prediction of human disease-associated phosphorylation sites with combined feature selection approach and support vector machine.IET systems biology · 2015Article
- Tyrosine Kinase Ligand-Receptor Pair Prediction by Using Support Vector Machine.Advances in bioinformatics · 2015Article
- Accurate in silico identification of species-specific acetylation sites by integrating protein sequence-derived and functional features.Scientific reports · 2014Article
- PSEA: Kinase-specific prediction and analysis of human phosphorylation substrates.Scientific reports · 2014Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phosphorylation is an important type of protein post-translational modification. Identification of possible phosphorylation sites of a protein is important for understanding its functions. Unbiased screening for phosphorylation sites by in vitro or in vivo experiments is time consuming and expensive; in silico prediction can provide functional candidates and help narrow down the experimental efforts. Most of the existing prediction algorithms take only the polypeptide sequence around the phosphorylation sites into consideration. However, protein phosphorylation is a very complex biological process in vivo. The polypeptide sequences around the potential sites are not sufficient to determine the phosphorylation status of those residues. In the current work, we integrated various data sources such as protein functional domains, protein subcellular location and protein-protein interactions, along with the polypeptide sequences to predict protein phosphorylation sites. The heterogeneous information significantly boosted the prediction accuracy for some kinase families. To demonstrate potential application of our method, we scanned a set of human proteins and predicted putative phosphorylation sites for Cyclin-dependent kinases, Casein kinase 2, Glycogen synthase kinase 3, Mitogen-activated protein kinases, protein kinase A, and protein kinase C families (available at http://cmbi.bjmu.edu.cn/huphospho). The predicted phosphorylation sites can serve as candidates for further experimental validation. Our strategy may also be applicable for the in silico identification of other post-translational modification substrates.
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