ArticleBMC bioinformatics2010
PostMod: sequence based prediction of kinase-specific phosphorylation sites with indirect relationship.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- GraphPhos: Predict Protein-Phosphorylation Sites Based on Graph Neural Networks.International journal of molecular sciences · 2025Article
- A web portal for exploring kinase-substrate interactions.NPJ systems biology and applications · 2024Article
- Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery.Proteomes · 2023Review
- Accurate, high-coverage assignment of in vivo protein kinases to phosphosites from in vitro phosphoproteomic specificity data.PLoS computational biology · 2022Article
- Computational Phosphorylation Network Reconstruction: An Update on Methods and Resources.Methods in molecular biology (Clifton, N.J.) · 2021Article
- RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites.Computational and structural biotechnology journal · 2020Article
- NTyroSite: Computational Identification of Protein Nitrotyrosine Sites Using Sequence Evolutionary Features.Molecules (Basel, Switzerland) · 2018Article
- PhosphoPredict: A bioinformatics tool for prediction of human kinase-specific phosphorylation substrates and sites by integrating heterogeneous feature selection.Scientific reports · 2017Article
- Study of phosphorylation events for cancer diagnoses and treatment.Clinical and translational medicine · 2015Article
- Article
- An ensemble method approach to investigate kinase-specific phosphorylation sites.International journal of nanomedicine · 2014Article
- The role of CDK5 and GSK3B kinases in hyperphosphorylation of microtubule associated protein tau (MAPT) in Alzheimer's disease.Bioinformation · 2013Article
- Motif-All: discovering all phosphorylation motifs.BMC bioinformatics · 2011Article
- Intracellular sorting signals for sequential trafficking of human cytomegalovirus UL37 proteins to the endoplasmic reticulum and mitochondria.Journal of virology · 2010Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
backgroundPost-translational modifications (PTMs) have a key role in regulating cell functions. Consequently, identification of PTM sites has a significant impact on understanding protein function and revealing cellular signal transductions. Especially, phosphorylation is a ubiquitous process with a large portion of proteins undergoing this modification. Experimental methods to identify phosphorylation sites are labor-intensive and of high-cost. With the exponentially growing protein sequence data, development of computational approaches to predict phosphorylation sites is highly desirable.
resultsHere, we present a simple and effective method to recognize phosphorylation sites by combining sequence patterns and evolutionary information and by applying a novel noise-reducing algorithm. We suggested that considering long-range region surrounding a phosphorylation site is important for recognizing phosphorylation peptides. Also, from compared results to AutoMotif in 36 different kinase families, new method outperforms AutoMotif. The mean accuracy, precision, and recall of our method are 0.93, 0.67, and 0.40, respectively, whereas those of AutoMotif with a polynomial kernel are 0.91, 0.47, and 0.17, respectively. Also our method shows better or comparable performance in four main kinase groups, CDK, CK2, PKA, and PKC compared to six existing predictors.
conclusionOur method is remarkable in that it is powerful and intuitive approach without need of a sophisticated training algorithm. Moreover, our method is generally applicable to other types of PTMs.
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