Prediction of post‐translational glycosylation and phosphorylation of proteins from the amino acid sequence

By Nikolaj S. Blom, Thomas Sicheritz-Pontén, Ramneek Gupta, S. Gammeltoft, S. Brunak. Proteomics, 2004

1,023 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e9eeb1b57d351350b39c7512a39e33544c834050

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In Molecular Biology

1. Guangchuang Yu
2. Sheng Wang
3. Jinbo Xu
4. Siqi Sun
5. Peer Bork
6. Lars Juhl Jensen
7. Yi Guan
8. T. T. Lam

Citations in the corpus

• NetworKIN: a resource for exploring cellular phosphorylation networks (2007, methodology): “This has motivated the development of numerous computational methods for predicting kinase–substrate relations, for example, Scansite (4), NetphosK (5,6), PREDIKIN (7), PredPhospho (8) GPS (9), PPSP (10) and KinasePhos (11).”
• Post-translational modifications in proteins: resources, tools and prediction methods (2021, background): “The reversible reactions contain covalent modifications, and the irreversible ones, which proceed in one direction, include proteolytic modifications (5).”
• KinasePhos 2.0: a web server for identifying protein kinase-specific phosphorylation sites based on sequences and coupling patterns (2007, background): “NetPhos (7) developed neural networks to predict phosphorylation sites on serine, threonine and tyrosine residues; however, it cannot provide information on the kinases involved and NetPhosK (8) applied an artificial neural network algorithm to predict 17 PK groups-specific phosphorylation sites.”
• ggtree: an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data (2017, methodology): “The NLG sites were predicted using the NETNGLYC 1.0 Server (Blom et al. 2004) and were read into R and stored in NAG variable (Appendix S2).”
• GPS 2.0, a Tool to Predict Kinase-specific Phosphorylation Sites in Hierarchy *S (2008, methodology): “0) (15, 16), NetPhosK (13), and pkaPS (6).”
• A Review of Ensemble Methods in Bioinformatics (2010, methodology): “3 The application of ensemble methods to regulatory elements prediction Regulatory elements prediction is a general term that encompasses tasks such as promoter region recognition [5, 65], transcription start sites prediction [66], or glycosylation site and phosphorylation site prediction [67].”
• The structure of mouse cytomegalovirus m04 protein determined from sparse NMR data reveals a new fold of the m02-m06 family of viral immune modulators (2014, neutral): “(C) High-confidence predicted glycosylation sites using NetNGlyc (Blom et al., 2004).”
• AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling (2016, background): “For example, in post-translation modification (PTM) site prediction, the phosphorylation and methylation sites occur much less frequently than normal residues [2].”