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

Proteomics, 2004

1,551 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/60bc81a704f675f882088a2e40ee83124b2a4699

Citations in the corpus

• GPS 2.0, a Tool to Predict Kinase-specific Phosphorylation Sites in Hierarchy *S (2008, methodology): “ilarity from BLAST results (9–13, 15–17, 19).”
• Molecular Evolution of Zika Virus during Its Emergence in the 20th Century (2014, methodology): “0 [51,52] methods that employ algorithms based in neural networks to predict, respectively, C-mannosylated, mucin-type O-linked, N-acetylglucosamine (GlcNAc) and N-linked”
• 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).”
• 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].”
• Linear Motif Atlas for Phosphorylation-Dependent Signaling (2008, methodology): “Although many other machine-learning algorithms are available, we chose to use ANNs because they are fast to construct and to execute and have been successfully used for predicting various linear motifs ( 20 , 35 , 36 ).”
• PHOSIDA 2011: the posttranslational modification database (2010, neutral): “NetPhosK (37) aims to Figure 1.”
• 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.”
• 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): “…recombination (RASREC) algorithm within Rosetta greatly enhances the sampling of nonlocal features such as long-range contacts through b pairings (Bradley and Baker, 2006) and allows structural convergence in systems with complicated backbone topologies and high contact order (Lange and Baker,…”