A machine learning approach for predicting methionine oxidation sites

• Published in: BMC bioinformatics Vol. 18; no. 1; p. 430
• Main Authors: Aledo, Juan C, Cantón, Francisco R, Veredas, Francisco J
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 29.09.2017; BioMed Central; BMC
• Subjects: Algorithms; Artificial intelligence; Bioinformatics; Classification; Computer applications; Information storage; Kinases; Learning algorithms; Likelihood Functions; Machine Learning; Mathematical models; Methionine; Methionine - metabolism; Methionine sufoxide; Methods; Models, Theoretical; N-Terminus; Neural networks; Oxidation; Oxidation prediction; Oxidation resistance; Oxidation-Reduction; Oxidation-reduction reaction; Oxidative stress; Performance prediction; Phosphorylation; Physiological aspects; Polypeptides; Post-translation; Post-translation modification; Prediction models; Proteins; Residues; ROC Curve; Sensitivity analysis; Spatial discrimination; Sulfoxidation; Sulfur; Support vector machines; Translation; Methionine sufoxide; Oxidation prediction; Post-translation modification; Machine learning

The oxidation of protein-bound methionine to form methionine sulfoxide, has traditionally been regarded as an oxidative damage. However, recent evidences support the view of this reversible reaction as a regulatory post-translational modification. The perception that methionine sulfoxidation may pro...