Although reactive oxygen species are best known as damag- ing agents linked to aerobic metabolism, it is now clear that they can also function as messengers in cellular signalling processes. Methionine, one of the two sulphur containing amino acids in proteins, is liable to be oxidized by a well-known reactive oxygen species: hydrogen perox- ide. The awareness that methionine oxidation may provide a mecha- nism to the modulation of a wide range of protein functions and cellular processes has recently encouraged proteomic approaches. However, these experimental studies are considerably time-consuming, labor-intensive and expensive, thus making the development of in silico methods for predicting methionine oxidation sites highly desirable. In the field of pro- tein phosphorylation, computational prediction of phosphorylation sites has emerged as a popular alternative approach. On the other hand, very few in-silico studies for methionine oxidation prediction exist in the lit- erature. In the current study we have addressed this issue by developing predictive models based on machine learning strategies and models— random forests, support vector machines, neural networks and flexible discriminant analysis—, aimed at accurate prediction of methionine oxi- dation sites.
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