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O-GlcNAcylation Prediction: An Unattained Objective

Abstract : Background: O-GlcNAcylation is an essential post-translational modification (PTM) in mammalian cells. It consists in the addition of a N-acetylglucosamine (GlcNAc) residue onto serines or threonines by an O-GlcNAc transferase (OGT). Inhibition of OGT is lethal, and misregulation of this PTM can lead to diverse pathologies including diabetes, Alzheimer’s disease and cancers. Knowing the location of O-GlcNAcylation sites and the ability to accurately predict them is therefore of prime importance to a better understanding of this process and its related pathologies. Purpose: Here, we present an evaluation of the current predictors of O-GlcNAcylation sites based on a newly built dataset and an investigation to improve predictions. Methods: Several datasets of experimentally proven O-GlcNAcylated sites were combined, and the resulting meta-dataset was used to evaluate three prediction tools. We further defined a set of new features following the analysis of the primary to tertiary structures of experimentally proven O-GlcNAcylated sites in order to improve predictions by the use of different types of machine learning techniques. Results: Our results show the failure of currently available algorithms to predict O-GlcNAcylated sites with a precision exceeding 9%. Our efforts to improve the precision with new features using machine learning techniques do succeed for equal proportions of O-GlcNAcylated and non-O-GlcNAcylated sites but fail like the other tools for real-life proportions where ∼ 1.4% of S/T are O-GlcNAcylated. Conclusion: Present-day algorithms for O-GlcNAcylation prediction narrowly outperform random prediction. The inclusion of additional features, in combination with machine learning algorithms, does not enhance these predictions, emphasizing a pressing need for further development. We hypothesize that the improvement of prediction algorithms requires characterization of OGT’s partners.
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https://hal.univ-lille.fr/hal-03268924
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Soumis le : mercredi 23 juin 2021 - 15:34:32
Dernière modification le : mardi 19 octobre 2021 - 16:17:41
Archivage à long terme le : : vendredi 24 septembre 2021 - 18:50:07

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P21.03 Mauri et al., 2021 Adv ...
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Distributed under a Creative Commons Paternité - Pas d'utilisation commerciale 4.0 International License

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Theo Mauri, Laurence Menu-Bouaouiche, Muriel Bardor, Tony Lefebvre, Marc Lensink, et al.. O-GlcNAcylation Prediction: An Unattained Objective. Advances and applications in bioinformatics and chemistry, 2021, 14, pp.87-102. ⟨10.2147/aabc.s294867⟩. ⟨hal-03268924⟩

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