Computational Prediction of O-linked Glycosylation Sites that Preferentially Map on Intrinsically Disordered Regions of Extracellular Proteins

Abstract

O-glycosylation of mammalian proteins is one of the important posttranslational modifications. We applied a support vector machine (SVM) to predict whether Ser or Thr is glycosylated, in order to elucidate the O-glycosylation mechanism. O-glycosylated sites were often found clustered along the sequence, whereas other sites were located sporadically. Therefore, we developed two types of SVMs for predicting clustered and isolated sites separately. We found that the amino acid composition was effective for predicting the clustered type, whereas the site-specific algorithm was effective for the isolated type. The highest prediction accuracy for the clustered type was 74%, while that for the isolated type was 79%. The existence frequency of amino acids around the O-glycosylation sites was different in the two types: namely, Pro, Val and Ala had high existence probabilities at each specific position relative to a glycosylation site, especially for the isolated type. Independent component analyses for the amino acid sequences around O-glycosylation sites showed the position-specific existences of the identified amino acids as independent components. The O-glycosylation sites were preferentially located within intrinsically disordered regions of extracellular proteins: particularly, more than 90% of the clustered O-GalNAc glycosylation sites were observed in intrinsically disordered regions. This feature could be the key for understanding the non-conservation property of O-glycosylation, and its role in functional diversity and structural stability.

Elucidating crosstalk mechanisms between phosphorylation and O-GlcNAcylation

“…For example, OGT has been reported to be activated by tyrosine phosphorylation (36), and CaMKII kinase has been reported to be activated by O-GlcNAcylation (31). Finally, we hypothesize that since the majority of O-GlcNAcylation substrates known to date occur either on intrinsically disordered regions of proteins (37) or cotranslationally before protein folding (38), the results described here will have implications for intact protein substrates, modulating their function in vivo.…”

Enhanced Prediction of Intrinsically Disordered Regions with the Optimized Data

“…Amino acid distribution X (5) Localized electrical effect X (10) Polarity [Grantham] X (11) Polarity [Zimmerman] X (24) Beta-sheet propensity derived from designed sequences X (27) Turn propensity X ( Helix Propensity (A+E+Q+H+K+M+L+R) X(101) Position-specific score for A X(102) Position-specific score for C X(103) Position-specific score for D X(104) Position-specific score for E X(106) Position-specific score for G X(107) Position-specific score for H X(108) Position-specific score for I X(112) Position-specific score for N X(115) Position-specific score for R X(116) Position-specific score for S X(117) Position-specific score for T…”

“…Thus, as alternatives to experimental methods, several computational methods have been suggested for disorder prediction based on the amino acid sequence. Most preferred machine learning techniques can be cited as neural networks [6]- [8] and support vector machines [9], [10].…”

“…To improve the quality of prediction, recent studies have sought to find more useful features and to develop more robust predictor [7], [10], [13] For instance, Su et al have used a condensed position specific scoring matrix profile by merging associated columns of the matrix concerned with several physicochemical properties of amino acids [14]. They achieved rather successful prediction via training a Radial Basis Function (RBF) neural network with their proposed PSSMP patterns.…”

Detection of methylation, acetylation and glycosylation of protein residues by monitoring 13C chemical-shift changes

“…Finally, we explore whether the computed  values upon-glycosylation, at the DFT-level of theory (Garay et al, 2014), for the 13 C nucleus closer to the glycosylation site, namely 13 C  for the Ser and Thr and 13 C  for the Asn residue, respectively, can be used as a probe with which to sense the most commonly seen O-and N-glycosylation, namely the O-linked N-acetylglucosamine (GlcpNAc) and N-acetylgalactosamine (GalpNAc) glycosylation of Ser and Thr (Nishikawa et al, 2010), and the N-acetylglucosamine glycosylation of Asn (Chauhan et al, 2013). By focusing our attention on the values upon glycosylation for some selected 13 C nuclei of the residue side-chain, we will be able to determine whether, first, the -values can be used to determine glycosylation and, second, the type of glycosylated residue, e.g., GlcpNAc or GalpNAc for Ser and Thr.…”