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哺乳动物蛋白质O-糖基化的预测:UDP-N-乙酰半乳糖胺:多肽N-乙酰半乳糖胺基转移酶的特异性模式

Prediction of O-glycosylation of mammalian proteins: specificity patterns of UDP-GalNAc:polypeptide N-acetylgalactosaminyltransferase.

作者信息

Hansen J E, Lund O, Engelbrecht J, Bohr H, Nielsen J O, Hansen J E

机构信息

Laboratory for Infectious Diseases, Hvidovre Hospital, University of Copenhagen, Denmark.

出版信息

Biochem J. 1995 Jun 15;308 ( Pt 3)(Pt 3):801-13. doi: 10.1042/bj3080801.

Abstract

The specificity of the enzyme(s) catalysing the covalent link between the hydroxyl side chains of serine or threonine and the sugar moiety N-acetylgalactosamine (GalNAc) is unknown. Pattern recognition by artificial neural networks and weight matrix algorithms was performed to determine the exact position of in vivo O-linked GalNAc-glycosylated serine and threonine residues from the primary sequence exclusively. The acceptor sequence context for O-glycosylation of serine was found to differ from that of threonine and the two types were therefore treated separately. The context of the sites showed a high abundance of proline, serine and threonine extending far beyond the previously reported region covering positions -4 through +4 relative to the glycosylated residue. The O-glycosylation sites were found to cluster and to have a high abundance in the N-terminal part of the protein. The sites were also found to have an increased preference for three different classes of beta-turns. No simple consensus-like rule could be deduced for the complex glycosylation sequence acceptor patterns. The neural networks were trained on the hitherto largest data material consisting of 48 carefully examined mammalian glycoproteins comprising 264 O-glycosylation sites. For detection neural network algorithms were much more reliable than weight matrices. The networks correctly found 60-95% of the O-glycosylated serine/threonine residues and 88-97% of the non-glycosylated residues in two independent test sets of known glycoproteins. A computer server using E-mail for prediction of O-glycosylation sites has been implemented and made publicly available. The Internet address is NetOglyc@cbs.dtu.dk.

摘要

催化丝氨酸或苏氨酸的羟基侧链与糖部分N - 乙酰半乳糖胺(GalNAc)之间共价连接的酶的特异性尚不清楚。利用人工神经网络和权重矩阵算法进行模式识别,仅从一级序列确定体内O - 连接的GalNAc糖基化丝氨酸和苏氨酸残基的确切位置。发现丝氨酸O - 糖基化的受体序列上下文与苏氨酸不同,因此将这两种类型分别处理。位点的上下文显示脯氨酸、丝氨酸和苏氨酸的丰度很高,远远超出了先前报道的相对于糖基化残基覆盖位置 -4至 +4的区域。发现O - 糖基化位点聚集并且在蛋白质的N末端部分具有高丰度。还发现这些位点对三种不同类型的β - 转角有更高的偏好。对于复杂的糖基化序列受体模式,无法推导出简单的类似共识的规则。神经网络是在迄今为止最大的数据材料上进行训练的,该数据材料由48种经过仔细研究的哺乳动物糖蛋白组成,包含264个O - 糖基化位点。对于检测,神经网络算法比权重矩阵可靠得多。在两个独立的已知糖蛋白测试集中,网络正确地找到了60 - 95%的O - 糖基化丝氨酸/苏氨酸残基和88 - 97%的非糖基化残基。已经实现了一个使用电子邮件预测O - 糖基化位点的计算机服务器并向公众开放。互联网地址是NetOglyc@cbs.dtu.dk

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