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The specificity of UDP-GalNAc:polypeptide N-acetylgalactosaminyltransferase as inferred from a database of in vivo substrates and from the in vitro glycosylation of proteins and peptides.从体内底物数据库以及蛋白质和肽的体外糖基化推断UDP-GalNAc:多肽N-乙酰半乳糖胺基转移酶的特异性。
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一种用于预测N-乙酰半乳糖胺转移酶特异性的序列偶联向量投影模型。

A sequence-coupled vector-projection model for predicting the specificity of GalNAc-transferase.

作者信息

Chou K C

机构信息

Upjohn Laboratories, Kalamazoo, Michigan 49001-4940, USA.

出版信息

Protein Sci. 1995 Jul;4(7):1365-83. doi: 10.1002/pro.5560040712.

DOI:10.1002/pro.5560040712
PMID:7670379
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2143175/
Abstract

The specificity of GalNAc-transferase is consistent with the existence of an extended site composed of nine subsites, denoted by R4, R3, R2, R1, R0, R1', R2', R3', and R4', where the acceptor at R0 is either Ser or Thr to which the reducing monosaccharide is being anchored. To predict whether a peptide will react with the enzyme to form a Ser- or Thr-conjugated glycopeptide, a new method has been proposed based on the vector-projection approach as well as the sequence-coupled principle. By incorporating the sequence-coupled effect among the subsites, the interaction mechanism among subsites during glycosylation can be reflected and, by using the vector projection approach, arbitrary assignment for insufficient experimental data can be avoided. The very high ratio of correct predictions versus total predictions for the data in both the training and the testing sets indicates that the method is self-consistent and efficient. It provides a rapid means for predicting O-glycosylation and designing effective inhibitors of GalNAc-transferase, which might be useful for targeting drugs to specific sites in the body and for enzyme replacement therapy for the treatment of genetic disorders.

摘要

N-乙酰半乳糖胺转移酶的特异性与一个由九个亚位点组成的扩展位点的存在相一致,这些亚位点分别用R4、R3、R2、R1、R0、R1'、R2'、R3'和R4'表示,其中R0处的受体是丝氨酸或苏氨酸,还原单糖锚定在其上。为了预测一种肽是否会与该酶反应形成丝氨酸或苏氨酸共轭糖肽,基于向量投影方法以及序列偶联原理提出了一种新方法。通过纳入亚位点之间的序列偶联效应,可以反映糖基化过程中亚位点之间的相互作用机制,并且通过使用向量投影方法,可以避免对不足的实验数据进行任意赋值。训练集和测试集中数据的正确预测与总预测的比例非常高,这表明该方法是自洽且有效的。它为预测O-糖基化和设计N-乙酰半乳糖胺转移酶的有效抑制剂提供了一种快速手段,这可能有助于将药物靶向体内特定部位以及用于治疗遗传疾病的酶替代疗法。