Mohl Jonathon E, Gerken Thomas, Leung Ming-Ying
Department of Mathematical Sciences and Border Biomedical Research Center, The University of Texas at El Paso, El Paso, TX 79968, USA.
Departments of Biochemistry and Chemistry, Case Western Reserve University, Cleveland, OH, 44106, USA.
J Theor Comput Chem. 2020 May;19(3). doi: 10.1142/s0219633620400039. Epub 2020 Jun 15.
Mucin-type O-glycosylation is one of the most common post-translational modifications of proteins. This glycosylation is initiated in the Golgi by the addition of the sugar N-acetylgalactosamine (GalNAc) onto protein Ser and Thr residues by a family of polypeptide GalNAc transferases. In humans there are 20 isoforms that are differentially expressed across tissues that serve multiple important biological roles. Using random peptide substrates, isoform specific amino acid preferences have been obtained in the form of enhancement values (EV). These EVs alone have previously been used to predict O-glycosylation sites via the web based ISOGlyP (Isoform Specific O-Glycosylation Prediction) tool. Here we explore additional protein features to determine whether these can complement the random peptide derived enhancement values and increase the predictive power of ISOGlyP. The inclusion of additional protein substrate features (such as secondary structure and surface accessibility) was found to increase sensitivity with minimal loss of specificity, when tested with three different published O-glycoproteomics data sets, thus increasing the overall accuracy of the ISOGlyP predictions.
粘蛋白型O-糖基化是蛋白质最常见的翻译后修饰之一。这种糖基化在高尔基体中起始，通过一族多肽N-乙酰半乳糖胺（GalNAc）转移酶将糖N-乙酰半乳糖胺添加到蛋白质的丝氨酸（Ser）和苏氨酸（Thr）残基上。在人类中，有20种同工型在不同组织中差异表达，发挥多种重要的生物学作用。使用随机肽底物，已通过增强值（EV）的形式获得了同工型特异性氨基酸偏好。此前仅这些EV已被用于通过基于网络的ISOGlyP（同工型特异性O-糖基化预测）工具预测O-糖基化位点。在这里，我们探索其他蛋白质特征，以确定这些特征是否可以补充源自随机肽的增强值，并提高ISOGlyP的预测能力。当用三个不同的已发表的O-糖蛋白质组学数据集进行测试时，发现纳入其他蛋白质底物特征（如二级结构和表面可及性）可在特异性损失最小的情况下提高敏感性，从而提高ISOGlyP预测的整体准确性。
