Department of Computer Science and Engineering Technology, University of Houston-Downtown, Houston, TX 77002, United States.
School of Computing, Wichita State University, Wichita, KS 67260, United States.
Bioinformatics. 2024 Nov 1;40(11). doi: 10.1093/bioinformatics/btae643.
O-linked glycosylation, an essential post-translational modification process in Homo sapiens, involves attaching sugar moieties to the oxygen atoms of serine and/or threonine residues. It influences various biological and cellular functions. While threonine or serine residues within protein sequences are potential sites for O-linked glycosylation, not all serine and/or threonine residues undergo this modification, underscoring the importance of characterizing its occurrence. This study presents a novel approach for predicting intracellular and extracellular O-linked glycosylation events on proteins, which are crucial for comprehending cellular processes. Two base multi-layer perceptron models were trained by leveraging a stacked generalization framework. These base models respectively use ProtT5 and Ankh O-linked glycosylation site-specific embeddings whose combined predictions are used to train the meta-multi-layer perceptron model. Trained on extensive O-linked glycosylation datasets, the stacked-generalization model demonstrated high predictive performance on independent test datasets. Furthermore, the study emphasizes the distinction between nucleocytoplasmic and extracellular O-linked glycosylation, offering insights into their functional implications that were overlooked in previous studies. By integrating the protein language model's embedding with stacked generalization techniques, this approach enhances predictive accuracy of O-linked glycosylation events and illuminates the intricate roles of O-linked glycosylation in proteomics, potentially accelerating the discovery of novel glycosylation sites.
Stack-OglyPred-PLM produces Sensitivity, Specificity, Matthews Correlation Coefficient, and Accuracy of 90.50%, 89.60%, 0.464, and 89.70%, respectively on a benchmark NetOGlyc-4.0 independent test dataset. These results demonstrate that Stack-OglyPred-PLM is a robust computational tool to predict O-linked glycosylation sites in proteins.
The developed tool, programs, training, and test dataset are available at https://github.com/PakhrinLab/Stack-OglyPred-PLM.
O -linked 糖基化是人类中一种重要的翻译后修饰过程，涉及将糖基部分连接到丝氨酸和/或苏氨酸残基的氧原子上。它影响各种生物和细胞功能。虽然蛋白质序列中的丝氨酸或苏氨酸残基是 O 连接糖基化的潜在位点，但并非所有丝氨酸和/或苏氨酸残基都经历这种修饰，这突显了表征其发生的重要性。本研究提出了一种预测蛋白质细胞内和细胞外 O 连接糖基化事件的新方法，这对于理解细胞过程至关重要。两种基于碱基的多层感知器模型通过利用堆叠泛化框架进行训练。这些基本模型分别使用 ProtT5 和 Ankh O 连接糖基化位点特异性嵌入，其组合预测用于训练元多层感知器模型。在广泛的 O 连接糖基化数据集上进行训练，堆叠泛化模型在独立测试数据集上表现出高预测性能。此外，该研究强调了核质和细胞外 O 连接糖基化之间的区别，为其功能意义提供了新的见解，这些见解在以前的研究中被忽视了。通过将蛋白质语言模型的嵌入与堆叠泛化技术相结合，该方法提高了 O 连接糖基化事件的预测准确性，并阐明了 O 连接糖基化在蛋白质组学中的复杂作用，可能加速新糖基化位点的发现。
Stack-OglyPred-PLM 在基准 NetOGlyc-4.0 独立测试数据集上的灵敏度、特异性、马修斯相关系数和准确性分别为 90.50%、89.60%、0.464 和 89.70%。这些结果表明，Stack-OglyPred-PLM 是一种强大的计算工具，可以预测蛋白质中的 O 连接糖基化位点。
开发的工具、程序、培训和测试数据集可在 https://github.com/PakhrinLab/Stack-OglyPred-PLM 上获得。
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