Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China; Huazhong University of Science and Technology Ezhou Industrial Technology Research Institute, Ezhou 436044, China.
Brief Bioinform. 2021 Mar 22;22(2):1836-1847. doi: 10.1093/bib/bbaa038.
As an important reversible lipid modification, S-palmitoylation mainly occurs at specific cysteine residues in proteins, participates in regulating various biological processes and is associated with human diseases. Besides experimental assays, computational prediction of S-palmitoylation sites can efficiently generate helpful candidates for further experimental consideration. Here, we reviewed the current progress in the development of S-palmitoylation site predictors, as well as training data sets, informative features and algorithms used in these tools. Then, we compiled a benchmark data set containing 3098 known S-palmitoylation sites identified from small- or large-scale experiments, and developed a new method named data quality discrimination (DQD) to distinguish data quality weights (DQWs) between the two types of the sites. Besides DQD and our previous methods, we encoded sequence similarity values into images, constructed a deep learning framework of convolutional neural networks (CNNs) and developed a novel algorithm of graphic presentation system (GPS) 6.0. We further integrated nine additional types of sequence-based and structural features, implemented parallel CNNs (pCNNs) and designed a new predictor called GPS-Palm. Compared with other existing tools, GPS-Palm showed a >31.3% improvement of the area under the curve (AUC) value (0.855 versus 0.651) for general prediction of S-palmitoylation sites. We also produced two species-specific predictors, with corresponding AUC values of 0.900 and 0.897 for predicting human- and mouse-specific sites, respectively. GPS-Palm is free for academic research at http://gpspalm.biocuckoo.cn/.
作为一种重要的可还原脂质修饰，S-棕榈酰化主要发生在蛋白质中特定的半胱氨酸残基上，参与调节各种生物过程，并与人类疾病有关。除了实验检测外，S-棕榈酰化位点的计算预测可以有效地为进一步的实验研究生成有用的候选物。在这里，我们回顾了 S-棕榈酰化位点预测器的最新进展，以及这些工具中使用的训练数据集、信息特征和算法。然后，我们编制了一个基准数据集，其中包含了 3098 个从小规模或大规模实验中鉴定出来的已知 S-棕榈酰化位点，并开发了一种新的方法，称为数据质量判别（DQD），以区分这两种类型的位点的数据质量权重（DQW）。除了 DQD 和我们之前的方法外，我们还将序列相似性值编码为图像，构建了卷积神经网络（CNN）的深度学习框架，并开发了图形表示系统（GPS）6.0 的新算法。我们进一步集成了九种额外的基于序列和结构的特征，实现了并行卷积神经网络（pCNNs），并设计了一个名为 GPS-Palm 的新预测器。与其他现有的工具相比，GPS-Palm 对 S-棕榈酰化位点的一般预测的 AUC 值（0.855 对 0.651）提高了超过 31.3%。我们还生成了两个物种特异性的预测器，分别对应于预测人类和小鼠特异性位点的 AUC 值为 0.900 和 0.897。GPS-Palm 可在 http://gpspalm.biocuckoo.cn/ 上免费用于学术研究。
