Department of Computer Science, University of Management and Technology, Lahore, 54770, Pakistan.
Computer and Information Sciences Department, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Malaysia.
Sci Rep. 2022 Jan 7;12(1):128. doi: 10.1038/s41598-021-03895-4.
In biological systems, Glutamic acid is a crucial amino acid which is used in protein biosynthesis. Carboxylation of glutamic acid is a significant post-translational modification which plays important role in blood coagulation by activating prothrombin to thrombin. Contrariwise, 4-carboxy-glutamate is also found to be involved in diseases including plaque atherosclerosis, osteoporosis, mineralized heart valves, bone resorption and serves as biomarker for onset of these diseases. Owing to the pathophysiological significance of 4-carboxyglutamate, its identification is important to better understand pathophysiological systems. The wet lab identification of prospective 4-carboxyglutamate sites is costly, laborious and time consuming due to inherent difficulties of in-vivo, ex-vivo and in vitro experiments. To supplement these experiments, we proposed, implemented, and evaluated a different approach to develop 4-carboxyglutamate site predictors using pseudo amino acid compositions (PseAAC) and deep neural networks (DNNs). Our approach does not require any feature extraction and employs deep neural networks to learn feature representation of peptide sequences and performing classification thereof. Proposed approach is validated using standard performance evaluation metrics. Among different deep neural networks, convolutional neural network-based predictor achieved best scores on independent dataset with accuracy of 94.7%, AuC score of 0.91 and F1-score of 0.874 which shows the promise of proposed approach. The iCarboxE-Deep server is deployed at https://share.streamlit.io/sheraz-n/carboxyglutamate/app.py .
在生物系统中，谷氨酸是一种重要的氨基酸，用于蛋白质的生物合成。谷氨酸的羧化作用是一种重要的翻译后修饰，通过激活凝血酶原转化为凝血酶，在血液凝固中发挥重要作用。相反，4-羧基谷氨酸也被发现与包括斑块动脉粥样硬化、骨质疏松症、矿化心脏瓣膜、骨吸收在内的疾病有关，并作为这些疾病发病的生物标志物。由于 4-羧基谷氨酸的病理生理意义，其鉴定对于更好地理解病理生理系统非常重要。由于体内、体外和体外实验固有的困难，对潜在 4-羧基谷氨酸位点的湿实验室鉴定既昂贵又费力且耗时。为了补充这些实验，我们提出、实施和评估了一种使用伪氨基酸组成（PseAAC）和深度神经网络（DNN）开发 4-羧基谷氨酸位点预测器的不同方法。我们的方法不需要任何特征提取，并使用深度神经网络学习肽序列的特征表示，并对其进行分类。所提出的方法使用标准性能评估指标进行验证。在不同的深度神经网络中，基于卷积神经网络的预测器在独立数据集上取得了最佳成绩，准确率为 94.7%，AUC 得分为 0.91，F1 得分为 0.874，这表明了所提出方法的前景。iCarboxE-Deep 服务器部署在 https://share.streamlit.io/sheraz-n/carboxyglutamate/app.py 。
