Abstract
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.
Highlights
In biological systems, Glutamic acid is a crucial amino acid which is used in protein biosynthesis
The critical evaluation metrics employed in this study include the receiver operating characteristics learning curve (ROC), precision-recall, Area under Curve, accuracy, and matthew’s correlation coefficient to name a few
To represent the findings of precision-recall curve in a single scalar value, mean average precision is used which is defined as the region under the precision-recall curve
Summary
Glutamic acid is a crucial amino acid which is used in protein biosynthesis. 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). DNNs do not require prior feature extraction, because the deep model can automatically learn the low-dimensional, task specific and optimal feature representation from hierarchical non-linear transformations of original pseudo Amino ACID Composition (PseAAC) sequences. The majority of the works for PTM prediction are comprised of conventional machine-learning-based feature extraction methodology, deep learning is gaining popularity to solve proteomics and genomics problems due to the non-requirement of prior costly feature e xtraction[14,26,27]. In contrast to previously proposed conventional ML based predictors, which rely on quality of features, the current analysis aims to devise an in-silico approach for CarboxE site prediction by fusing DNNs with Chou’s five-step rule[28] as presented in Fig. 1 and used extensively by previous s tudies[5,11,12,13,14,15,16,17]
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