TY  - JOUR
AV  - none
N1  - cited By 7
Y1  - 2022///
TI  - Computational identification of 4-carboxyglutamate sites to supplement physiological studies using deep learning
KW  - glutamic acid; protein
KW  -  amino acid sequence; biology; chemistry; metabolism; molecular model; protein conformation; protein processing; reproducibility; structure activity relation
KW  -  Amino Acid Sequence; Computational Biology; Deep Learning; Glutamic Acid; Models
KW  -  Molecular; Protein Conformation; Protein Processing
KW  -  Post-Translational; Proteins; Reproducibility of Results; Structure-Activity Relationship
PB  - Nature Research
IS  - 1
JF  - Scientific Reports
A1  - Naseer, S.
A1  - Ali, R.F.
A1  - Fati, S.M.
A1  - Muneer, A.
SN  - 20452322
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122590853&doi=10.1038%2fs41598-021-03895-4&partnerID=40&md5=81f613909aac5d8271dfb20b4570b5f5
VL  - 12
N2  - 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. © 2022, The Author(s).
ID  - scholars16165
ER  -