eprintid: 16165
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/61/65
datestamp: 2023-12-19 03:22:43
lastmod: 2023-12-19 03:22:43
status_changed: 2023-12-19 03:05:45
type: article
metadata_visibility: show
creators_name: Naseer, S.
creators_name: Ali, R.F.
creators_name: Fati, S.M.
creators_name: Muneer, A.
title: Computational identification of 4-carboxyglutamate sites to supplement physiological studies using deep learning
ispublished: pub
keywords: glutamic acid; protein, amino acid sequence; biology; chemistry; metabolism; molecular model; protein conformation; protein processing; reproducibility; structure activity relation, Amino Acid Sequence; Computational Biology; Deep Learning; Glutamic Acid; Models, Molecular; Protein Conformation; Protein Processing, Post-Translational; Proteins; Reproducibility of Results; Structure-Activity Relationship
note: cited By 7
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. © 2022, The Author(s).
date: 2022
publisher: Nature Research
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122590853&doi=10.1038%2fs41598-021-03895-4&partnerID=40&md5=81f613909aac5d8271dfb20b4570b5f5
id_number: 10.1038/s41598-021-03895-4
full_text_status: none
publication: Scientific Reports
volume: 12
number: 1
refereed: TRUE
issn: 20452322
citation:   Naseer, S. and Ali, R.F. and Fati, S.M. and Muneer, A.  (2022) Computational identification of 4-carboxyglutamate sites to supplement physiological studies using deep learning.  Scientific Reports, 12 (1).   ISSN 20452322