iAceS-Deep: Sequence-Based Identification of Acetyl Serine Sites in Proteins Using PseAAC and Deep Neural Representations

In the biological systems, Acetylation is a crucial post-translational modification, prevalent in various physiological functions and pathological conditions like carcinoma and malignancies. To better understand serine acetylation, the first step is the efficient identification of the same. Although...

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Main Authors: Naseer, S., Fati, S.M., Muneer, A., Ali, R.F.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.28995 /
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123356892&doi=10.1109%2fACCESS.2022.3144226&partnerID=40&md5=9e2504b1bb721bed0046165e76dcf0ab
http://eprints.utp.edu.my/28995/
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spelling utp-eprints.289952022-03-17T02:56:41Z iAceS-Deep: Sequence-Based Identification of Acetyl Serine Sites in Proteins Using PseAAC and Deep Neural Representations Naseer, S. Fati, S.M. Muneer, A. Ali, R.F. In the biological systems, Acetylation is a crucial post-translational modification, prevalent in various physiological functions and pathological conditions like carcinoma and malignancies. To better understand serine acetylation, the first step is the efficient identification of the same. Although multiple large-scale in-vivo, ex-vivo, and in-vitro methods have been applied to detect serine acetylation biomarkers, these experimental methods are time-consuming and labor-intensive. This research aims to develop an in-silico solution to supplement wetlab experiments for efficient detection of serine acetylation sites by combining Chou's Pseudo Amino Acid Composition (PseAAC) with deep neural networks (DNNs). By employing well-known DNNs for feature learning and classification of peptide sequences, our approach obsoletes the need to separately perform costly and cumbersome feature learning process. Based on performance evaluation using standard evaluation metrics, CNN and FCN based models, for AcetylSerine site identification, surpassed previously reported predictors which shows the efficacy of proposed approach. © 2013 IEEE. Institute of Electrical and Electronics Engineers Inc. 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123356892&doi=10.1109%2fACCESS.2022.3144226&partnerID=40&md5=9e2504b1bb721bed0046165e76dcf0ab Naseer, S. and Fati, S.M. and Muneer, A. and Ali, R.F. (2022) iAceS-Deep: Sequence-Based Identification of Acetyl Serine Sites in Proteins Using PseAAC and Deep Neural Representations. IEEE Access, 10 . pp. 12953-12965. http://eprints.utp.edu.my/28995/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description In the biological systems, Acetylation is a crucial post-translational modification, prevalent in various physiological functions and pathological conditions like carcinoma and malignancies. To better understand serine acetylation, the first step is the efficient identification of the same. Although multiple large-scale in-vivo, ex-vivo, and in-vitro methods have been applied to detect serine acetylation biomarkers, these experimental methods are time-consuming and labor-intensive. This research aims to develop an in-silico solution to supplement wetlab experiments for efficient detection of serine acetylation sites by combining Chou's Pseudo Amino Acid Composition (PseAAC) with deep neural networks (DNNs). By employing well-known DNNs for feature learning and classification of peptide sequences, our approach obsoletes the need to separately perform costly and cumbersome feature learning process. Based on performance evaluation using standard evaluation metrics, CNN and FCN based models, for AcetylSerine site identification, surpassed previously reported predictors which shows the efficacy of proposed approach. © 2013 IEEE.
format Article
author Naseer, S.
Fati, S.M.
Muneer, A.
Ali, R.F.
spellingShingle Naseer, S.
Fati, S.M.
Muneer, A.
Ali, R.F.
iAceS-Deep: Sequence-Based Identification of Acetyl Serine Sites in Proteins Using PseAAC and Deep Neural Representations
author_sort Naseer, S.
title iAceS-Deep: Sequence-Based Identification of Acetyl Serine Sites in Proteins Using PseAAC and Deep Neural Representations
title_short iAceS-Deep: Sequence-Based Identification of Acetyl Serine Sites in Proteins Using PseAAC and Deep Neural Representations
title_full iAceS-Deep: Sequence-Based Identification of Acetyl Serine Sites in Proteins Using PseAAC and Deep Neural Representations
title_fullStr iAceS-Deep: Sequence-Based Identification of Acetyl Serine Sites in Proteins Using PseAAC and Deep Neural Representations
title_full_unstemmed iAceS-Deep: Sequence-Based Identification of Acetyl Serine Sites in Proteins Using PseAAC and Deep Neural Representations
title_sort iaces-deep: sequence-based identification of acetyl serine sites in proteins using pseaac and deep neural representations
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2022
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123356892&doi=10.1109%2fACCESS.2022.3144226&partnerID=40&md5=9e2504b1bb721bed0046165e76dcf0ab
http://eprints.utp.edu.my/28995/
_version_ 1741197180735062016
score 11.62408

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