Vol.66, No.2, 2021, pp.2165-2181, doi:10.32604/cmc.2020.013646
OPEN ACCESS
ARTICLE
Gly-LysPred: Identification of Lysine Glycation Sites in Protein Using Position Relative Features and Statistical Moments via Chou’s 5 Step Rule
  • Shaheena Khanum1, Muhammad Adeel Ashraf2, Asim Karim1, Bilal Shoaib3, Muhammad Adnan Khan4, Rizwan Ali Naqvi5, Kamran Siddique6, Mohammed Alswaitti6,*
1 Department of Computer Science, Lahore University of Management Sciences, Lahore, 54792, Pakistan
2 Department of Computer Science, University of Management and Technology, Lahore, 54770, Pakistan
3 School of Computer Science, Minhaj University Lahore, Lahore, 54770, Pakistan
4 Department of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan
5 Department of Unmanned Vehicle Engineering, Sejong University, Seoul, Korea
6 Department of Information and Communication Technology, School of Electrical and Computer Engineering, Xiamen University Malaysia, Sepang, 43900, Malaysia
* Corresponding Author: Mohammed Alswaitti. Email:
Received 15 August 2020; Accepted 04 September 2020; Issue published 26 November 2020
Abstract
Glycation is a non-enzymatic post-translational modification which assigns sugar molecule and residues to a peptide. It is a clinically important attribute to numerous age-related, metabolic, and chronic diseases such as diabetes, Alzheimer’s, renal failure, etc. Identification of a non-enzymatic reaction are quite challenging in research. Manual identification in labs is a very costly and time-consuming process. In this research, we developed an accurate, valid, and a robust model named as Gly-LysPred to differentiate the glycated sites from non-glycated sites. Comprehensive techniques using position relative features are used for feature extraction. An algorithm named as a random forest with some preprocessing techniques and feature engineering techniques was developed to train a computational model. Various types of testing techniques such as self-consistency testing, jackknife testing, and cross-validation testing are used to evaluate the model. The overall model’s accuracy was accomplished through self-consistency, jackknife, and cross-validation testing 100%, 99.92%, and 99.88% with MCC 1.00, 0.99, and 0.997 respectively. In this regard, a user-friendly webserver is also urbanized to accumulate the whole procedure. These features vectorization methods suggest that they can play a critical role in other web servers which are developed to classify lysine glycation.
Keywords
Gly-LysPred; PseAAC; post-translational modification; lysine glycation; Chou’s 5 step rule; position relative features
Cite This Article
S. Khanum, M. A. Ashraf, A. Karim, B. Shoaib, M. A. Khan et al., "Gly-lyspred: identification of lysine glycation sites in protein using position relative features and statistical moments via chou’s 5 step rule," Computers, Materials & Continua, vol. 66, no.2, pp. 2165–2181, 2021.
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