Open Access iconOpen Access

ARTICLE

crossmark

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: email

Computers, Materials & Continua 2021, 66(2), 2165-2181. https://doi.org/10.32604/cmc.2020.013646

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


Cite This Article

APA Style
Khanum, S., Ashraf, M.A., Karim, A., Shoaib, B., Khan, M.A. et al. (2021). 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, 66(2), 2165-2181. https://doi.org/10.32604/cmc.2020.013646
Vancouver Style
Khanum S, Ashraf MA, Karim A, Shoaib B, Khan MA, Naqvi RA, et al. Gly-lyspred: identification of lysine glycation sites in protein using position relative features and statistical moments via chou’s 5 step rule. Comput Mater Contin. 2021;66(2):2165-2181 https://doi.org/10.32604/cmc.2020.013646
IEEE Style
S. Khanum et al., "Gly-LysPred: Identification of Lysine Glycation Sites in Protein Using Position Relative Features and Statistical Moments via Chou’s 5 Step Rule," Comput. Mater. Contin., vol. 66, no. 2, pp. 2165-2181. 2021. https://doi.org/10.32604/cmc.2020.013646

Citations




cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 2542

    View

  • 1305

    Download

  • 0

    Like

Share Link