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 Alswaitti^6,*

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:

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

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.

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