Gly-LysPred: Identification of Lysine Glycation Sites in Protein Using Position Relative Features and Statistical Moments via Chou's 5 Step Rule
- Authors
- Khanum, S.; Ashraf, M.A.; Karim, A.; Shoaib, B.; Khan, M.A.; Naqvi, R.A.; Siddique, K.; Alswaitti, M.
- Issue Date
- Feb-2021
- Publisher
- TECH SCIENCE PRESS
- Keywords
- Chou' s 5 step rule; Gly-LysPred; Lysine glycation; Position relative features; Post-translational modification; PseAAC
- Citation
- CMC-COMPUTERS MATERIALS & CONTINUA, v.66, no.2, pp.2165 - 2181
- Journal Title
- CMC-COMPUTERS MATERIALS & CONTINUA
- Volume
- 66
- Number
- 2
- Start Page
- 2165
- End Page
- 2181
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/84656
- DOI
- 10.32604/cmc.2020.013646
- ISSN
- 1546-2218
- 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 timeconsuming 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. © 2021 Tech Science Press. All rights reserved.
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