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predPhogly-Site: Predicting phosphoglycerylation sites by incorporating probabilistic sequence-coupling information into PseAAC and addressing data imbalance

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  • Sabit Ahmed
  • Afrida Rahman
  • Md Al Mehedi Hasan
  • Md Khaled Ben Islam
  • Julia Rahman
  • Shamim Ahmad

Abstract

Post-translational modification (PTM) involves covalent modification after the biosynthesis process and plays an essential role in the study of cell biology. Lysine phosphoglycerylation, a newly discovered reversible type of PTM that affects glycolytic enzyme activities, and is responsible for a wide variety of diseases, such as heart failure, arthritis, and degeneration of the nervous system. Our goal is to computationally characterize potential phosphoglycerylation sites to understand the functionality and causality more accurately. In this study, a novel computational tool, referred to as predPhogly-Site, has been developed to predict phosphoglycerylation sites in the protein. It has effectively utilized the probabilistic sequence-coupling information among the nearby amino acid residues of phosphoglycerylation sites along with a variable cost adjustment for the skewed training dataset to enhance the prediction characteristics. It has achieved around 99% accuracy with more than 0.96 MCC and 0.97 AUC in both 10-fold cross-validation and independent test. Even, the standard deviation in 10-fold cross-validation is almost negligible. This performance indicates that predPhogly-Site remarkably outperformed the existing prediction tools and can be used as a promising predictor, preferably with its web interface at http://103.99.176.239/predPhogly-Site.

Suggested Citation

  • Sabit Ahmed & Afrida Rahman & Md Al Mehedi Hasan & Md Khaled Ben Islam & Julia Rahman & Shamim Ahmad, 2021. "predPhogly-Site: Predicting phosphoglycerylation sites by incorporating probabilistic sequence-coupling information into PseAAC and addressing data imbalance," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-17, April.
    • Handle: RePEc:plo:pone00:0249396
    DOI: 10.1371/journal.pone.0249396
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    References listed on IDEAS

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    1. Wei-Zhong Lin & Jian-An Fang & Xuan Xiao & Kuo-Chen Chou, 2011. "iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-7, September.
    2. Yan Xu & Xin Wen & Li-Shu Wen & Ling-Yun Wu & Nai-Yang Deng & Kuo-Chen Chou, 2014. "iNitro-Tyr: Prediction of Nitrotyrosine Sites in Proteins with General Pseudo Amino Acid Composition," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-10, August.
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