DeepSuccinylSite: A deep learning based approach for protein succinylation site prediction

Niraj Thapa, Meenal Chaudhari, Sean McManus, Kaushik Roy, Robert H. Newman, Hiroto Saigo, Dukka B. Kc

研究成果: Contribution to journalArticle査読

11 被引用数 (Scopus)

抄録

Background: Protein succinylation has recently emerged as an important and common post-translation modification (PTM) that occurs on lysine residues. Succinylation is notable both in its size (e.g., at 100 Da, it is one of the larger chemical PTMs) and in its ability to modify the net charge of the modified lysine residue from + 1 to - 1 at physiological pH. The gross local changes that occur in proteins upon succinylation have been shown to correspond with changes in gene activity and to be perturbed by defects in the citric acid cycle. These observations, together with the fact that succinate is generated as a metabolic intermediate during cellular respiration, have led to suggestions that protein succinylation may play a role in the interaction between cellular metabolism and important cellular functions. For instance, succinylation likely represents an important aspect of genomic regulation and repair and may have important consequences in the etiology of a number of disease states. In this study, we developed DeepSuccinylSite, a novel prediction tool that uses deep learning methodology along with embedding to identify succinylation sites in proteins based on their primary structure. Results: Using an independent test set of experimentally identified succinylation sites, our method achieved efficiency scores of 79%, 68.7% and 0.48 for sensitivity, specificity and MCC respectively, with an area under the receiver operator characteristic (ROC) curve of 0.8. In side-by-side comparisons with previously described succinylation predictors, DeepSuccinylSite represents a significant improvement in overall accuracy for prediction of succinylation sites. Conclusion: Together, these results suggest that our method represents a robust and complementary technique for advanced exploration of protein succinylation.

本文言語英語
論文番号63
ジャーナルBMC bioinformatics
21
DOI
出版ステータス出版済み - 4 23 2020

All Science Journal Classification (ASJC) codes

  • 構造生物学
  • 生化学
  • 分子生物学
  • コンピュータ サイエンスの応用
  • 応用数学

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