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Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams

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  • Abdollah Dehzangi
  • Yosvany López
  • Sunil Pranit Lal
  • Ghazaleh Taherzadeh
  • Abdul Sattar
  • Tatsuhiko Tsunoda
  • Alok Sharma

Abstract

Post-translational modification refers to the biological mechanism involved in the enzymatic modification of proteins after being translated in the ribosome. This mechanism comprises a wide range of structural modifications, which bring dramatic variations to the biological function of proteins. One of the recently discovered modifications is succinylation. Although succinylation can be detected through mass spectrometry, its current experimental detection turns out to be a timely process unable to meet the exponential growth of sequenced proteins. Therefore, the implementation of fast and accurate computational methods has emerged as a feasible solution. This paper proposes a novel classification approach, which effectively incorporates the secondary structure and evolutionary information of proteins through profile bigrams for succinylation prediction. The proposed predictor, abbreviated as SSEvol-Suc, made use of the above features for training an AdaBoost classifier and consequently predicting succinylated lysine residues. When SSEvol-Suc was compared with four benchmark predictors, it outperformed them in metrics such as sensitivity (0.909), accuracy (0.875) and Matthews correlation coefficient (0.75).

Suggested Citation

  • Abdollah Dehzangi & Yosvany López & Sunil Pranit Lal & Ghazaleh Taherzadeh & Abdul Sattar & Tatsuhiko Tsunoda & Alok Sharma, 2018. "Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-16, February.
  • Handle: RePEc:plo:pone00:0191900
    DOI: 10.1371/journal.pone.0191900
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    References listed on IDEAS

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    1. Frédéric Lamoliatte & Danielle Caron & Chantal Durette & Louiza Mahrouche & Mohamed Ali Maroui & Olivier Caron-Lizotte & Eric Bonneil & Mounira K. Chelbi-Alix & Pierre Thibault, 2014. "Large-scale analysis of lysine SUMOylation by SUMO remnant immunoaffinity profiling," Nature Communications, Nature, vol. 5(1), pages 1-11, December.
    2. Yan Xu & Jun Ding & Ling-Yun Wu & Kuo-Chen Chou, 2013. "iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    3. 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.
    4. Alok Sharma & Abdollah Dehzangi & James Lyons & Seiya Imoto & Satoru Miyano & Kenta Nakai & Ashwini Patil, 2014. "Evaluation of Sequence Features from Intrinsically Disordered Regions for the Estimation of Protein Function," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-7, February.
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