C-iSUMO: A sumoylation site predictor that incorporates intrinsic characteristics of amino acid sequences

Yosvany López, Abdollah Dehzangi, Hamendra Manhar Reddy, Alok Sharma

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Post-translational modifications are considered important molecular interactions in protein science. One of these modifications is “sumoylation” whose computational detection has recently become a challenge. In this paper, we propose a new computational predictor which makes use of the sine and cosine of backbone torsion angles and the accessible surface area for predicting sumoylation sites. The aforementioned features were computed for all the proteins in our benchmark dataset, and a training matrix consisting of sumoylation and non-sumoylation sites was ultimately created. This training matrix was balanced by undersampling the majority class (non-sumoylation sites) using the NearMiss method. Finally, an AdaBoost classifier was used for discriminating between sumoylation and non-sumoylation sites. Our predictor was called “C-iSumo” because of its effective use of circular functions. C-iSumo was compared with another predictor which was outperformed in statistical metrics such as sensitivity (0.734), accuracy (0.746) and Matthews correlation coefficient (0.494).

Original languageEnglish (US)
Article number107235
JournalComputational Biology and Chemistry
Volume87
DOIs
StatePublished - Aug 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Organic Chemistry
  • Computational Mathematics

Keywords

  • Adaboost
  • Amino acids
  • Computational prediction
  • Proteins
  • Sumoylation

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