PSSM-Suc: Accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction

Abdollah Dehzangi, Yosvany López, Sunil Pranit Lal, Ghazaleh Taherzadeh, Jacob Michaelson, Abdul Sattar, Tatsuhiko Tsunoda, Alok Sharma

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

Abstract

Post-translational modification (PTM) is a covalent and enzymatic modification of proteins, which contributes to diversify the proteome. Despite many reported PTMs with essential roles in cellular functioning, lysine succinylation has emerged as a subject of particular interest. Because its experimental identification remains a costly and time-consuming process, computational predictors have been recently proposed for tackling this important issue. However, the performance of current predictors is still very limited. In this paper, we propose a new predictor called PSSM-Suc which employs evolutionary information of amino acids for predicting succinylated lysine residues. Here we described each lysine residue in terms of profile bigrams extracted from position specific scoring matrices. We compared the performance of PSSM-Suc to that of existing predictors using a widely used benchmark dataset. PSSM-Suc showed a significant improvement in performance over state-of-the-art predictors. Its sensitivity, accuracy and Matthews correlation coefficient were 0.8159, 0.8199 and 0.6396, respectively.

Original languageEnglish (US)
Pages (from-to)97-102
Number of pages6
JournalJournal of Theoretical Biology
Volume425
DOIs
StatePublished - Jul 21 2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Keywords

  • Amino acids
  • Protein sequences
  • Succinylation prediction

Fingerprint

Dive into the research topics of 'PSSM-Suc: Accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction'. Together they form a unique fingerprint.

Cite this