Ilipo-pseaac: Identification of lipoylation sites using statistical moments and general pseaac

Talha Imtiaz Baig, Yaser Daanial Khan, Talha Mahboob Alam, Bharat Biswal, Hanan Aljuaid, Durdana Qaiser Gillani

Research output: Contribution to journalArticlepeer-review

Abstract

Lysine Lipoylation is a protective and conserved Post Translational Modification (PTM) in proteomics research like prokaryotes and eukaryotes. It is connected with many biological processes and closely linked with many metabolic diseases. To develop a perfect and accurate classification model for identifying lipoylation sites at the protein level, the computational methods and several other factors play a key role in this purpose. Usually, most of the techniques and different traditional experimentalmodels have a very high cost. They are time-consuming; so, it is required to construct a predictor model to extract lysine lipoylation sites. This study proposes a model that could predict lysine lipoylation sites with the help of a classification method known as Artificial Neural Network (ANN). The ANN algorithm deals with the noise problemand imbalance classification in lipoylation sites dataset samples. As the result shows in ten-fold cross-validation, a brilliant performance is achieved through the predictor model with an accuracy of 99.88%, and also achieved 0.9976 as the highest value of MCC. So, the predictor model is a very useful and helpful tool for lipoylation sites prediction. Some of the residues around lysine lipoylation sites play a vital part in prediction, as demonstrated during feature analysis. The wonderful results reported through the evaluation and prediction of this model can provide an informative and relative explanation for lipoylation and its molecular mechanisms.

Original languageEnglish (US)
Pages (from-to)215-230
Number of pages16
JournalComputers, Materials and Continua
Volume71
Issue number1
DOIs
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Biomaterials
  • Modeling and Simulation
  • Mechanics of Materials
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Amino acid
  • Feature vector
  • Lipoylation lysine
  • Mathew's correlation coefficient
  • Neural network
  • Post translational modification

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