Mal-light: Enhancing lysine malonylation sites prediction problem using evolutionary-based features

Md Wakil Ahmad, Md Easin Arafat, Ghazaleh Taherzadeh, Alok Sharma, Shubhashis Roy Dipta, Abdollah Dehzangi, Swakkhar Shatabda

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Post Translational Modification (PTM) is considered an important biological process with a tremendous impact on the function of proteins in both eukaryotes, and prokaryotes cells. During the past decades, a wide range of PTMs has been identified. Among them, malonylation is a recently identified PTM which plays a vital role in a wide range of biological interactions. Notwithstanding, this modification plays a potential role in energy metabolism in different species including Homo Sapiens. The identification of PTM sites using experimental methods is time-consuming and costly. Hence, there is a demand for introducing fast and cost-effective computational methods. In this study, we propose a new machine learning method, called Mal-Light, to address this problem. To build this model, we extract local evolutionary-based information according to the interaction of neighboring amino acids using a bi-peptide based method. We then use Light Gradient Boosting (LightGBM) as our classifier to predict malonylation sites. Our results demonstrate that Mal-Light is able to significantly improve malonylation site prediction performance compared to previous studies found in the literature. Using Mal-Light we achieve Matthew's correlation coefficient (MCC) of 0.74 and 0.60, Accuracy of 86.66% and 79.51%, Sensitivity of 78.26% and 67.27%, and Specificity of 95.05% and 91.75%, for Homo Sapiens and Mus Musculus proteins, respectively. Mal-Light is implemented as an online predictor which is publicly available at: (http://brl.uiu.ac.bd/MalLight/)

Original languageEnglish (US)
Article number9075999
Pages (from-to)77888-77902
Number of pages15
JournalIEEE Access
Volume8
DOIs
StatePublished - 2020
Externally publishedYes

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

Keywords

  • Cluster centroid based majority under-sampling technique
  • evolutionary information
  • light gradient boosting
  • lysine malonylation
  • machine learning
  • post translational modifications

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