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/).
Highlights
Post-translational modifications (PTMs) are the key tools for regulating numerous biological processes that are affiliated with the control activities of various cells and diseases [1]–[4]
For the purpose of this study, we examine five statistical performance matrices of Mal-Light namely, accuracy, sensitivity, specificity, F1-score, and Matthew’s correlation coefficient [21]–[23], [49], [82], which has been extensively used in the literature
ANALYSIS OF THE RESULTS FOR DIFFERENT SPECIES Here, we report malonylation sites prediction performance for all six species specified in Table 1, and we have collected the dataset from Protein Lysine Modification Database (PLMD) [45]
Summary
Post-translational modifications (PTMs) are the key tools for regulating numerous biological processes that are affiliated with the control activities of various cells and diseases [1]–[4]. Lysine is one of the most widely modified residues among the 20 types of natural amino acids through PTM [8]. It has been associated with numerous PTMs including glycation [9], succinylation [10], [11], methylation [12], [13], acetylation [14], and sumoylation [15]. Lysine malonylation (Kmal) is a recently identified PTM type that is evolutionarily conserved, which is associated with several
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
