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Updated: Dec 29, 2025
Specificity Analysis of Protein Lysine Methyltransferases Using SPOT Peptide Arrays
Published on: November 29, 2014
iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments
Sarah Ilyas1, Waqar Hussain1, Adeel Ashraf1
11Department of Computer Science, School of Systems and Technology, University of Management and Technology, P.O. Box 10033, C-II, Johar Town, Lahore54770, Pakistan; 2Faculty of Computing and Information Technology in Rabigh, Jeddah, 21577, KSA; 3Gordon Life Science Institute, Boston, MA02478, USA; 4Department of Computer Sciences, Abdul Wali Khan University, Mardan, Pakistan.
Background:
Methylation is one of the most important post-translational modifications in the human body which usually arises on lysine among the most intensely modified residues. It performs a dynamic role in numerous biological procedures, such as regulation of gene expression, regulation of protein function and RNA processing. Therefore, to identify lysine methylation sites is an important challenge as some experimental procedures are time-consuming.
Objective:
Herein, we propose a computational predictor named iMethylK_pseAAC to identify lysine methylation sites.
Methods:
Firstly, we constructed feature vectors based on PseAAC using position and composition rel-ative features and statistical moments. A neural network is trained based on the extracted features. The performance of the proposed method is then validated using cross-validation and jackknife testing.
Results:
The objective evaluation of the predictor showed accuracy of 96.7% for self-consistency, 91.61% for 10-fold cross-validation and 93.42% for jackknife testing.
Conclusion:
It is concluded that iMethylK_pseAAC outperforms the counterparts to identify lysine methylation sites such as iMethyl_pseACC, BPB_pPMS and PMeS.
