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Identify lysine carboxylation sites in proteins by integrating statistical moments and position relative features

Author(s):

Saba Amanat, Adeel Ashraf, Waqar Hussain, Nouman Rasool and Yaser Daanial Khan*  

Abstract:


Carboxylation is one of the most biologically important post-translational modifications and occurs on lysine, arginine and glutamine residues of a protein. Among all these three, the covalent attachment of the carboxyl group with the lysine side chain is the most frequent and biologically important type of carboxylation. The lysine residues play an important part in catalytic reactions, calcium absorption and muscle protein construction. For studying such biological functions, it is essential to correctly determine the lysine sites sensitive to carboxylation. Experimental determination through site-directed mutagenesis and high throughput mass spectrometry is a costly and time taking process, thus, the reliable computational model is required for identification of carboxylysine sites. In this paper, we present a computational model for the prediction of the carboxylysine site which is based on machine learning. Training of the model is performed by a neural network using experimentally verified and updated data. Statistical moments have been used to train a neural network. The model is validated by jackknife, cross-validation, self-consistency and independent testing. Accuracy determined through validation was 93.93% for jackknife test, 95.16% for cross-validation, 94.3% for self-consistency and 94.3% for independent testing. The proposed model has better performance as compared to the existing model PreLysCar, however, the accuracy can be improved further, in future, due to increasing number of carboxylysine sites in proteins.

Keywords:

Carboxylation, Carboxylysine, Statistical Moments, PseAAC, 5-step rule

Affiliation:

Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Department of Life Sciences, School of Science University of Management and Technology, Lahore, Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore



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