N-MyristoylG-PseAAC: Sequence-based Prediction of N-Myristoyl Glycine Sites in Proteins by Integration of PseAAC and Statistical Moments
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Published:2019-02-11
Issue:3
Volume:16
Page:226-234
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ISSN:1570-1786
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Container-title:Letters in Organic Chemistry
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language:en
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Short-container-title:LOC
Author:
Khan Sher Afzal1, Khan Yaser Daanial2, Ahmad Shakeel3, Allehaibi Khalid H.4
Affiliation:
1. Department of Information Technology, Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Jeddah, Saudi Arabia 2. Department of Computer Science, University of Management Technology, Lahore, Pakistan 3. Department of Computer Sciences, FCITR, King Abdulaziz University, Jeddah, Saudi Arabia 4. Department of Computer Sciences, FCIT, King Abdulaziz University, Jeddah, Saudi Arabia
Abstract
N-Myristoylation, an irreversible protein modification, occurs by the covalent attachment of myristate with the N-terminal glycine of the eukaryotic and viral proteins, and is associated with a variety of pathogens and disease-related proteins. Identification of myristoylation sites through experimental mechanisms can be costly, labour associated and time-consuming. Due to the association of N-myristoylation with various diseases, its timely prediction can help in diagnosing and controlling the associated fatal diseases. Herein, we present a method named N-MyristoylG-PseAAC in which we have incorporated PseAAC with statistical moments for the prediction of N-Myristoyl Glycine (NMG) sites. A benchmark dataset of 893 positive and 1093 negative samples was collected and used in this study. For feature vector, various position and composition relative features along with the statistical moments were calculated. Later on, a back propagation neural network was trained using feature vectors and scaled conjugate gradient descent with adaptive learning was used as an optimizer. Selfconsistency testing and 10-fold cross-validation were performed to evaluate the performance of N-MyristoylG-PseAAC, by using accuracy metrics. For self-consistency testing, 99.80% Acc, 99.78% Sp, 99.81% Sn and 0.99 MCC were observed, whereas, for 10-fold cross validation, 97.18% Acc, 98.54% Sp, 96.07% Sn and 0.94 MCC were observed. Thus, it was found that the proposed predictor can help in predicting the myristoylation sites in an efficient and accurate way.
Funder
King Abdul Aziz University, Jeddah, Saudi Arabia
Publisher
Bentham Science Publishers Ltd.
Subject
Organic Chemistry,Biochemistry
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