Open Access

ARTICLE

iPhosD-PseAAC: Identification of phosphoaspartate sites in proteins using statistical moments and PseAAC

ALAA OMRAN ALMAGRABI¹, YASER DAANIAL KHAN², SHER AFZAL KHAN^3,*

1 Faculty of Computing and Information Technology, Department of Information Systems, King Abdulaziz University, Jeddah, 80200, Saudi Arabia
2 Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, 54770, Pakistan
3 Department of Computer Sciences, Abdul Wali Khan University Mardan, Mardan, 23200, Pakistan

* Address correspondence to: Sher Afzal Khan, sher.afzal@awkum. edu.pk

BIOCELL 2021, 45(5), 1287-1298. https://doi.org/10.32604/biocell.2021.013770

Received 20 August 2020; Accepted 18 February 2021; Issue published 12 July 2021

Abstract

Phosphoaspartate is one of the major components of eukaryotes and prokaryotic two-component signaling pathways, and it communicates the signal from the sensor of histidine kinase, through the response regulator, to the DNA alongside transcription features and initiates the transcription of correct response genes. Thus, the prediction of phosphoaspartate sites is critical, and its experimental identification can be expensive, time-consuming, and tedious. For this purpose, we propose iPhosD-PseAAC, a new computational model for predicting phosphoaspartate sites in a particular protein sequence using Chou’s 5-steps rues: (1) Benchmark dataset. (2) The feature extraction techniques such as pseudo amino acid composition (PseAAC), statistical moments, and position relative features. (3) For the classification, artificial neural network AAN will be used. (4) In this step, 10-fold cross-validation and self-consistency testing will be used for validation. For self-consistency testing, 100% Acc is achieved, whereas, for 10-fold crossvalidation 95.14% Acc, 95.58% Sn, 94.70% Sp and 0.95 MCC are observed. (5). The final step is the development of a user-friendly web server for the ease of users. Thus, the iPhosD-PseAAC is the first and novel predictor for accurate and efficient identification of phosphoaspartate sites.

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