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

作     者：ALAA OMRAN ALMAGRABI YASER DAANIAL KHAN SHER AFZAL KHAN 

作者机构：Faculty of Computing and Information TechnologyDepartment of Information SystemsKing Abdulaziz UniversityJeddah80200Saudi Arabia Department of Computer ScienceSchool of Systems and TechnologyUniversity of Management and TechnologyLahore54770Pakistan Department of Computer SciencesAbdul Wali Khan University MardanMardan23200Pakistan 

出 版 物：《BIOCELL》 (生物细胞（英文）)

年 卷 期：2021年第45卷第5期

页      面：1287-1298页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the Deanship of Scientific Research(DSR)at King Abdulaziz University Jeddah https://dsr.kau.edu.sa/Default-305-EN under Grant No.G:136-611-1441 

主　　题：Phosphorylation Phosphoaspartate Prediction 5-step rule Statistical moments PseAAC 

摘      要：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 ***,the prediction of phosphoaspartate sites is critical,and its experimental identification can be expensive,time-consuming,and *** 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 *** 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 ***,the iPhosD-PseAAC is the first and novel predictor for accurate and efficient identification of phosphoaspartate sites.