Technical gazette, Vol. 26 No. 5, 2019.
Original scientific paper
https://doi.org/10.17559/TV-20190730093945
An Improved Prediction Model for Zinc-Binding Sites in Proteins Based on Bayesian Method
Hui Li
; (1) School of Software Engineering, Jinling Institute of Technology, Jiangsu 211169, China / (2) Center for Intelligent Computer Human Interaction, Nanjing Institute of Big Data, Jiangsu, 211169 China / (3) College of Computer Science and Technology, Nanj
Dechang Pi
orcid.org/0000-0002-6593-4563
; College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Jiangsu, 211169 China
Chuanming Chen
; College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Jiangsu, 211169 China
Abstract
The zinc ion is the second richest metal ion in organisms. The proteins binding to zinc ions have important biological functions. However, few scholars have integrated the existing tools to predict the zinc-binding sites in proteins. To make up for this gap, this paper combines three well-known prediction tools into an improved model called IBayes_Zinc to predict the zinc-binding sites, and utilizes the advantages of the Bayesian method in handling incomplete or partial missing data. Specifically, the prediction scores of three existing sequence-based prediction tools were adopted, and the missing values were padded, forming an integrated classification tool. Then, the probabilities of positive and negative samples were computed and categorized as the class with higher probabilities. Experiments were conducted on a non-redundant training dataset and an independent testing dataset. The results show that our method surpassed the other three methods by nearly 5–13% in Matthew correlation coefficient (MCC) and outperformed the latter in recall and precision. The research findings promote the detection of zinc-binding sites in protein sequence and the identification of metalloprotein functions.
Keywords
Bayesian; missing; protein prediction; zinc-binding sites
Hrčak ID:
226039
URI
Publication date:
8.10.2019.
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