Prediction of Protein–Metal Ion-Binding Sites Using Sequence Homology and Machine-Learning Methods
Zihan Tian 1, Cao Wei 1, Yutaka Moriwaki 1, Tohru Terada 1, Shugo Nakamura 1, Kazuya Sumikoshi 1, Fang Chun 1, and Kentaro Shimizu 1*
Advanced Bioinformatics & Chemistry 1(1) 025-036 https://doi.org/10.25163/abc.11208022130119
Submitted: 21 July 2019 Revised: 22 August 2019 Published: 06 September 2019
The sensitivity of the machine-learning method was 0.8239, and many metal ion-binding sites were predicted only by the machine-learning method.
Abstract
Metal ions are essential for metalloproteins to perform their catalytic or structural functions. To understand their role in protein function, it is important to identify metal ion-binding sites. Because experimental identification is labor-intensive and time-consuming, computational methods are expected to be used in the prediction of protein–metal ion-binding sites. A range of computational methods have been proposed to predict metal ion-binding sites from protein sequences. In this study, we implemented two methods of predicting metal ion-binding sites for Ca2+, Co2+, Cu2+, Cu+, Fe3+, Fe2+, Hg2+, Mg2+, Mn2+, Ni2+, and Zn2+ from amino acid sequences. One is a homology-based method, and the other is a machine-learning method. The homology-based method predicts the binding sites from homologous sequences obtained by a protein–protein basic local alignment search tool (BLASTP) search. The machine-learning method uses a support vector machine with three protein sequence features. Our results showed that the homology-based method achieved an accuracy of 0.9905 and a specificity of 0.9978, while the machine-learning method showed balanced performance with regard to accuracy, sensitivity, and specificity. Especially, the sensitivity of the machine-learning method was 0.8239, and many metal ion-binding sites were predicted only by the machine-learning method.
Keywords: protein, metal ion, binding site prediction, machine learning, homology search
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