Prediction of zinc-binding sites using multiple sequence profiles and machine learning methods. (2nd May 2019)

• Record Type: Journal Article
• Authors: Yan, Renxiang; Wang, Xiaofeng; Tian, Yarong; Xu, Jing; Xu, Xiaoli; Lin, Juan
• Is Part Of: Molecular omics. Volume 15:Number 3(2019)
• Page Start: 205
• Page End: 215
• Publication Date: 2019-05-02
• Subjects: Molecular biology -- Periodicals; Biochemistry -- Periodicals; Biological systems -- Periodicals; Molecular Biology; Computational Biology; Biochemistry; Biological systems; Molecular biology; Periodicals; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals
• DOI: 10.1039/c9mo00043g
• Languages: English
• ISSNs: 2515-4184

Abstract

Abstract : The zinc (Zn 2+ ) cofactor has been proven to be involved in numerous biological mechanisms and the zinc-binding site is recognized as one of the most important post-translation modifications in proteins. Abstract : The zinc (Zn 2+ ) cofactor has been proven to be involved in numerous biological mechanisms and the zinc-binding site is recognized as one of the most important post-translation modifications in proteins. Therefore, accurate knowledge of zinc ions in protein structures can provide potential clues for elucidation of protein folding and functions. However, determining zinc-binding residues by experimental means is usually lab-intensive and associated with high cost in most cases. In this context, the development of computational tools for identifying zinc-binding sites is highly desired, especially in the current post-genomic era. In this work, we developed a novel zinc-binding site prediction method by combining several intensively-trained machine learning models. To establish an accurate and generative method, we downloaded all zinc-binding proteins from the Protein Data Bank and prepared a non-redundant dataset. Meanwhile, a well-prepared dataset by other groups was also used. Then, effective and complementary features were extracted from sequences and three-dimensional structures of these proteins. Moreover, several well-designed machine learning models were intensively trained to construct accurate models. To assess the performance, the obtained predictors were stringently benchmarked using the diverse zinc-binding sites. Furthermore, several state-of-the-art in silico methods developed specifically for zinc-binding sites were also evaluated and compared. The results confirmed that our method is very competitive in real world applications and could become a complementary tool to wet lab experiments. To facilitate research in the community, a web server and stand-alone program implementing our method were constructed and are publicly available at ;http://bioinformatics.fzu.edu.cn/znMachine.html . The downloadable program of our method can be easily used for the high-throughput screening of potential zinc-binding sites across proteomes.