To the top

Page Manager: Webmaster
Last update: 9/11/2012 3:13 PM

Tell a friend about this page
Print version

Prediction of zinc-bindin… - University of Gothenburg, Sweden Till startsida
Sitemap
To content Read more about how we use cookies on gu.se

Prediction of zinc-binding sites using multiple sequence profiles and machine learning methods

Journal article
Authors R. X. Yan
X. F. Wang
Yarong Tian
J. Xu
X. L. Xu
J. Lin
Published in Molecular Omics
Volume 15
Issue 3
Pages 205-215
Publication year 2019
Published at Institute of Biomedicine, Department of Infectious Medicine
Pages 205-215
Language en
Links dx.doi.org/10.1039/c9mo00043g
Keywords human health, proteins, features, Biochemistry & Molecular Biology, adtman er, 1990, biochemistry, v29, p6323
Subject categories Biochemistry and Molecular Biology

Abstract

The zinc (Zn2+) 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.

Page Manager: Webmaster|Last update: 9/11/2012
Share:

The University of Gothenburg uses cookies to provide you with the best possible user experience. By continuing on this website, you approve of our use of cookies.  What are cookies?