Prediction of zinc-binding sites using multiple sequence profiles and machine learning methods-Reference-Cited by-同舟云学术

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

Published:2019 Issue:3 Volume:15 Page:205-215
ISSN:2515-4184
Container-title:Molecular Omics
language:en
Short-container-title:Mol. Omics

Author:

Yan Renxiang¹²³⁴⁵^ORCID,Wang Xiaofeng⁶⁷⁸⁴,Tian Yarong⁹¹⁰¹¹¹²,Xu Jing¹²³⁴⁵,Xu Xiaoli¹²³⁴,Lin Juan¹²³⁴⁵

Affiliation:

1. School of Biological Sciences and Engineering

2. Fuzhou University

3. Fuzhou 350002

4. China

5. Fujian Key Laboratory of Marine Enzyme Engineering

6. College of Mathematics and Computer Science

7. Shanxi Normal University

8. Linfen 041004

9. Institute of Biomedicine

10. Sahlgrenska Academy

11. University of Gothenburg

12. Sweden

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.

Funder

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

Genetics,Molecular Biology,Biochemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2019/MO/C9MO00043G

Reference46 articles.

1. Zinc and human health: an update

2. Predicting small ligand binding sites in proteins using backbone structure

3. Hepatic metal ion transporter ZIP8 regulates manganese homeostasis and manganese-dependent enzyme activity

4. Zinc and the immune system

5. Zinc Deficiency: A Special Challenge

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