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Translational Biomedical Informatics

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Attention for Chapter 3: Exploring Human Diseases and Biological Mechanisms by Protein Structure Prediction and Modeling.
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Chapter title
Exploring Human Diseases and Biological Mechanisms by Protein Structure Prediction and Modeling.
Chapter number 3
Book title
Translational Biomedical Informatics
Published in
Advances in experimental medicine and biology, November 2016
DOI 10.1007/978-981-10-1503-8_3
Pubmed ID
Book ISBNs
978-9-81-101502-1, 978-9-81-101503-8
Authors

Juexin Wang, Joseph Luttrell IV, Ning Zhang, Saad Khan, NianQing Shi, Michael X. Wang, Jing-Qiong Kang, Zheng Wang, Dong Xu, Joseph Luttrell

Editors

Bairong Shen, Haixu Tang, Xiaoqian Jiang

Abstract

Protein structure prediction and modeling provide a tool for understanding protein functions by computationally constructing protein structures from amino acid sequences and analyzing them. With help from protein prediction tools and web servers, users can obtain the three-dimensional protein structure models and gain knowledge of functions from the proteins. In this chapter, we will provide several examples of such studies. As an example, structure modeling methods were used to investigate the relation between mutation-caused misfolding of protein and human diseases including epilepsy and leukemia. Protein structure prediction and modeling were also applied in nucleotide-gated channels and their interaction interfaces to investigate their roles in brain and heart cells. In molecular mechanism studies of plants, rice salinity tolerance mechanism was studied via structure modeling on crucial proteins identified by systems biology analysis; trait-associated protein-protein interactions were modeled, which sheds some light on the roles of mutations in soybean oil/protein content. In the age of precision medicine, we believe protein structure prediction and modeling will play more and more important roles in investigating biomedical mechanism of diseases and drug design.

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Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 39 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 21%
Student > Bachelor 6 15%
Researcher 4 10%
Professor 4 10%
Unspecified 3 8%
Other 5 13%
Unknown 9 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 26%
Computer Science 6 15%
Agricultural and Biological Sciences 4 10%
Unspecified 3 8%
Medicine and Dentistry 3 8%
Other 4 10%
Unknown 9 23%