↓ Skip to main content

Carbohydrate-Based Vaccines

Overview of attention for book
Attention for Chapter 4: Carbohydrate-Based Vaccines
Altmetric Badge

Readers on

mendeley
9 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
Carbohydrate-Based Vaccines
Chapter number 4
Book title
Carbohydrate-Based Vaccines
Published in
Methods in molecular biology, January 2015
DOI 10.1007/978-1-4939-2874-3_4
Pubmed ID
Book ISBNs
978-1-4939-2873-6, 978-1-4939-2874-3
Authors

Dingjan, Tamir, Agostino, Mark, Ramsland, Paul A, Yuriev, Elizabeth, Ramsland, Paul A., Tamir Dingjan, Mark Agostino, Paul A. Ramsland, Elizabeth Yuriev

Abstract

Carbohydrate-protein recognition is vital to many processes in health and disease. In particular, elucidation of the structural basis of carbohydrate binding is important to the development of oligosaccharides and oligosaccharide mimetics as vaccines for infectious diseases and cancer. Computational structural techniques are valuable for the study of carbohydrate-protein recognition due to the challenges associated with experimental determination of carbohydrate-protein complexes. AutoMap is a computer program that we have developed to study protein-ligand recognition. AutoMap determines the interactions taking place in a set of highly ranked poses obtained from molecular docking and processes these to identify the protein residues most likely to be involved in interactions. In this protocol, we describe the use of AutoMap and illustrate its suitability for studying antibody recognition of the Lewis Y tetrasaccharide, which is a potential cancer vaccine antigen.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 33%
Researcher 2 22%
Professor > Associate Professor 1 11%
Student > Postgraduate 1 11%
Unknown 2 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 22%
Agricultural and Biological Sciences 1 11%
Immunology and Microbiology 1 11%
Medicine and Dentistry 1 11%
Chemistry 1 11%
Other 1 11%
Unknown 2 22%