期刊
JOURNAL OF CHEMICAL EDUCATION
卷 94, 期 5, 页码 650-655出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jchemed.6b00533
关键词
First-Year Undergraduate/General; Upper-Division Undergraduate; Biochemistry; Laboratory Instruction; Computer-Based Learning; Inquiry-Based/Discovery Learning; Internet/Web-Based Learning; X-ray Crystallography; Computational Chemistry; Proteins/Peptides
资金
- Valparaiso University Creative Works Committee
- Indiana Space Grant Consortium INSGC-NASA
- National Science Foundation (NSF-MRI grant)
- Pittsburgh Conference Memorial National College
An introductory bioinformatics laboratory experiment focused on protein analysis has been developed that is suitable for undergraduate students in introductory biochemistry courses. The laboratory experiment is designed to be potentially used as a stand-alone activity in which students are introduced to basic bioinformatics tools and applications after they have covered fundamental aspects concerning the structure and function of proteins within a class. This laboratory experiment explores three different protein bioinformatics tasks using freeware programs available on the National Center for Biotechnology Information Web site. Students first determine the identity of a protein, and its close homologues, to which they have been given an amino acid sequence using the BLASTP search tool. They then perform a multiple alignment of this protein with a given selection of proteins using the COBALT program and analyze and interpret the alignment results to predict the most promising sites for mutagenesis. Lastly, students use either the embedded JSmol viewer available on the Research Collaboratory for Structural Bioinformatics Web site or the Protein Workshop program to visualize and annotate the X-ray crystal structure of the green fluorescent protein and identify secondary structural components in the protein's tertiary structure. The suggested tasks integrate introductory background knowledge concerning the purpose of bioinformatics tools in protein research, hands-on training in the use of the relevant software, and practice in both evaluating the produced bioinformatics data and analyzing the data to predict promising experimental choices.
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