Using circular dichroism spectra to estimate protein secondary structure

Using circular dichroism spectra to estimate protein secondary structure

2006 | Norma J. Greenfield
This article provides a comprehensive guide to using circular dichroism (CD) spectroscopy to estimate the secondary structure and folding properties of proteins. CD is a rapid and effective method for evaluating protein structure, particularly useful in proteomics and structural genomics. The article covers the basic steps of obtaining and interpreting CD data, including the preparation of buffers, proteins, and cuvettes, as well as the analysis of spectra to estimate secondary structural composition. Key considerations for designing and implementing CD experiments are discussed, such as choosing appropriate cuvettes, preparing buffers, and determining protein concentration. The article also details various methods for analyzing CD spectra, including linear regression, ridge regression, singular value decomposition, variable selection, self-consistent methods, neural networks, and convex constraint algorithms. Each method is described in detail, along with the advantages and limitations. The article concludes with a protocol for setting up and operating a CD machine, troubleshooting tips, and anticipated results.This article provides a comprehensive guide to using circular dichroism (CD) spectroscopy to estimate the secondary structure and folding properties of proteins. CD is a rapid and effective method for evaluating protein structure, particularly useful in proteomics and structural genomics. The article covers the basic steps of obtaining and interpreting CD data, including the preparation of buffers, proteins, and cuvettes, as well as the analysis of spectra to estimate secondary structural composition. Key considerations for designing and implementing CD experiments are discussed, such as choosing appropriate cuvettes, preparing buffers, and determining protein concentration. The article also details various methods for analyzing CD spectra, including linear regression, ridge regression, singular value decomposition, variable selection, self-consistent methods, neural networks, and convex constraint algorithms. Each method is described in detail, along with the advantages and limitations. The article concludes with a protocol for setting up and operating a CD machine, troubleshooting tips, and anticipated results.
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