In this chapter we discuss the combination of nonlinear regression with auxiliary data analysis methods to extract secondary structural features of a protein from its infrared spectrum. Proteins are biopolymers made up of linear chains of amino acid molecules linked end to end in peptide bonds. They are also known as polypeptides [1]. The structures of an amino acid and a dipeptide with a peptide bond are as follows.


amino acid peptide bond

The sequence of amino acids in the polypeptide chain is called the primary structure. The secondary structure of a protein is the way in which it is folded in the native state. Proteins fold in a complicated manner that is essential to their biological function. Proteins called enzymes are catalysts for life-supporting reactions whose activity and function depend intimately on their secondary structures. Other proteins serve as structural components of living organisms, and their function also relies on the secondary structure [1],

Secondary structures of proteins are characterized by helices, sheets, and extended regions within the polypeptide backbone. Other identifiable structural subunits include turns, loops, and disordered coils.

Secondary structural analysis of proteins can be done by X-ray crystallography, nuclear magnetic resonance spectroscopy (see Chapter 8), circular dichroism, and infrared spectroscopy. Among these methods, the technique of Fourier transform infrared (FT-IR) spectroscopy has emerged as a relatively straightforward technique for estimating secondary structural features of proteins in solution, provided the information is extracted from the data correctly [2-5J. The FT-IR method does not provide as detailed a structural picture of a protein as an X-ray crystal structure or an NMR analysis. However, it can be done rapidly on quite small amounts of material. The excellent signal to noise ratio, resolution, and accuracy of FT-IR has greatly facilitated this type of analysis.

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