Ftir Structural Analysis

[Leto Corrales]

Xray diffraction analysis and NMR are widely used for the structural analysis of proteins. However, infrared spectrophotometry is used for the secondary structural analysis, such as α-helices and β-sheets. As infrared spectrophotometry makes it easy to measure samples in all forms (solid/liquid and crystalline/non-crystalline), it is used to complement the analytical methods mentioned above.

Fig. 1 shows the transmission spectrum of bovine serum albumin.

The peak near 1650 cm-1 in Fig. 1 is the amide I band. It results from the C=O stretching vibrations of the peptide bond.

Similarly, the peaks near 1540 cm-1 (N-H bending vibration/C-N stretching vibration) and 1240 cm-1 (C-N stretching vibration/N-H bending vibration) are called the amide II band, and amide III band, respectively. The peak near 3300 cm-1 is thought to be N-H bending vibration and the peak near 1400 cm-1 to result from protein side-chain COO-.

As the absorption peak position and shape of the amide I band differ according to the secondary structure, peak analysis can yield information on the secondary structure.

An infrared spectrophotometer is mainly used to estimate the structure of organic compounds (qualification).

This instrument shines infrared light onto the molecules, which absorb infrared radiation equivalent to the interatomic vibrational energy of the atoms that comprise the molecules. It then estimates the structure and quantifies the compound by investigating this IR absorbance.

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This work deals with the production of porous silicon samples by electrochemical etching method and their analysis using FTIR and Raman spectroscopy. Porous silicon samples were prepared under various conditions, such as etching time and current density. A p-type silicon substrate was used to prepare the porous silicon structures. FTIR spectroscopy was performed to determine the chemical bonds formed during the etching process. The structural properties of the prepared samples were investigated by Raman spectroscopy.

As the last strands of the human genome project unravel, the focus now turns to proteomics. There are several instrumental methods to analyze protein chemistry and there is particular interest in the functional activity that can be directly attributed to the physical arrangement of a protein.

As opposed to simple chemicals, the 3-dimensional structure of proteins is an inherent part of the protein reactivity and functionality. For instance, muscle fiber is composed primarily of a single type of protein (fibrinogen) with a specific physical structure. In other proteins, rearrangement of the physical configuration can enhance or restrict the protein functionality or reactivity. Thus, the physical arrangement of the protein is as important as the sequence of amino acids. NMR analysis requires very expensive instrumentation. By contrast, FTIR analysis is readily available in most laboratories, has few constraints on sample type or methodology, is inexpensive and simple to operate. As a result, the study and interpretation of infrared protein spectra have been well documented in the literature1-5.

Although there are numerous vibrational interactions possible for crystalline proteins, in truth, most of them present infrared spectra much like synthetic polymers (Figure 1). Demonstrating C-H stretching and bending and some skeletal bands, protein spectra are quite simple in comparison to the complexity often presented by individual chemical spectra. Because of the role played by the amide groups as the backbone for the amino acid residues, the amide vibrational data provides the critical information necessary to predict the secondary structure of the protein. But the variations in the amide band structures are subtle and can be difficult to interpret.

The secondary structure is of intense interest because the configuration determines the reactivity of the protein and can change under certain conditions. There are several methods of analysis used to determine secondary structure (Table 1), but XRD and CD analysis have limited application due to sampling requirements and The JASCO Secondary Structure Estimation (SSE) software uses a spectral modeling procedure for multivariate analysis of infrared protein spectra predicting the secondary structure based on a spectral database of analyzed proteins. Whether the protein is composed of one type of secondary structure, e.g., α-helix, β-sheet, etc. or multiple structural elements, the SSE software can provide an answer.

All spectra were collected using an FT/IR-4600 instrument system equipped with the Spectra Manager II software suite and the optional Secondary Structure Estimation (SSE) software program. Protein spectra were collected using 64 scans at 4 cm-1 resolution, co-added and averaged to obtain all single-beam background and sample spectra. Sample spectra were analyzed with the SSE software after data collection.

Myoglobin and lysozyme samples were dissolved into buffer solution then analyzed as a thin film between two ZnSe windows in a liquid demountable cell. The fingerprint region of a spectrum of lysozome solution is presented as Figure 2.

Figure 3 is the amide region of the lysozyme solution prior to data pre-treatment while Figure 4 is after the buffer subtraction and water vapor correction. Figure 4 also illustrates the various protein secondary structure models as they are used to fit the lysozyme data (black trace). The SSE prediction results for both protein solutions are presented as Table 2, agreeing with published data3,5.

While simple, these examples illustrate the ease with which the analysis is conducted. Simply collect the infrared spectra of the protein solution and the buffer, supply the spectra to the SSE software and the secondary structure prediction is calculated within seconds.

In addition, the accuracy of the SSE program increases as corroborated protein spectra are added to your personal database. Supplied with an initial database of over 50 proteins and their substantiated secondary structure, the SSE software package is ready and able to provide answers from the very start of data processing.

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