In structural biology, determination of structure of proteins is a key step to understand their function and potentially modify it during related health problems. An interesting tool in this process is the absorption variance between the left- and right- circularly polarized infrared light measured as vibrational circular dichroism (VCD). The spectra are usually more sensitive to molecular structure, although the measurement is getting more difficult than for plain infrared light.
If supported by the theory, the VCD technique is an excellent way of determining the secondary structure of proteins in solution. However, for molecules composed of hundreds and thousands of atoms, even the relatively efficient density functional theory cannot be used, since its direct application is very inefficient in terms of required computer time and memory. Although many simpler computational methodologies aimed at significantly increasing the application potential of VCD spectroscopy were proposed, a comparison of experimental spectra with quantum-chemical simulations reamins the most convenient and objective way to extract information on the structure. For large molecules suitable approximate theoretical models thus have to be used to understand the origin of the spectral signals.
Recently, a team of researchers led by professor Petr Bour from the Institute of Organic Chemistry and Biochemistry, Academy of Sciences in Prague, Czech Republic, developed such a model called “Cartesian-coordinate based tensor transfer”. They assessed its potential in enabling the extension of the density functional theory, and obtained spectral intensities of large globular proteins nearly at quantum-chemical precision. The model thus brought about not only a better understanding of the dependence of VCD spectral shapes on the geometry, but increased practical sensitivity of the method to fine structural details and interactions with the environment. Their work is currently published in the research journal, Phys. Chem. Chem. Phys.
Their experiments were designed to thoroughly test the methodology. The set of proteins included hen-egg-white and human lysozyme, human serum albumin, myoglobin, lactalbumin, insulin and concanavalin A. VCD measurements in the native aqueous environment could be achieved by placing the samples in a demountable custom made calcium fluoride cell devoid of any spacer. Eventually, results of the “Cartesian-coordinate based tensor transfer” computations were confronted with the experimental data and protein structure.
The authors observed, for example, that that the high- and low-frequency amide I vibrations differed in their sensitivity to coupling with distant vibrations. Surprisingly, they found that the human and hen lysozymes provided rather dissimilar VCD spectra; this was explicable by long-range coupling of vibrational modes, and somewhat changed the concept of locality typically attributed to this kind of spectroscopy. As one of the authors, Josef Kapitan, says, “These results thus document the viability and enormous potential of VCD and other vibrational optical activity techniques for future molecular studies.”
In summary, professor Petr Bour and colleagues successfully tested the Cartesian-coordinate based tensor transfer approach, enabling to calculate vibrational properties of large molecular systems in general. In this case it helped to significantly clarify the relation between vibrational circular dichroism spectra of proteins and their structure. The simulations explained the main differences observed among proteins differing in secondary structure, provided an insight into the nature of the underlying normal mode vibrations, made it possible to assign all principal vibrational bands, and could partially explain fine spectral changes due to deuteration. Altogether, a very satisfactory agreement with the experiment was achieved and could be used to prevent erroneous interpretation of experimental data.
Jiri Kessler, Valery Andrushchenko, Josef Kapitan and Petr Bour. Insight into vibrational circular dichroism of proteins by density functional modeling. Phys.Chem.Chem.Phys., 2018, 20, 4926Go To Phys.Chem.Chem.Phys.