scholarly journals G-quadruplex secondary structure from circular dichroism spectroscopy

2017 ◽  
Author(s):  
Rafael del Villar-Guerra ◽  
John O. Trent ◽  
Jonathan B. Chaires

AbstractA curated library of circular dichroism spectra of 23 G-quadruplexes of known structure was built and analyzed. The goal of this study was to use this reference library to develop an algorithm to derive quantitative estimates of the secondary structure content of quadruplexes from their experimental CD spectra. Principle component analysis and singular value decomposition were used to characterize the reference spectral library. CD spectra were successfully fit to obtain estimates of the amounts of base steps in anti-anti, syn-anti or anti-syn conformations, in diagonal or lateral loops or in other conformations. The results show that CD spectra of nucleic acids can be analyzed to obtain quantitative structural information about secondary structure content in an analogous way to methods used to analyze protein CD spectra.

2021 ◽  
Author(s):  
Simon E. F. Spencer ◽  
Alison Rodger

Bayesian modelling capturing uncertainty and correlations in circular dichroism (CD) spectra suggests it is not possible to identify more than 3 distinct secondary structure classes from CD spectra above 175 nm.


Author(s):  
András Micsonai ◽  
Éva Bulyáki ◽  
József Kardos

Abstract Far-UV circular dichroism (CD) spectroscopy is a classical method for the study of the secondary structure of polypeptides in solution. It has been the general view that the α-helix content can be estimated accurately from the CD spectra. However, the technique was less reliable to estimate the β-sheet contents as a consequence of the structural variety of the β-sheets, which is reflected in a large spectral diversity of the CD spectra of proteins containing this secondary structure component. By taking into account the parallel or antiparallel orientation and the twist of the β-sheets, the Beta Structure Selection (BeStSel) method provides an improved β-structure determination and its performance is more accurate for any of the secondary structure types compared to previous CD spectrum analysis algorithms. Moreover, BeStSel provides extra information on the orientation and twist of the β-sheets which is sufficient for the prediction of the protein fold. The advantage of CD spectroscopy is that it is a fast and inexpensive technique with easy data processing which can be used in a wide protein concentration range and under various buffer conditions. It is especially useful when the atomic resolution structure is not available, such as the case of protein aggregates, membrane proteins or natively disordered chains, for studying conformational transitions, testing the effect of the environmental conditions on the protein structure, for verifying the correct fold of recombinant proteins in every scientific fields working on proteins from basic protein science to biotechnology and pharmaceutical industry. Here, we provide a brief step-by-step guide to record the CD spectra of proteins and their analysis with the BeStSel method.


2014 ◽  
Vol 6 (17) ◽  
pp. 6721-6726 ◽  
Author(s):  
Vincent Hall ◽  
Anthony Nash ◽  
Alison Rodger

SSNN is a self-organising map neural network approach for estimating protein structure from circular dichroism (CD) spectra. The method for using SSNN is described here, and SSNN is compared with CDSSTR, a well-known methodology for finding secondary structures from CD. SSNN compares well with similar methodologies.


2015 ◽  
Author(s):  
◽  
Olayinka Oshokoya

Determination of protein secondary structure has become an area of great importance in biochemistry and biophysics as protein secondary structure is directly related to protein function and protein related diseases. While NMR and x-ray crystallography can predict placement of each atom in proteins to within an angstrom, optical methods are the preferred techniques for rapid evaluation of protein secondary structure content. Such techniques require calibration data to predict unknown protein secondary structure content where accuracy may be improved with the application of multivariate analysis. We compare protein secondary structure predictions obtained from multivariate analysis of ultraviolet resonance Raman (UVRR) and circular dichroism (CD) spectroscopic data using classical and partial least squares, and multivariate curve resolution-alternating least squares is made. Based on this analysis, the suggested best approach to rapid and accurate secondary structure determination is a combination of both CD and UVRR spectroscopy. While initial studies suggest that a complementary use of spectroscopic data from optical methods such as circular dichroism (CD), infrared (IR) and ultraviolet resonance Raman (UVRR) coupled with multivariate calibration techniques like multivariate curve resolution-alternating least squares (MCR-ALS) is the preferred route for real-time and accurate evaluation of protein secondary structure, further study presents a new strategy for the improvement of secondary structure determination of proteins by fusing CD and UVRR spectroscopic data. Also, a new method for determining the structural composition of each protein is employed, which is based on the relative abundance of the (phi,psi) dihedral angles of the peptide backbone as they correspond to each type of secondary structure. Comparison of the predicted protein secondary structures from MCR-ALS analysis of CD, UVRR and fused data with definitions obtained from dihedral angles of the peptide backbone, yields lower overall root mean squared errors of calibration for helical, sheet, poly-proline II type and total unfolded secondary structures with fused data. Considering that a disadvantage of multivariate calibration methods is the requirement of known concentration or spectral profiles, and second-order calibration methods, such as parallel factor analysis (PARAFAC), do not have such a requirement due to the "second-order advantage", PARAFAC was employed for analysis of UVRR data. An exceptional feature of UVRR spectroscopy is that UVRR spectra are also dependent on excitation wavelength as they are on secondary structure composition. Thus, higher order data can be created by combining protein UVRR spectra of several proteins collected at multiple excitation wavelengths. PARAFAC has been used to analyze UVRR data collected at multiple excitation wavelengths on several proteins to determine secondary structure content.


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