scholarly journals Bayesian inference assessment of protein secondary structure analysis using circular dichroism data – how much structural information is contained in protein circular dichroism 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.


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