Protein Secondary Structure Prediction from Circular Dichroism Spectra Using a Self‐Organizing Map with Concentration Correction

Abstract

. (2014) Protein secondary structure prediction from circular dichroism spectra using a self-organizing map with concentration correction. Chirality, 26 (9). pp. 471-482. Permanent WRAP url:http://wrap.warwick.ac.uk/75651 Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Publisher's statement:"This is the peer reviewed version of the following article: Hall V., Sklepari M. and Rodger A. (2014), Protein Secondary Structure Prediction from Circular Dichroism Spectra Using a Self-Organizing Map with Concentration Correction, Chirality, 471-482, DOI: 10.1002/chir.22338, which has been published in final form at http://dx.doi.org/10.1002/chir.22338. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving." A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription. ABSTRACT Collecting circular dichroism (CD) spectra for protein solutions is a simple experiment, yet reliable extraction of secondary structure content is dependent on knowledge of the concentration of the protein-which is not always available with accuracy. We previously developed a self-organising map (SOM), called Secondary Structure Neural Network (SSNN), to cluster a database of CD spectra and use that map to assign the secondary structure content of new proteins from CD spectra. The performance of SSNN is at least as good as other available protein CD structure fitting algorithms. In this work we apply SSNN to a collection of spectra of experimental samples where there was suspicion that the nominal protein concentration was incorrect. We show that by plotting the normalized root mean square deviation of the SSNN predicted spectrum from the experimental one versus a concentration scaling-factor it is possible to improve the estimate of the protein concentration while providing an estimate of the secondary structure. For our implementation (51 data points 240 -190 nm in nm increments) good fits and structure estimates are obtained if the NRMSD (normalised root mean square displacement, RMSE/data range) is < 0....

Infrared absorbance spectroscopy of aqueous proteins: Comparison of transmission and ATR data collection and analysis for secondary structure fitting

“…By way of contrast to the IR situation, CD secondary structure estimation by fitting to idealized spectra for different structural motifs is generally recognized to be a poor way of proceeding, and various different methods have been developed for comparing a sample spectrum with a reference set of spectra for proteins of known structure . We recently developed a self‐organizing map neural network CD structure fitting methodology, SSNN, and validated it by leave‐one‐out comparisons with SELCON3 and CDsstr using established reference sets that cover the structural space. We could see no conceptual or fundamental reason why such an approach should not be appropriate for IR absorbance data.…”

“…Circular dichroism (CD) spectroscopy has been gradually accepted as a means of estimating the secondary structure of unknown proteins in the biopharmaceutical arena. There are a range of methods to extract the secondary structure content of a protein sample from its CD spectrum by comparing with a reference set of spectra of known secondary structures . However, any absorption spectroscopy technique has a dynamic range limited by the need to have enough photons reaching the detector for us to be able to count.…”

“…There are a range of methods to extract the secondary structure content of a protein sample from its CD spectrum by comparing with a reference set of spectra of known secondary structures. [2][3][4] However, any absorption spectroscopy technique has a dynamic range limited by the need to have enough photons reaching the detector for us to be able to count. In practice, for protein CD, this means the protein plus anything else in the solution should have an absorbance between about 0.3 and 2.5, and preferably about 1.…”

Bacterial expression, purification and biophysical characterization of the smallest plant reticulon isoform, RTNLB13

“…The machine units of mdeg were converted to mean residue ellipticity (MRE) to normalize to protein concentration. CD data were evaluated using a self-organizing map algorithm, Secondary Structure Neural Network (SSNN), which provided a prediction of the protein's secondary structure and an independent estimate of the samples' concentration 27 . Data below 196 nm were…”

“…Data below 195 nm were not considered due to light scattering and high absorbance reducing the quality of the data. The CD data were fit using the SSNN method 27,28 (see Fig. S2 for fits) in order to estimate the secondary structure content in each detergent environment, and the results are summarized in Fig.…”