. (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....

Combination of four well-established techniques complemented with temperature dependence for probing structural changes and detecting differences between insulin samples.

Attenuated total reflectance (ATR) infrared absorbance spectroscopy of proteins in aqueous solution is much easier to perform than transmission spectroscopy, where short path‐length cells need to be assembled reproducibly. However, the shape of the resulting ATR infrared spectrum varies with the refractive index of the sample and the instrument configuration. Refractive index in turn depends on the absorbance of the sample. In this work, it is shown that a room temperature triglycine sulfate detector and a ZnSe ATR unit can be used to collect reproducible spectra of proteins. A simple method for transforming the protein ATR spectrum into the shape of the transmission spectrum is also given, which proceeds by approximating a Kramers‐Krönig–determined refractive index of water as a sum of four linear components across the amide I and II regions. The light intensity at the crystal surface (with 45° incidence) and its rate of decay away from the surface is determined as a function of the wave number–dependent refractive index as well as the decay of the evanescent wave from the surface. The result is a single correction factor at each wave number. The spectra were normalized to a maximum of 1 between 1600 cm−1 and 1700 cm−1 and a self‐organizing map secondary structure fitting algorithm, SOMSpec, applied using the BioTools reference set. The resulting secondary structure estimates are encouraging for the future of ATR spectroscopy for biopharmaceutical characterization and quality control applications.