Circular dichroism spectroscopy is a quick method for determining the average secondary structures of proteins, probing their interactions with their environment, and aiding drug discovery. This article describes the development of a self-organising map structure-fitting methodology named secondary structure neural network (SSNN) to aid this process and reduce the level of expertise required. SSNN uses a database of spectra from proteins with known X-ray structures; prediction of structures for new proteins is then possible. It has been designed as 3 units: SSNN1 takes spectra for known proteins, clusters them into a map, and SSNN2 creates a matching structure map. SSNN3 places unknown spectra on the map and gives them structure vectors. SSNN3 output illustrates the process and results obtained. We detail the strengths and weaknesses of SSNN and compare it with widely accepted structure fitting programs. Current input format is Δε per amino acid residue from 240 to 190 nm in 1 nm steps for the known and unknown proteins and a vector summarizing the secondary structure elements of the known proteins. The format is readily modified to include input data with, for example, extended wavelength ranges or different assignment of secondary structures. SSNN can be used either pretrained with a reference set from the CDPro web site (direct application of SSNN3, with the provided output from SSNN1 and SSNN2) or all three modules can be used as required. SSNN3 is available trained (with the reference set of the 48-spectra set used in this work complemented by five additional spectra) at http://www2.warwick.ac.uk/fac/sci/chemistry/research/arodger/arodgergroup/research_intro/instrumentation/ssnn/.
. (2014) SSNN, a method for neural network protein secondary structure fitting using circular dichroism data. Analytical Methods, 6 (17). pp. 6721-6726. Permanent WRAP url:http://wrap.warwick.ac.uk/75654 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:Published version: http://dx.doi.org/10.1039/C3AY41831F 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. For more information, please contact the WRAP Team at: publications@warwick.ac.uk Circular dichroism (CD) spectroscopy is a quick method for measuring data that can be used to determine the average secondary structures of proteins, probe their interactions with their environment, and aid in drug discovery. This paper describes the operation and testing of a self-organising map (SOM) structure-fitting methodology named Secondary Structure Neural Network (SSNN), which is a methodology for estimating protein secondary structure from CD spectra of unknown proteins using CD spectra of proteins with known X-ray structures. SSNN comes in two standalone MATLAB applications for estimating unknown proteins' structures, one that uses a 10 pre-trained map and one that begins by training the SOM with a reference set of the user's choice. These are available at http://www2.warwick.ac.uk/fac/sci/chemistry/research/arodger/arodgergroup/research_intro/instrumentation/ssnn/ as SSNNGUI and SSNN1_2 respectively. They are available for both Macintosh and Windows formats with two reference sets: one obtained from the CDPro website, referred to as CDDATA.48 which has 48 protein spectra and structures, and one with 53 proteins (CDDATA.48 with 5 additional spectra). Here we compare SSNN with CDSSTR, a widely-used secondary structure methodology, and describe how to use the 15 standalone SSNN applications. Current input format is Δε per amino acid residue from 240 nm to 190 nm in 1 nm steps for the known and unknown proteins and a vector summarising the secondary structure elements of the known proteins. The format is readily modified to include input data with e.g. extende...
. (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....
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