Protein secondary structure prediction from circular dichroism spectra using a self-organizing map with concentration...

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-organizing 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 were obtained if the NRMSD (normalized root mean square displacement, RMSE/data range) is <0.03; reasonable for NRMSD <0.05; and variable above this. We also augmented the reference database with 100% helical spectra and truly random coil spectra.