Quantitative analysis of protein far UV circular dichroism spectra by neural networks

A new method based on neural network theory is presented toanalyze and quantify the information content of far UV circulardichroism spectra. Using a backpropagation network model witha single hidden layer between input and output, it was possibleto deduce five different secondary structure fractions (helix,parallel and antiparallel ß-sheet, ß-turnand random coil) with satisfactory correlations between calculatedand measured secondary structure data. We demonstrate that foreach wavelength interval a specific network is suitable. Theremaining discrepancy between the secondary structure data fromneural network prediction and crystallography may be attributedto errors in the determination of protein concentration andrandom noise in the CD signal, as indicated by simulations.