Evaluation of secondary structure of proteins from UV circular dichroism spectra using an unsupervised learning neural network

Abstract

An optimized self-organizing map algorithm has been used to obtain protein topological (proteinotopic) maps. A neural network is able to arrange a set of proteins depending on their ultraviolet circular dichroism spectra in a completely unsupervised learning process. Analysis of the proteinotopic map reveals that the network extracts the main secondary structure features even with the small number of examples used. Some methods to use the proteinotopic map for protein secondary structure prediction are tested showing a good performance in the 200-240 nm wavelength range that is likely to increase as new protein structures are known.