Local structural motifs of protein backbones are classified by self-organizing neural networks.

Important and relevant information is expected to be encoded in local structural elements of proteins. An unsupervised learning algorithm (Kohonen algorithm) was applied to the representation and unbiased classification of local backbone structures contained in a set of proteins. Training yielded a two-dimensional Kohonen feature map with 100 different structural motifs including certain helical and strand structures. All motifs were represented in a phi-psi-plot and some of them as a three-dimensional model. The course of structural motifs along the backbone of four selected proteins (cytochrome b5, cytochrome b562, lysozyme, gamma crystallin) was investigated in detail. Trajectories and histograms visualizing the abundance of characteristic motifs allowed for the distinction between different types of protein overall folds. It is demonstrated how the histograms may be used to construct a structural similarity matrix for proteins. The Kohonen algorithm provides a simple procedure for classification of local protein structures independent of any a priori knowledge of leading structural motifs. Training of the Kohonen network leads to the generation of "consensus structures' serving for the task of classification.