Visualization and Classification of Protein Secondary Structures using Self-Organizing Maps

In molecular biology, it is estimated that there is a correlation between the secondary structure of a protein and its functionality. While secondary structure prediction is ultimately possible in wet lab, determining a correlation with the functionality is a hard task which can be facilitated by a computational model. In that context, this paper presents an automated algorithm for the visualization and classification of enzymatic proteins with the aim of examining whether the functionality is correlated to the secondary structure. To that end, up-to-date protein data was acquired from publicly accessible databases in order to construct their secondary structures. The resulting data were injected into a tailored version of a Kohonen Self-Organizing Map (SOM). Part of the work was to determine a proper way of reducing large secondary structures to a common length in order to be able to cope with the constant dimensionality requirement of SOMs. The final contribution consisted in the labeling of the trained nodes. Eventually, we were able to get a visual intuition and some quantified assessment on the nature of this correlation.