Motivation: As an increasing number of protein structures become available, the need for algorithms that can quantify the similarity between protein structures increases as well. Thus, the comparison of proteins' structures, and their clustering accordingly to a given similarity measure, is at the core of today's biomedical research. In this paper, we show how an algorithmic information theory inspired Universal Similarity Metric (USM) can be used to calculate similarities between protein pairs. The method, besides being theoretically supported, is surprisingly simple to implement and computationally efficient. Results: Structural similarity between proteins in four different datasets was measured using the USM. The sample employed represented alpha, beta, alpha–beta, tim–barrel, globins and serpine protein types. The use of the proposed metric allows for a correct measurement of similarity and classification of the proteins in the four datasets.
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