Analyzing Protein Similarity by Clustering Molecular Surface Maps

Many biochemical and biomedical applications like protein engineering or drug design are concerned with finding functionally similar proteins, however, this remains to be a challenging task. We present a new imaged-based approach for identifying and visually comparing proteins with similar function that builds on the hierarchical clustering of Molecular Surface Maps. Such maps are two-dimensional representations of complex molecular surfaces and can be used to visualize the topology and different physico-chemical properties of proteins. Our method is based on the idea that visually similar maps also imply a similarity in the function of the mapped proteins. To determine map similarity we compute descriptive feature vectors using image moments, color moments, or a Convolutional Neural Network and use them for a hierarchical clustering of the maps. We show that image similarity as found by our clustering corresponds to functional similarity of mapped proteins by comparing our results to the BRENDA database, which provides a hierarchical function-based annotation of enzymes. We also compare our results to the TM-score, which is a similarity value for pairs of arbitrary proteins. Our visualization prototype supports the entire workflow from map generation, similarity computing to clustering and can be used to interactively explore and analyze the results. CCS Concepts • Human-centered computing → Dendrograms; Scientific visualization; • Applied computing → Bioinformatics; © 2020 The Author(s) Eurographics Proceedings © 2020 The Eurographics Association. DOI: 10.2312/vcbm.20201177 https://diglib.eg.org https://www.eg.org K. Schatz, F. Frieß, M. Schäfer, T. Ertl, and M. Krone / Analyzing Protein Similarity by Clustering Molecular Surface Maps