Analyzing Protein Similarity by Clustering Molecular Surface Maps

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dc.contributor.authorSchatz, Karstenen_US
dc.contributor.authorFrieß, Florianen_US
dc.contributor.authorSchäfer, Marcoen_US
dc.contributor.authorErtl, Thomasen_US
dc.contributor.authorKrone, Michaelen_US
dc.contributor.editorKozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgiaen_US
dc.date.accessioned2020-09-28T06:11:55Z
dc.date.available2020-09-28T06:11:55Z
dc.date.issued2020
dc.description.abstractMany 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.en_US
dc.description.sectionheadersProteins
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20201177
dc.identifier.isbn978-3-03868-109-0
dc.identifier.issn2070-5786
dc.identifier.pages103-114
dc.identifier.urihttps://doi.org/10.2312/vcbm.20201177
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20201177
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman centered computing
dc.subjectDendrograms
dc.subjectScientific visualization
dc.subjectApplied computing
dc.subjectBioinformatics
dc.titleAnalyzing Protein Similarity by Clustering Molecular Surface Mapsen_US
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VCBM 2020: Eurographics Workshop on Visual Computing for Biology and Medicine