TMvisDB: resource for transmembrane protein annotation and 3D visualization

Céline Marquet, Anastasia Grekova, Leen Houri, Michael Bernhofer, Luisa F. Jimenez-Soto, Tim Karl, Michael Heinzinger, Christian Dallago, Burkhard Rost
2022 · doi:10.1101/2022.11.30.518551
preprint OA: closed CC-BY-NC-ND-4.0
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Abstract

Since the rise of cellular organisms, transmembrane proteins (TMPs) have been crucial to a variety of cellular processes due to their central role as gates and gatekeepers. Despite their importance, experimental high-resolution structures for TMPs remain underrepresented due to technical limitations. With structure prediction methods coming of age, predictions might fill some of the need. However, identifying the membrane regions and topology in three-dimensional structure files requires additional in silico prediction. Here, we introduce TMvisDB to sieve through millions of predicted structures for TMPs. This resource enables both, to browse through 46 million predicted TMPs and to visualize those along with their topological annotations. The database was created by joining AlphaFold DB structure predictions and transmembrane topology predictions from the protein language model based method TMbed. We show the utility of TMvisDB for individual proteins through two single use cases, namely the B-lymphocyte antigen CD20 ( Homo sapiens ) and the cellulose synthase ( Novosphingobium sp. P6W ). To demonstrate the value for large scale analyses, we focus on all TMPs predicted for the human proteome. TMvisDB is freely available at tmvis.predictprotein.org .

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License: CC-BY-NC-ND-4.0