TMvisDB: resource for transmembrane protein annotation and 3D visualization

Céline Marquet(Technical University of Munich), Anastasia Grekova(Technical University of Munich), Leen Houri(Technical University of Denmark), Michael Bernhofer(Technical University of Munich), Luisa F. Jiménez‐Soto(Goethe Institut), Tim Karl(Technical University of Munich), Michael Heinzinger(Technical University of Munich), Christian Dallago(Albert Einstein College of Medicine), Burkhard Rost(Institute for Advanced Study)
bioRxiv (Cold Spring Harbor Laboratory)
December 2, 2022
Cited by 1Open Access
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Abstract

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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