TMVisDB: Annotation and 3D-visualization of Transmembrane Proteins.

Area of Science:

• Structural Biology
• Bioinformatics
• Computational Biology

Background:

• Transmembrane proteins (TMPs) are vital for cellular functions but challenging to study experimentally.
• High-resolution structures of TMPs are scarce due to experimental difficulties.
• Computational structure prediction methods are improving but require additional tools for large-scale analysis of membrane regions and topology.

Purpose of the Study:

• To introduce TMVisDB, a novel resource for browsing and visualizing millions of predicted transmembrane protein structures.
• To integrate predicted structures with topological annotations for enhanced analysis.
• To provide a cost-effective alternative to extensive in silico predictions.

Main Methods:

• TMVisDB integrates predicted transmembrane protein structures from AlphaFoldDB.
• It incorporates transmembrane topology predictions generated by the protein language model (pLM) based method, TMbed.
• The resource allows large-scale browsing and visualization of 46 million predicted TMPs.

Main Results:

• TMVisDB enables efficient exploration of a vast dataset of predicted TMP structures and their topologies.
• Demonstrated utility through case studies of B-lymphocyte antigen CD20 and cellulose synthase.
• Provided insights into predicted TMPs across the human proteome, highlighting the resource's value for large-scale analysis.

Conclusions:

• TMVisDB facilitates the study of transmembrane proteins by providing accessible predicted structures and topological information.
• The resource reduces the need for expensive, individual in silico predictions for large-scale TMP analysis.
• TMVisDB is a valuable tool for researchers in structural biology and bioinformatics studying transmembrane proteins.