The number of unique transmembrane (TM) protein structures doubled in the last four
years, which can be attributed to the revolution of cryo-electron microscopy. In addition,
AlphaFold2 (AF2) also provided a large number of predicted structures with high quality.
However, if a specific protein family is the subject of a study, collecting the structures
of the family members is highly challenging in spite of existing general and protein
domain-specific databases. Here, we demonstrate this and assess the applicability
and usability of automatic collection and presentation of protein structures via the
ABC protein superfamily. Our pipeline identifies and classifies transmembrane ABC
protein structures using the PFAM search and also aims to determine their conformational
states based on special geometric measures, conftors. Since the AlphaFold database
contains structure predictions only for single polypeptide chains, we performed AF2-Multimer
predictions for human ABC half transporters functioning as dimers. Our AF2 predictions
warn of possibly ambiguous interpretation of some biochemical data regarding interaction
partners and call for further experiments and experimental structure determination.
We made our predicted ABC protein structures available through a web application,
and we joined the 3D-Beacons Network to reach the broader scientific community through
platforms such as PDBe-KB.