TY  - JOUR
AU  - Tordai, Hedvig
AU  - Suhajda, Erzébet
AU  - Sillitoe, Ian
AU  - Nair, Sreenath
AU  - Varadi, Mihaly
AU  - Hegedűs, Tamás
TI  - Comprehensive Collection and Prediction of ABC Transmembrane Protein Structures in the AI Era of Structural Biology
JF  - INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
J2  - INT J MOL SCI
VL  - 23
PY  - 2022
IS  - 16
PG  - 16
SN  - 1661-6596
DO  - 10.3390/ijms23168877
UR  - https://m2.mtmt.hu/api/publication/33084278
ID  - 33084278
AB  - 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.
LA  - English
DB  - MTMT
ER  -