Erzsébet Suhajda, Tamás Hegedűs (2022.08.01 - 2022.10.31)

Semmelweis University

Publication: Comprehensive Collection and Prediction of ABC Transmembrane Protein Structures in the AI Era of Structural Biology
Poster

Grant: NKFIH 2020-2.1.1-ED-2021-00179

Abstract: Membrane proteins play crucial roles in cells’ life. They can bind substrates, transport ions, molecules or even drugs in and out of the cell. Their structural role is also important, as they can anchor cells to each other or to different surfaces.

In addition to their function, their structure is also unique. Membrane proteins span the cell membrane, part of their structure is located in the hydrophobic interior of the membrane, forming a transmembrane domain, and other parts are located inside the cell (intracellular) or outside the cell (extracellular). Thus, experimental determination of the structure of transmembrane proteins is a difficult task. For crystallization, membrane proteins must be removed from the membrane bilayer, therefore their native structure is often destroyed, making experimental procedures lengthy, expensive and uncertain.

Therefore, despite their vital role, only about 5% of experimentally resolved protein structures belong to membrane proteins, whereas about 50% of currently marketed drugs act through membrane proteins. Our aim is to investigate the structure and dynamics of transmembrane protein complexes with ATPase activity (e.g. ABC transporters responsible for multidrug resistance in tumor cells or calcium pumps) using both an artificial intelligence (deep learning) based structure determination method (AlphaFold) and molecular dynamics (MD) simulations. Our results will make a contribution to the performance testing of these modelling methods on membrane proteins, to the better understanding of the structures of biologically relevant complexes, and therefore can serve as a basis for future drug developments.