(545g) Flexible Backbone Membrane-Associated Protein Docking Improves Its Performance on Expanded Transmembrane-Protein Complex Datasets.

The oligomerization of protein macromolecules on cell surfaces plays a fundamental role in regulating cellular function, including signal transduction and the immune response. Despite their importance, membrane proteins (MPs) represent only 2% of all protein structures in the protein data bank (PDB), and their complexes are even scarcer. Computational methods are promising tools for modeling MP interfaces and predicting complex structures. Here, we present RosettaMPDock, a flexible transmembrane protein docking protocol that captures binding induced conformational changes. To generate diversity in backbone conformations for the RosettaMPDock, we used three conformer generation methods: perturbation of the backbones along the normal modes by 1 Å, refinement using the Relax protocol, backbone flexing using the Rosetta Backrub protocol. RosettaMPDock samples large conformational ensembles of flexible monomers and docks together protein targets within an implicit membrane environment. To improve the scoring efficiency, we have used a combination of low-resolution Motif Dock Score and membrane based high-resolution score Franklin2023. RosettaMPDock is benchmarked on 30 transmembrane-protein complexes of variable flexibility dataset. Our results show RosettaMPDock successfully predicts the correct interface (success defined as achieving 3 near-native structures in the 5 top-ranked ones) for 67% of moderately flexible targets (unbound-bound backbone motion within 1.5-2.5Å) and 60% of the highly flexible targets (unbound-bound backbone motion greater than 2.5Å), a substantial improvement from the existing membrane protein docking methods. We have also developed a hybrid protocol that refines AlphaFold-multimer structures with RosettaMPDock and further improves prediction success rates from 64% to 73%.