Advancing membrane-associated protein docking with improved sampling and scoring in Rosetta.

The oligomerization of protein macromolecules on cell membranes plays a fundamental role in regulating cellular function. From modulating signal transduction to directing immune response, membrane proteins (MPs) play a crucial role in biological processes and are often the target of many pharmaceutical drugs. Despite their biological relevance, the challenges in experimental determination have hampered the structural availability of membrane proteins and their complexes. Computational docking provides a promising alternative to model membrane protein complex structures. Here, we present Rosetta-MPDock, a flexible transmembrane (TM) protein docking protocol that captures binding-induced conformational changes. Rosetta-MPDock samples large conformational ensembles of flexible monomers and docks them within an implicit membrane environment. We benchmarked this method on 29 TM-protein complexes of variable backbone flexibility. These complexes are classified based on the root-mean-square deviation between the unbound and bound states (RMSDUB) as: rigid (RMSDUB <1.2 Å), moderately-flexible (RMSDUB ∈ [1.2, 2.2) Å), and flexible targets (RMSDUB > 2.2 Å). In a local docking scenario, i.e. with membrane protein partners starting ≈10 Å apart embedded in the membrane in their unbound conformations, Rosetta-MPDock successfully predicts the correct interface (success defined as achieving 3 near-native structures in the 5 top-ranked models) for 67% moderately flexible targets and 60% of the highly flexible targets, a substantial improvement from the existing membrane protein docking methods. Further, by integrating AlphaFold2-multimer for structure determination and using Rosetta-MPDock for docking and refinement, we demonstrate improved success rates over the benchmark targets from 64% to 73%. Rosetta-MPDock advances the capabilities for membrane protein complex structure prediction and modeling to tackle key biological questions and elucidate functional mechanisms in the membrane environment. The benchmark set and the code is available for public use at github.com/Graylab/MPDock.

Framework for in vivo T cell screens.

In vivo T cell screens are a powerful tool for elucidating complex mechanisms of immunity, yet there is a lack of consensus on the screen design parameters required for robust in vivo screens: gene library size, cell transfer quantity, and number of mice. Here, we describe the Framework for In vivo T cell Screens (FITS) to provide experimental and analytical guidelines to determine optimal parameters for diverse in vivo contexts. As a proof-of-concept, we used FITS to optimize the parameters for a CD8+ T cell screen in the B16-OVA tumor model. We also included unique molecular identifiers (UMIs) in our screens to (1) improve statistical power and (2) track T cell clonal dynamics for distinct gene knockouts (KOs) across multiple tissues. These findings provide an experimental and analytical framework for performing in vivo screens in immune cells and illustrate a case study for in vivo T cell screens with UMIs.