(629j) Flexible Membrane Protein Docking and Design with Biologically Realistic Implicit Membrane Models.

Membrane proteins participate in several life processes including transport, signaling and catalysis. They constitute over 30% of all proteins and are targets for over 60% of the FDA approved drug targets. Despite their importance, difficulties in overexpression and purification have limited the quality and quantity of experimental data, thus computational tools can help expedite the discovery. However, a critical limitation in capturing the complex environment results in either a too computationally expensive model or a severely simplified bilayer representation.

In this work, we will introduce an implicit approach that captures realistic features of the membrane such as the anisotropic density of water in the membrane, electrostatics due to the lipid layer and nanoscale dimensions of membranes with different lipid compositions. Due to the implicit nature of the model, our platform can tackle problems such as docking of larger proteins and rationally design a membrane protein given a backbone. Using two case studies, one on flexible docking of membrane proteins and another on designing beta barrel proteins, we will discuss improvements of this model and compare its performance with previous membrane models. Our method advances high-resolution membrane protein structure prediction and design toward tackling key biological questions and engineering challenges.