(505d) Implicit Model Captures Electrostatic Features of the Cell Membrane Environment.

Membrane protein structure prediction and design are challenging due to the complexity of capturing the interactions in the lipid layer, such as those arising from electrostatics. Accurately capturing electrostatic energies in the low-dielectric membrane often requires expensive Poisson-Boltzmann calculations that are not scalable for membrane protein structure prediction and design. In this work, we have developed a fast-to-compute implicit energy function that considers the realistic characteristics of different lipid bilayers, making design calculations tractable.

Implicit membrane models accelerate this complex biomolecular problem by representing the solvent and the lipid bilayer as a continuous medium. However, existing implicit models either do not consider realistic features of the membrane environment, such as the effect of the lipid head group or the variable dielectric constant, or the calculations are expensive. This method captures the impact of lipid head group using a mean-field based approach and uses a depth-dependent dielectric constant to characterize the membrane environment. This energy function Franklin2023 is built upon Franklin2019, which is based on experimentally derived hydrophobicity scales in the membrane bilayer. In addition to capturing the anisotropic shape of water-filled pores and the effect of different lipid compositions, Franklin2023 can also capture the electrostatic features in the bilayer. Until recently, the energy functions have been developed and tested for specialized tasks, questioning their generalizability. To overcome the challenge of over-fitting and specialized model, we assembled a suite of 12 tests that can probe (1) protein orientation in the bilayer, (2) stability, (3) sequence recovery, and (4) docking of membrane proteins. Relative to Franklin2019, Franklin2023 has improved the calculation of the tilt angle of membrane proteins for 90% of WALP peptides of different length, 15% of transmembrane-peptides and 25% of the adsorbed peptides. The results for Franklin2023 shows equivalent performances for stability and design tests. Additionally for flexible transmembrane protein-protein docking, relative to Franklin2019, Franklin2023 improved the N5 (number of near-native decoys among 5 top-scoring decoys) for 62% of targets. The speed and calibration of the implicit model based on various tests will help Franklin2023 access biophysical phenomena at different time and length scales and accelerate the membrane protein design pipeline.