(382c) Protein Structure Prediction and Design in a Biologically-Realistic Implicit Membrane | AIChE

(382c) Protein Structure Prediction and Design in a Biologically-Realistic Implicit Membrane


Alford, R. F. - Presenter, Johns Hopkins University
Fleming, K. G., Johns Hopkins University
Fleming, P., Johns Hopkins University
Gray, J. J., Johns Hopkins University
Protein design is a powerful tool for elucidating biological mechanisms and engineering new therapeutics and nanotechnologies. While soluble protein design has advanced, membrane protein design remains challenging due to difficulties in modeling the lipid bilayer. In this work, we developed an implicit approach that captures both the anisotropic structure and the nanoscale dimensions of membranes with different lipid compositions. In addition, the energy function parameters were derived from experimental measurements of phospholipid bilayers. The model improves performance in computational benchmarks against experimental targets including prediction of protein orientations in the membrane, ΔΔG of mutation calculations, native structure discrimination, and native sequence recovery. When applied to de novo design, this approach generates sequences with amino acid distributions that better reflect those in native membrane proteins, overcoming a shortcoming of oversimplified membrane treatments that mostly generate leucine-rich designs. Further, the proteins designed in the new membrane model exhibit other native-like features including interfacial aromatic side chains, hydrophobic lengths compatible with bilayer thickness, and polar pores. Together, the scientific benchmarks and de novo designs demonstrate the lipid-specific membrane model emulates native bilayer properties. As a result, the implicit membrane promises to be a robust platform for high-resolution membrane protein structure prediction and design.