Energy Landscape Analysis of Membrane Proteins Using NMR-Based Hybrid Restraint Potentials

Most biomolecular simulations depend on the quality of empirical force fields, and the use of hybrid restraint potentials has emerged as a promising approach. In this contribution, we extend the application of hybrid potentials to membrane proteins by developing optimized restraints derived from experimentally determined NMR data. NMR chemical shift, chemical shift anisotropy, dipolar coupling, and NOE distance information are combined with appropriately weighted empirical force fields to study two transmembrane systems, namely sarcolipin and phospholamban. To remedy the problems of rare events and broken ergodicity, the energy landscape framework, including basin-hopping global optimization and discrete path sampling, is employed for exploring the underlying energy landscapes. Much of the appeal of the hybrid potential approach is the ability to study membrane proteins in the absence of conventional explicit or implicit solvent and lipid molecules, thereby simplifying the sampling of complex biomolecular conformational spaces. Our results suggest that the hybridization of NMR constraints as penalty energies with empirical force fields improves global optimization and energy landscape analysis by excluding experimentally incompatible structures.