Lipid membrane permeation of drug molecules was investigated with Heterogeneous Dielectric Generalized Born (HDGB) based models using solubility-diffusion theory and machine learning. Free energy profiles were obtained for neutral molecules by the standard HDGB and Dynamic HDGB (DHDGB) to account for the membrane deformation upon insertion of drugs. We also obtained hybrid free energy profiles where the neutralization of charged molecules was taken into account upon membrane insertion. The evaluation of the predictions was done against experimental permeability coefficients from Parallel Artificial Membrane Permeability Assays (PAMPA) and effects of partial charge sets, CGenFF, AM1-BCC and OPLS, on the performance of the predictions were discussed. (D)HDGB based models improved the predictions over the twostate implicit membrane models and partial charge sets seemed to have a strong impact on the predictions. Machine learning increased the accuracy of the predictions although it could not outperform physics-based approach in terms of correlations.

Dynamic nuclear polarization (DNP)-enhanced solid-state NMR spectroscopy has been shown to hold great potential for functional studies of membrane proteins at low temperatures due to its great sensitivity improvement. There are, however, numerous applications for which experiments at ambient temperature are desirable and which would also benefit from DNP signal enhancement. Here, we demonstrate as a proof of concept that a significant signal increase for lipid bilayers under room-temperature conditions can be achieved by utilizing the Overhauser effect. Experiments were carried out on aligned bilayers at 400 MHz/263 GHz using a stripline structure combined with a Fabry-Perot microwave resonator. A signal enhancement of protons of up to -10 was observed. Our results demonstrate that Overhauser DNP at high field provides efficient polarization transfer within insoluble samples, which is driven by fast local molecular fluctuations. Furthermore, our experimental setup offers an attractive option for DNP-enhanced solid-state NMR on ordered membranes and provides a general perspective toward DNP at ambient temperatures.

Phase separation processes are increasingly being recognized as important organizing mechanisms of biological macromolecules in cellular environments. Well established drivers of phase separation are multi-valency and intrinsic disorder. Here, we show that globular macromolecules may condense simply based on electrostatic complementarity. More specifically, phase separation of mixtures between RNA and positively charged proteins is described from a combination of multiscale computer simulations with microscopy and spectroscopy experiments. Phase diagrams were mapped out as a function of molecular concentrations in experiment and as a function of molecular size and temperature via simulations. The resulting condensates were found to retain at least some degree of internal dynamics varying as a function of the molecular composition. The results suggest a more general principle for phase separation that is based primarily on electrostatic complementarity without invoking polymer properties as in most previous studies. Simulation results furthermore suggest that such phase separation may occur widely in heterogenous cellular environment between nucleic acid and protein components.

Phase separation of globular RNA and positively charged proteins is reported from a combination of coarse-grained simulations parametrized based on atomistic simulations, theory informed by the coarse-grained simulations, and experimental validation via confocal microscopy and FRET spectroscopy. Phase separation is found to depend on concentration, size, and charge of the proteins, requiring a minimum protein size, minimum protein charge, and minimum protein concentration before condensates can form. The general principle for phase separation is based on electrostatic complementarity rather than invoking polymer character as in most previous studies. Simulation results furthermore suggest that such phase separation may occur in heterogenous cellular environment, not just between tRNA and cellular proteins but also between ribosomes and proteins where there may be competition for positively charged proteins. STATEMENT OF SIGNIFICANCELiquid-liquid phase separation has been recognized as a key mechanism for forming membraneless organelles in cells. Commonly discussed mechanisms invoke a role of disordered peptides and specific multi-valent interactions. We report here phase separation of RNA and proteins based on a more universal principle of charge complementarity that does not require disorder or specific interactions. The findings are supported by coarse-grained simulations, theory, and experimental validation via microscopy and FRET spectroscopy. The implications of this work are that condensate formation may be an even more universal phenomenon in biological systems than thought to date.

A scoring protocol based on implicit membrane-based scoring functions and a new protocol for optimizing the positioning of proteins inside the membrane was evaluated for its capacity to discriminate native-like states from misfolded decoys. A decoy set previously established by the Baker lab (Proteins (2006), 62, 1010–1025) was used along with a second set that was generated to cover higher resolution models. The Implicit Membrane Model 1 (IMM1), IMM1 model with CHARMM 36 parameters (IMM1-p36), generalized Born with simple switching (GBSW), heterogeneous dielectric generalized Born version 2 (HDGBv2) and 3 (HDGBv3) were tested along with the new HDGB van der Waals (HDGBvdW) model that adds implicit van der Waals contributions to the solvation free energy. For comparison, scores were also calculated with the distance-scaled finite ideal-gas reference (DFIRE) scoring function. Z-scores for native state discrimination, energy vs. root mean square deviation (RMSD) correlations, and the ability to select the most native-like structures as top-scoring decoys were evaluated to assess the performance of the scoring functions. Ranking of the decoys in the Baker set that were relatively far from the native state was challenging and dominated largely by packing interactions that was captured best by DFIRE with less benefit of the implicit membrane-based models. Accounting for the membrane environment was much more important in the second decoy set where especially the HDGB-based scoring functions performed very well in ranking decoys and providing significant correlations between scores and RMSD that show promise for improving membrane protein structure prediction and refinement applications. The new membrane structure scoring protocol was implemented in the MEMScore web server (http://feiglab.org/memscore).