SummaryThere has been exponential growth in the number of membrane protein structures determined. Nevertheless, these structures are usually resolved in the absence of their lipid environment. Coarse-grained molecular dynamics (CGMD) simulations enable insertion of membrane proteins into explicit models of lipid bilayers. We have automated the CGMD methodology, enabling membrane protein structures to be identified upon their release into the PDB and embedded into a membrane. The simulations are analyzed for protein-lipid interactions, identifying lipid binding sites, and revealing local bilayer deformations plus molecular access pathways within the membrane. The coarse-grained models of membrane protein/bilayer complexes are transformed to atomistic resolution for further analysis and simulation. Using this automated simulation pipeline, we have analyzed a number of recently determined membrane protein structures to predict their locations within a membrane, their lipid/protein interactions, and the functional implications of an enhanced understanding of the local membrane environment of each protein.

Coarse-grained molecular dynamics simulations of the E. coli outer membrane proteins FhuA, LamB, NanC, OmpA and OmpF in a POPE/POPG (3∶1) bilayer were performed to characterise the diffusive nature of each component of the membrane. At small observation times (<10 ns) particle vibrations dominate phospholipid diffusion elevating the calculated values from the longer time-scale bulk value (>50 ns) of 8.5×10−7 cm2 s−1. The phospholipid diffusion around each protein was found to vary based on distance from protein. An asymmetry in the diffusion of annular lipids in the inner and outer leaflets was observed and correlated with an asymmetry in charged residues in the vicinity of the inner and outer leaflet head-groups. Protein rotational and translational diffusion were also found to vary with observation time and were inversely correlated with the radius of gyration of the protein in the plane of the bilayer. As the concentration of protein within the bilayer was increased, the overall mobility of the membrane decreased reflected in reduced lipid diffusion coefficients for both lipid and protein components. The increase in protein concentration also resulted in a decrease in the anomalous diffusion exponent α of the lipid. Formation of extended clusters and networks of proteins led to compartmentalisation of lipids in extreme cases.

The rapid growth of structural information for G-protein-coupled receptors (GPCRs) has led to a greater understanding of their structure, function, selectivity, and ligand binding. Although novel ligands have been identified using methods such as virtual screening, computationally driven lead optimization has been possible only in isolated cases because of challenges associated with predicting binding free energies for related compounds. Here, we provide a systematic characterization of the performance of free-energy perturbation (FEP) calculations to predict relative binding free energies of congeneric ligands binding to GPCR targets using a consistent protocol and no adjustable parameters. Using the FEP+ package, first we validated the protocol, which includes a full lipid bilayer and explicit solvent, by predicting the binding affinity for a total of 45 different ligands across four different GPCRs (adenosine A2AAR, β1 adrenergic, CXCR4 chemokine, and δ opioid receptors). Comparison with experimental binding affinity measurements revealed a highly predictive ranking correlation (average spearman ρ = 0.55) and low root-mean-square error (0.80 kcal/mol). Next, we applied FEP+ in a prospective project, where we predicted the affinity of novel, potent adenosine A2A receptor (A2AR) antagonists. Four novel compounds were synthesized and tested in a radioligand displacement assay, yielding affinity values in the nanomolar range. The affinity of two out of the four novel ligands (plus three previously reported compounds) was correctly predicted (within 1 kcal/mol), including one compound with approximately a tenfold increase in affinity compared to the starting compound. Detailed analyses of the simulations underlying the predictions provided insights into the structural basis for the two cases where the affinity was overpredicted. Taken together, these results establish a protocol for systematically applying FEP+ to GPCRs and provide guidelines for identifying potent molecules in drug discovery lead optimization projects.

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Recent advances in graphics processing unit (GPU) hardware and improved efficiencies of atomistic simulation programs allow for the screening of a large number of polymers to predict properties that require running and analyzing long molecular dynamics (MD) trajectories. This paper outlines a MD simulation workflow based on GPU MD simulation and the refined optimized potentials for liquid simulation (OPLS) OPLS3e force field to calculate glass transition temperatures (T gs) of 315 polymers for which Bicerano reported experimental values [BiceranoJ. Bicerano, J. Prediction of Polymer PropertiesMarcel Dekker Inc.New York1996]. Applying the workflow across this large set of polymers allowed for a comprehensive evaluation of the protocol performance and helped in understanding its merits and limitations. We observe a consistent trend between predicted T g values and empirical observation across several subsets of polymers. Thus, the protocol established in this work is promising for exploring targeted chemical spaces and aids in the evaluation of polymers for various applications, including composites, coatings, electrical casings, etc. During the stepwise cooling simulation for the calculation of T g, a subset of polymers clearly showed an ordered structure developing as the temperature decreased. Such polymers have a point of discontinuity on the specific volume vs temperature plot, which we associate with the melting temperature (T m). We demonstrate the distinction between crystallized and amorphous polymers by examining polyethylene. Linear polyethylene shows a discontinuity in the specific volume vs temperature plot, but we do not observe the discontinuity for branched polyethylene simulations.