Martini Coarse-Grained Force Field:Extension to DNA

We systematically parameterized a coarsegrained (CG) model for DNA that is compatible with the Martini force field. The model maps each nucleotide into six to seven CG beads and is parameterized following the Martini philosophy. The CG nonbonded interactions are based on partitioning of the nudeobases between polar and nonpolar solvents as well as base base potential of mean force calculations. The bonded interactions are fit to single-stranded DNA (ssDNA) atomistic simulations and an elastic network is used to retain double-stranded DNA (dsDNA) and other specific DNA conformations. We present the implementation of the Martini DNA model and demonstrate the properties of individual bases, ssDNA as well as dsDNA, and DNA-protein complexes. The model opens up large-scale simulations of DNA interacting with a wide range of other (bio)molecules that are available within the Martini framework

The power of coarse graining in biomolecular simulations

Computational modeling of biological systems is challenging because of the multitude of spatial and temporal scales involved. Replacing atomistic detail with lower resolution, coarse grained (CG), beads has opened the way to simulate large-scale biomolecular processes on time scales inaccessible to all-atom models. We provide an overview of some of the more popular CG models used in biomolecular applications to date, focusing on models that retain chemical specificity. A few state-of-the-art examples of protein folding, membrane protein gating and self-assembly, DNA hybridization, and modeling of carbohydrate fibers are used to illustrate the power and diversity of current CG modeling

Synthetic Analogues of the Snail Toxin 6-Bromo-2-mercaptotryptamine Dimer (BrMT) Reveal That Lipid Bilayer Perturbation Does Not Underlie Its Modulation of Voltage-Gated Potassium Channels

Drugs do not act solely by canonical ligand–receptor binding interactions. Amphiphilic drugs partition into membranes, thereby perturbing bulk lipid bilayer properties and possibly altering the function of membrane proteins. Distinguishing membrane perturbation from more direct protein–ligand interactions is an ongoing challenge in chemical biology. Herein, we present one strategy for doing so, using dimeric 6-bromo-2-mercaptotryptamine (BrMT) and synthetic analogues. BrMT is a chemically unstable marine snail toxin that has unique effects on voltage-gated K+ channel proteins, making it an attractive medicinal chemistry lead. BrMT is amphiphilic and perturbs lipid bilayers, raising the question of whether its action against K+ channels is merely a manifestation of membrane perturbation. To determine whether medicinal chemistry approaches to improve BrMT might be viable, we synthesized BrMT and 11 analogues and determined their activities in parallel assays measuring K+ channel activity and lipid bilayer properties. Structure–activity relationships were determined for modulation of the Kv1.4 channel, bilayer partitioning, and bilayer perturbation. Neither membrane partitioning nor bilayer perturbation correlates with K+ channel modulation. We conclude that BrMT’s membrane interactions are not critical for its inhibition of Kv1.4 activation. Further, we found that alkyl or ether linkages can replace the chemically labile disulfide bond in the BrMT pharmacophore, and we identified additional regions of the scaffold that are amenable to chemical modification. Our work demonstrates a strategy for determining if drugs act by specific interactions or bilayer-dependent mechanisms, and chemically stable modulators of Kv1 channels are reported

Machine learning–driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins

RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques. We demonstrate here a multiscale simulation infrastructure that uses machine learning to create a scale-bridging ensemble of over 100,000 simulations of active wild-type KRAS on a complex, asymmetric membrane. Initialized and validated with experimental data (including a new structure of active wild-type KRAS), these simulations represent a substantial advance in the ability to characterize RAS-membrane biology. We report distinctive patterns of local lipid composition that correlate with interfacially promiscuous RAS multimerization. These lipid fingerprints are coupled to RAS dynamics, predicted to influence effector binding, and therefore may be a mechanism for regulating cell signaling cascades