The coarse-grained Martini force field is widely used in biomolecular simulations. Here, we present the refined model, Martini 3 (http://cgmartini.nl), with an improved interaction balance, new bead types, and expanded ability to include specific interactions representing, e.g. hydrogen bonding and electronic polarizability. The new model allows more accurate predictions of molecular packing and interactions in general, which is exemplified with a vast and diverse set of applications, ranging from oil/water partitioning and miscibility data to complex molecular systems, involving protein-protein and protein-lipid interactions and material science applications as ionic liquids and aedamers.

Cell membranes contain a large variety of lipid types and are crowded with proteins, endowing them with the plasticity needed to fulfill their key roles in cell functioning. The compositional complexity of cellular membranes gives rise to a heterogeneous lateral organization, which is still poorly understood. Computational models, in particular molecular dynamics simulations and related techniques, have provided important insight into the organizational principles of cell membranes over the past decades. Now, we are witnessing a transition from simulations of simpler membrane models to multicomponent systems, culminating in realistic models of an increasing variety of cell types and organelles. Here, we review the state of the art in the field of realistic membrane simulations and discuss the current limitations and challenges ahead.

In this contribution, for the first time, the molecular n-doping of a donor-acceptor (D-A) copolymer achieving 200-fold enhancement of electrical conductivity by rationally tailoring the side chains without changing its D-A backbone is successfully improved. Instead of the traditional alkyl side chains for poly{[N,N'-bis(2-octyldodecyl)-naphthalene-1,4,5,8-bis(dicarboximide)-2,6-diyl](NDI)-alt-5,5'-(2,2'-bithiophene)} (N2200), polar triethylene glycol type side chains is utilized and a high electrical conductivity of 0.17 S cm after doping with (4-(1,3-dimethyl-2,3-dihydro-1H-benzoimidazol-2-yl)phenyl)dimethylamine is achieved, which is the highest reported value for n-type D-A copolymers. Coarse-grained molecular dynamics simulations indicate that the polar side chains can significantly reduce the clustering of dopant molecules and favor the dispersion of the dopant in the host matrix as compared to the traditional alkyl side chains. Accordingly, intimate contact between the host and dopant molecules in the NDI-based copolymer with polar side chains facilitates molecular doping with increased doping efficiency and electrical conductivity. For the first time, a heterogeneous thermoelectric transport model for such a material is proposed, that is the percolation of charge carriers from conducting ordered regions through poorly conductive disordered regions, which provides pointers for further increase in the themoelectric properties of n-type D-A copolymers.

The computational and conceptual simplifications realized by coarse-grain (CG) models make them a ubiquitous tool in the current computational modeling landscape. Building block based CG models, such as the Martini model, possess the key advantage of allowing for a broad range of applications without the need to reparametrize the force field each time. However, there are certain inherent limitations to this approach, which we investigate in detail in this work. We first study the consequences of the absence of specific cross Lennard-Jones parameters between different particle sizes. We show that this lack may lead to artificially high free energy barriers in dimerization profiles. We then look at the effect of deviating too far from the standard bonded parameters, both in terms of solute partitioning behavior and solvent properties. Moreover, we show that too weak bonded force constants entail the risk of artificially inducing clustering, which has to be taken into account when designing elastic network models for proteins. These results have implications for the current use of the Martini CG model and provide clear directions for the reparametrization of the Martini model. Moreover, our findings are generally relevant for the parametrization of any other building block based force field.

The detailed understanding of the binding of small molecules to proteins is the key for the development of novel drugs or to increase the acceptance of substrates by enzymes. Nowadays, computer-aided design of protein-ligand binding is an important tool to accomplish this task. Current approaches typically rely on high-throughput docking essays or computationally expensive atomistic molecular dynamics simulations. Here, we present an approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein-ligand interactions of small drug-like molecules. Remarkably, we achieve high accuracy without the need of any a priori knowledge of binding pockets or pathways. Our approach is applied to a range of systems from the wellcharacterized T4 lysozyme over members of the GPCR family and nuclear receptors to a variety of enzymes. The presented results open the way to high-throughput screening of ligand libraries or protein mutations using the coarse-grained Martini model.

A complex cell envelope, composed of a mixture of lipid types including complex lipopolysaccharides, protects bacteria from the external environment. Clearly, the proteins embedded within the various components of the cell envelope have an intricate relationship with their local environment. Therefore, to obtain meaningful results, molecular simulations need to mimic as far as possible this chemically heterogeneous system. However, setting up such systems for computational studies is far from trivial, and consequently the vast majority of simulations of outer membrane proteins still rely on oversimplified phospholipid membrane models. This work presents an update of CHARMM-GUI Martini Maker for coarse-grained modeling and simulation of complex bacterial membranes with lipopolysaccharides. The qualities of the outer membrane systems generated by Martini Maker are validated by simulating them in bilayer, vesicle, nanodisc, and micelle environments (with and without outer membrane proteins) using the Martini force field. We expect this new feature in Martini Maker to be a useful tool for modeling large, complicated bacterial outer membrane systems in a user-friendly manner.

Background: PPARγ agonists improve insulin sensitivity but also evoke weight gain.Results: GQ-16 is a PPARγ partial agonist that blocks receptor phosphorylation by Cdk5 and improves insulin sensitivity in diabetic mice in the absence of weight gain.Conclusion: The unique binding mode of GQ-16 appears to be responsible for the compound's advantageous pharmacological profile.Significance: Similar compounds could have promise as anti-diabetic therapeutics.

Thiazolidinediones (TZDs) act through peroxisome proliferator activated receptor (PPAR) γ to increase insulin sensitivity in type 2 diabetes (T2DM), but deleterious effects of these ligands mean that selective modulators with improved clinical profiles are needed. We obtained a crystal structure of PPARγ ligand binding domain (LBD) and found that the ligand binding pocket (LBP) is occupied by bacterial medium chain fatty acids (MCFAs). We verified that MCFAs (C8–C10) bind the PPARγ LBD in vitro and showed that they are low-potency partial agonists that display assay-specific actions relative to TZDs; they act as very weak partial agonists in transfections with PPARγ LBD, stronger partial agonists with full length PPARγ and exhibit full blockade of PPARγ phosphorylation by cyclin-dependent kinase 5 (cdk5), linked to reversal of adipose tissue insulin resistance. MCFAs that bind PPARγ also antagonize TZD-dependent adipogenesis in vitro. X-ray structure B-factor analysis and molecular dynamics (MD) simulations suggest that MCFAs weakly stabilize C-terminal activation helix (H) 12 relative to TZDs and this effect is highly dependent on chain length. By contrast, MCFAs preferentially stabilize the H2-H3/β-sheet region and the helix (H) 11-H12 loop relative to TZDs and we propose that MCFA assay-specific actions are linked to their unique binding mode and suggest that it may be possible to identify selective PPARγ modulators with useful clinical profiles among natural products.