The ever increasing computer power, together with the improved accuracy of atomistic force fields, enables researchers to investigate biological systems at the molecular level with remarkable detail. However, the relevant length and time scales of many processes of interest are still hardly within reach even for state-of-the-art hardware, thus leaving important questions often unanswered. The computer-aided investigation of many biological physics problems thus largely benefits from the usage of coarse-grained models, that is, simplified representations of a molecule at a level of resolution that is lower than atomistic. A plethora of coarse-grained models have been developed, which differ most notably in their granularity; this latter aspect determines one of the crucial open issues in the field, i.e. the identification of an optimal degree of coarsening, which enables the greatest simplification at the expenses of the smallest information loss. In this review, we present the problem of coarse-grained modeling in biophysics from the viewpoint of system representation and information content. In particular, we discuss two distinct yet complementary aspects of protein modeling: on the one hand, the relationship between the resolution of a model and its capacity of accurately reproducing the properties of interest; on the other hand, the possibility of employing a lower resolution description of a detailed model to extract simple, useful, and intelligible information from the latter.

The recently proposed Hamiltonian adaptive resolution scheme (H-AdResS) allows the performance of molecular simulations in an open boundary framework. It allows changing, on the fly, the resolution of specific subsets of molecules (usually the solvent), which are free to diffuse between the atomistic region and the coarse-grained reservoir. So far, the method has been successfully applied to pure liquids. Coupling the H-AdResS methodology to hybrid models of proteins, such as the molecular mechanics/coarse-grained (MM/CG) scheme, is a promising approach for rigorous calculations of ligand binding free energies in low-resolution protein models. Toward this goal, here we apply for the first time H-AdResS to two atomistic proteins in dual-resolution solvent, proving its ability to reproduce structural and dynamic properties of both the proteins and the solvent, as obtained from atomistic simulations.

G-protein-coupled receptors (GPCRs) constitute as much as 30% of the overall proteins targeted by FDA-approved drugs. However, paucity of structural experimental information and low sequence identity between members of the family impair the reliability of traditional docking approaches and atomistic molecular dynamics simulations for in silico pharmacological applications. We present here a dual-resolution approach tailored for such low-resolution models. It couples a hybrid Molecular Mechanics/Coarse-Grained (MM/CG) scheme, previously developed by us for GPCR/ligand complexes, with a Hamiltonian-based Adaptive Resolution Scheme (H-AdResS) for the solvent. This dual-resolution approach removes potentially inaccurate atomistic details from the model, while building a rigorous statistical ensemble - the grand canonical one - in the high-resolution region. We validate the method on a well-studied GPCR/ligand complex, for which the 3D-structure is known, against atomistic simulations. This implementation paves the way for future accurate in silico studies of low-resolution ligand/GPCRs models.

In recent years, a few multiple-resolution modeling strategies have been proposed, in which functionally relevant parts of a biomolecule are described with atomistic resolution, with the remainder of the system being concurrently treated using a coarse-grained model. In most cases, the parametrization of the latter requires lengthy reference all-atom simulations and/or the usage of off-shelf coarse-grained force fields, whose interactions have to be refined to fit the specific system under examination. Here, we overcome these limitations through a novel multiresolution modeling scheme for proteins, dubbed coarse-grained anisotropic network model for variable resolution simulations, or CANVAS. This scheme enables a user-defined modulation of the resolution level throughout the system structure; a fast parametrization of the potential without the necessity of reference simulations; and the straightforward usage of the model on the most commonly used molecular dynamics platforms. The method is presented and validated with two case studies, the enzyme adenylate kinase and the therapeutic antibody pembrolizumab, by comparing the results obtained with the CANVAS model against fully atomistic simulations. The modeling software, implemented in Python, is made freely available for the community on a collaborative github repository.

Methicillin-resistant Staphylococcus aureus is among those pathogens currently posing the highest threat to public health. Its host immune evasion strategy is mediated by pore-forming toxins (PFTs), among which the bicomponent gamma-hemolysin is one of the most common. The complexity of the porogenesis mechanism by gamma-hemolysin poses difficulties in the development of antivirulence therapies targeting PFTs from S. aureus, and sparse and apparently contrasting experimental data have been produced. Here, through a large set of molecular dynamics simulations at different levels of resolution, we investigate the first step of pore formation, and in particular the effect of membrane composition on the ability of gamma-hemolysin components, LukF and Hlg2, to steadily adhere to the lipid bilayer in the absence of proteinaceous receptors. Our simulations are in agreement with experimental data of gamma-hemolysin pore formation on model membranes, which are here explained on the basis of the bilayer properties. Our computational investigation suggests a possible rationale to explain experimental data on phospholipid binding to the LukF component, and to hypothesise a mechanism by which, on purely lipidic bilayers, the stable anchoring of LukF to the cell surface facilitates Hlg2 binding, through the exposure of its N-terminal region. We expect that further insights on the mechanism of transition between soluble and membrane bound-forms and on the role played by the lipid molecules will contribute to the design of antivirulence agents with enhanced efficacy against methicillin-resistant S. aureus infections.