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Assessing the permeation rate of drug-like molecules across a lipid membrane is of paramount importance for pharmaceutical applications. While the Meyer-Overton rule relates the permeability coeffi...

Unraveling the relation between the chemical structure of small drug-like compounds and their rate of passive permeation across lipid membranes is of fundamental importance for pharmaceutical applications. The elucidation of a comprehensive structure-permeability relationship expressed in terms of a few molecular descriptors is unfortunately hampered by the overwhelming number of possible compounds. In this work, we reduce a priori the size and diversity of chemical space to solve an analogous---but smoothed out---structure-property relationship problem. This is achieved by relying on a physics-based coarse-grained model that reduces the size of chemical space, enabling a comprehensive exploration of this space with greatly reduced computational cost. We perform high-throughput coarse-grained (HTCG) simulations to derive a permeability surface in terms of two simple molecular descriptors---bulk partitioning free energy and pKa. The surface is constructed by exhaustively simulating all coarse-grained compounds that are representative of small organic molecules (ranging from 30 to 160 Da) in a high-throughput scheme. We provide results for acidic, basic and zwitterionic compounds. Connecting back to the atomic resolution, the HTCG predictions for more than 500,000 compounds allow us to establish a clear connection between specific chemical groups and the resulting permeability coefficient, enabling for the first time an inverse design procedure. Our results have profound implications for drug synthesis: the predominance of commonly-employed chemical moieties narrows down the range of permeabilities., Comment: 9 pages, 4 figures (SI: 23 pages; 9 figures)

The computational resources required by atomistic simulations of biomolecular systems still limit their applicability to relatively short time and length scales, at odds with those typically characterising biological processes. By integrating out most of the microscopic degrees of freedom in favor of a description in terms of few sites interacting through effective potentials, coarse-grained (CG) models constitute a powerful instrument for broadening the class of accessible phenomena, at the same time providing accurate results [1]. Also an exact CG procedure, however, inherently comes at a price: a loss of information, quantified by an increase in entropy, arising when a system is observed through "CG glasses" [2]. Interestingly, this loss only depends on the mapping, i.e., the sites one employs to represent the system at the CG level, which are often a priori selected only based on physical intuition [3]. Several questions follow: how wide and diverse is the space of possible CG mappings of a biomolecule? Within this space, are there representations that minimise the information loss, and do these "privileged" mappings give hints on the underlying biological processes? In this work, we address these topics by first characterising the space of CG representations of a system through the definition of a distance between mappings. Subsequently, we develop a workflow enabling to estimate the increase in entropy of a protein arising from CG'ing. Finally, we show that minimising this quantity over the space of possible CG representations suggests a connection between the biological relevance of a chemical fragment composing the biomolecule and the amount of information it contains [4]. [1] R. Menichetti, A. Pelissetto and F. Randisi, J. Chem. Phys. 146, 244908 (2017).[2] J. F. Rudzinski and W. G. Noid, J. Chem. Phys. 135, 214101 (2011).[3] P. Diggins IV et al., J. Chem. Theory Comput. 15, 648 (2019).[4] M. Giulini, et al., J. Chem. Theory Comput. 16, 6795 (2020).

Abstract A mapping of a macromolecule is a prescription to construct a simplified representation of the system in which only a subset of its constituent atoms is retained. As the specific choice of the mapping affects the analysis of all-atom simulations as well as the construction of coarse-grained models, the characterisation of the mapping space has recently attracted increasing attention. We here introduce a notion of scalar product and distance between reduced representations, which allows the study of the metric and topological properties of their space in a quantitative manner. Making use of a Wang–Landau enhanced sampling algorithm, we exhaustively explore such space, and examine the qualitative features of mappings in terms of their squared norm. A one-to-one correspondence with an interacting lattice gas on a finite volume leads to the emergence of discontinuous phase transitions in mapping space, which mark the boundaries between qualitatively different reduced representations of the same molecule. Graphicabstract

The permeation of small-molecule drugs across a phospholipid membrane bears much interest both in the pharmaceutical sciences and in physical chemistry. Connecting the chemistry of the drug and the lipids to the resulting thermodynamic properties remains of immediate importance. Here we report molecular dynamics (MD) simulation trajectories using the coarse-grained (CG) Martini force field. A wide, representative coverage of chemistry is provided: across solutes—exhaustively enumerating all 105 CG dimers—and across six phospholipids. For each combination, umbrella-sampling simulations provide detailed structural information of the solute at all depths from the bilayer midplane to bulk water, allowing a precise reconstruction of the potential of mean force. Overall, the present database contains trajectories from 15,120 MD simulations. This database may serve the further identification of structure-property relationships between compound chemistry and drug permeability., Measurement(s)molecular dynamics trajectoriesTechnology Type(s)computational modeling technique • molecular dynamics simulationFactor Type(s)Small organic molecules • phospholipid Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11733504

In theoretical modeling of a physical system, a crucial step consists of the identification of those degrees of freedom that enable a synthetic yet informative representation of it. While in some cases this selection can be carried out on the basis of intuition and experience, straightforward discrimination of the important features from the negligible ones is difficult for many complex systems, most notably heteropolymers and large biomolecules. We here present a thermodynamics-based theoretical framework to gauge the effectiveness of a given simplified representation by measuring its information content. We employ this method to identify those reduced descriptions of proteins, in terms of a subset of their atoms, that retain the largest amount of information from the original model; we show that these highly informative representations share common features that are intrinsically related to the biological properties of the proteins under examination, thereby establishing a bridge between protein structure, energetics, and function.

The partitioning of small molecules in cell membranes---a key parameter for pharmaceutical applications---typically relies on experimentally-available bulk partitioning coefficients. Computer simulations provide a structural resolution of the insertion thermodynamics via the potential of mean force, but require significant sampling at the atomistic level. Here, we introduce high-throughput coarse-grained molecular dynamics simulations to screen thermodynamic properties. This application of physics based models in a large-scale study of small molecules establishes linear relationships between partitioning coefficients and key features of the potential of mean force. This allows us to predict the structure of the insertion from bulk experimental measurements for more than 400,000 compounds. The potential of mean force hereby becomes an easily accessible quantity---already recognized for its high predictability of certain properties, e.g., passive permeation. Further, we demonstrate how coarse graining helps reduce the size of chemical space, enabling a hierarchical approach to screening small molecules., 8 pages, 6 figures. Typos fixed, minor corrections

We consider a coarse-grained (CG) model with pairwise interactions, suitable to describe low-density solutions of star-branched polymers of functionality $f$. Each macromolecule is represented by a CG molecule with $(f+1)$ interaction sites, which captures the star topology. Potentials are obtained by requiring the CG model to reproduce a set of distribution functions computed in the microscopic model in the zero-density limit. Explicit results are given for $f=6,12$ and $40$. We use the CG model to compute the osmotic equation of state of the solution for concentrations $c$ such that $\Phi_p = c/c^* \lesssim 1$, where $c^*$ is the overlap concentration. We also investigate in detail the phase diagram for f=40, identifying the boundaries of the solid intermediate phase. Finally, we investigate how the polymer size changes with $c$. For $\Phi_p\lesssim 0.3$ polymers become harder as $f$ increases at fixed reduced concentration $c/c^*$. On the other hand, for $\Phi_p\gtrsim 0.3$, polymers show the opposite behavior: At fixed $\Phi_p$, the larger the value of $f$, the larger their size reduction is., Comment: 29 pages (17 pages main paper, 12 pages supplementary material), 24 figures, 8 tables. Manuscript accepted for publication in J. Chem. Phys

We critically discuss and review the general ideas behind single- and multi-site coarse-grained (CG) models as applied to macromolecular solutions in the dilute and semi-dilute regime. We first consider single-site models with zero-density and density-dependent pair potentials. We highlight advantages and limitations of each option in reproducing the thermodynamic behavior and the large-scale structure of the underlying reference model. As a case study we consider solutions of linear homopolymers in a solvent of variable quality. Secondly, we extend the discussion to multi-component systems presenting, as a test case, results for mixtures of colloids and polymers. Specifically, we found the CG model with zero-density potentials to be unable to predict fluid-fluid demixing in a reasonable range of densities for mixtures of colloids and polymers of equal size. For larger colloids, the polymer volume fractions at which phase separation occurs are largely overestimated. CG models with density-dependent potentials are somewhat less accurate than models with zero-density potentials in reproducing the thermodynamics of the system and, although they presents a phase separation, they significantly underestimate the polymer volume fractions along the binodal. Finally, we discuss a general multi-site strategy, which is thermodynamically consistent and fully transferable with the number of sites, and that allows us to overcome most of the limitations discussed for single-site models., 23 pages, 9 figures, 4 tables

We discuss the reliability of integral-equation methods based on several commonly used closure relations in determining the phase diagram of coarse-grained models of soft-matter systems characterized by mutually interacting soft and hard-core particles. Specifically, we consider a set of potentials appropriate to describe a system of hard-sphere colloids and linear homopolymers in good solvent, and investigate the behavior when the soft particles are smaller than the colloids, which is the regime of validity of the coarse-grained models. Using computer-simulation results as a benchmark, we find that the hypernetted-chain approximation provides accurate estimates of thermodynamics and structure in the colloid-gas phase in which the density of colloids is small. On the other hand, all closures considered appear to be unable to describe the behavior of the mixture in the colloid-liquid phase, as they cease to converge at polymer densities significantly smaller than those at the binodal. As a consequence, integral equations appear to be unable to predict a quantitatively correct phase diagram., Comment: 16 pages, 11 figures, 3 tables

The number of small organic molecules is overwhelmingly large--so large, that most of it remains unexplored. Computer simulations offer an appealing framework to probe many of these compounds without the need to synthesize them in the laboratory. The main hurdles preventing a high-throughput characterization of many small molecules relies on the time investment to parametrize the force field---a process that typically requires significant human intervention---and extensive sampling requirements. We address these issues by first sampling from the coarse-grained Martini model, for which we developed an automated parametrization protocol for small molecules. The resulting potential-of-mean-force (PMF) curves for the insertion of small molecules in lipid membranes show excellent agreement for a number of benchmark cases. Our framework allows us to run high-throughput molecular dynamics simulations and estimate tens of thousands of relative free energies of different small molecules in a lipid bilayer. They provide useful data to explore structure-property relationships from computer simulations.

We compare different coarse-grained models for star polymers. We find that phenomenological models inspired by the Daoud-Cotton model reproduce quite poorly the thermodynamics of these systems, even if the potential is assumed to be density dependent, as done in the analysis of experimental results. We also determine the minumum value fc of the functionality of the star polymer for which a fluid-solid transition occurs. By applying the Hansen-Verlet criterion we find 35 < fc < 40. This result is confirmed by an analysis based on the modified (reference) hypernetted chain method and is qualitatively consistent with previous work., 9 pages. In the new version, comments added and a few typos corrected. To appear in J. Chem. Phys