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Accurate modeling of the solvent environment for biological molecules is crucial for computational biology and drug design. A popular approach to achieve long simulation time scales for large system sizes is to incorporate the effect of the solvent in a mean-field fashion with implicit solvent models. However, a challenge with existing implicit solvent models is that they often lack accuracy or certain physical properties compared to explicit solvent models as the many-body effects of the neglected solvent molecules are difficult to model as a mean field. Here, we leverage machine learning (ML) and multi-scale coarse graining (CG) in order to learn implicit solvent models that can approximate the energetic and thermodynamic properties of a given explicit solvent model with arbitrary accuracy, given enough training data. Following the previous ML–CG models CGnet and CGSchnet, we introduce ISSNet, a graph neural network, to model the implicit solvent potential of mean force. ISSNet can learn from explicit solvent simulation data and be readily applied to molecular dynamics simulations. We compare the solute conformational distributions under different solvation treatments for two peptide systems. The results indicate that ISSNet models can outperform widely used generalized Born and surface area models in reproducing the thermodynamics of small protein systems with respect to explicit solvent. The success of this novel method demonstrates the potential benefit of applying machine learning methods in accurate modeling of solvent effects for in silico research and biomedical applications. [ABSTRACT FROM AUTHOR]

Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at an atomic resolution. However, a coarse graining model must be formulated such that the conclusions we draw from it are consistent with the conclusions we would draw from a model at a finer level of detail. It has been proved that a force matching scheme defines a thermodynamically consistent coarse-grained model for an atomistic system in the variational limit. Wang et al. [ACS Cent. Sci. 5, 755 (2019)] demonstrated that the existence of such a variational limit enables the use of a supervised machine learning framework to generate a coarse-grained force field, which can then be used for simulation in the coarse-grained space. Their framework, however, requires the manual input of molecular features to machine learn the force field. In the present contribution, we build upon the advance of Wang et al. and introduce a hybrid architecture for the machine learning of coarse-grained force fields that learn their own features via a subnetwork that leverages continuous filter convolutions on a graph neural network architecture. We demonstrate that this framework succeeds at reproducing the thermodynamics for small biomolecular systems. Since the learned molecular representations are inherently transferable, the architecture presented here sets the stage for the development of machine-learned, coarse-grained force fields that are transferable across molecular systems. [ABSTRACT FROM AUTHOR]

Permeation of many small molecules through lipid bilayers can be directly observed in molecular dynamics simulations on the nano- and microsecond timescale. While unbiased simulations provide an unobstructed view of the permeation process, their feasibility for computing permeability coefficients depends on various factors that differ for each permeant. The present work studies three small molecules for which unbiased simulations of permeation are feasible within less than a microsecond, one hydrophobic (oxygen), one hydrophilic (water), and one amphiphilic (ethanol). Permeabilities are computed using two approaches: counting methods and a maximum-likelihood estimation for the inhomogeneous solubility diffusion (ISD) model. Counting methods yield nearly model-free estimates of the permeability for all three permeants. While the ISD-based approach is reasonable for oxygen, it lacks precision for water due to insufficient sampling and results in misleading estimates for ethanol due to invalid model assumptions. It is also demonstrated that simulations using a Langevin thermostat with collision frequencies of 1/ps and 5/ps yield oxygen permeabilities and diffusion constants that are lower than those using Nosé–Hoover by statistically significant margins. In contrast, permeabilities from trajectories generated with Nosé–Hoover and the microcanonical ensemble do not show statistically significant differences. As molecular simulations become more affordable and accurate, calculation of permeability for an expanding range of molecules will be feasible using unbiased simulations. The present work summarizes theoretical underpinnings, identifies pitfalls, and develops best practices for such simulations. [ABSTRACT FROM AUTHOR]

Permeation of small molecules through membranes is a fundamental biological process, and molecular dynamics simulations have proven to be a promising tool for studying the permeability of membranes by providing a precise characterization of the free energy and diffusivity. In this study, permeation of ethanol through three different membranes of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylserine (POPS), PO-phosphatidylethanolamine (POPE), and PO-phosphatidylcholine (POPC) is studied. Permeabilities are calculated and compared with two different approaches based on Fick's first law and the inhomogeneous solubility-diffusion model. Microsecond simulation of double bilayers of these membranes provided a direct measurement of permeability by a flux-based counting method. These simulations show that a membrane of POPC has the highest permeability, followed by POPE and POPS. Due to the membrane-modulating properties of ethanol, the permeability increases as functions of concentration and saturation of the inner leaflet in a double bilayer setting, as opposed to the customary definition as a proportionality constant. This concentration dependence is confirmed by single bilayer simulations at different ethanol concentrations ranging from 1% to 18%, where permeability estimates are available from transition-based counting and the inhomogeneous solubility-diffusion model. We show that the free energy and diffusion profiles for ethanol lack accuracy at higher permeant concentrations due to non-Markovian kinetics caused by collective behavior. In contrast, the counting method provides unbiased estimates. Finally, the permeabilities obtained from single bilayer simulations are combined to represent natural gradients felt by a cellular membrane, which accurately models the non-equilibrium effects on ethanol permeability from single bilayer simulations in equilibrium. [ABSTRACT FROM AUTHOR]