Automated Parametrization of the Coarse-Grained Martini Force Field for Small Organic Molecules

The systematic exploration of chemical compound space holds many promises toward structure–function relationships and material design. In the context of computer simulations, progress is hampered by both the sheer number of compounds and the efforts associated with parametrizing a force field for every new molecule. A coarse-grained (CG) representation provides not only a reduced phase space but also a smaller number of compounds, due to the redundancy of CG representations mapping to the same structure. Though many CG models require the explicit force-field parametrization of a molecule with all others, others assume transferability by means of mixing rules, such as the Martini force field. To alleviate the burden associated with tedious parametrizations for each new compound, the present work aims at automating the mapping and parametrization of common small organic molecules for Martini. We test the method by analyzing the water/octanol partitioning of more than 650 neutral molecules, the hydration free energy of 354 others, and the free energies of hydration and solvation in octanol of another 69 compounds. Last, we compare with all-atom simulations the thermodynamics of insertion of four individual solute molecules in a phospholipid membrane. The protocol demonstrates the feasibility of an automated parametrization scheme for Martini and provides prospects for high-throughput simulation methodologies

Entanglement-Stabilized Nanoporous Polymer Films Made by Mechanical Deformation

We present a new simulation-guided process to create nanoporous materials, which does not require specific chemical treatment and solely relies on mechanical deformation of pure highly entangled homopolymer films. Starting from fully equilibrated freestanding thick polymer melt films, we apply a simple “biaxial expansion” deformation. Upon expansion holes form, which are prevented from growing and coalescing beyond a characteristic size due to the entanglement structure of the melt. We investigate the local morphology, the void formation upon expansion, and their stabilization. The dependence of the average void (pore) size and void fraction (porosity) on the total strain and subsequent relaxation is investigated. Furthermore, the stabilization of the porous structure of the thin expanded films through cooling below the glass transition temperature Tg is discussed

Coil–Globule–Coil Transition of PNIPAm in Aqueous Methanol: Coupling All-Atom Simulations to Semi-Grand Canonical Coarse-Grained Reservoir

Conformational transitions of (bio)­macromolecules in aqueous mixtures are intimately linked to local concentration fluctuations of different solvent components. Though computer simulations are ideally suited to investigate such phenomena, in conventional setups the excess of one cosolvent close to the solute leads to depletion elsewhere, requiring very large simulation domains to avoid system size effects. We, here, propose an approach to overcome this depletion effect, which combines the adaptive resolution scheme (AdResS) with a Metropolis particle exchange criterion. In AdResS, a small all-atom region, containing the solute, is coupled to a coarse-grained reservoir, where the particle exchange is performed. The particle exchange would be almost impossible had they been performed in an all-atom setup of a dense molecular liquid. As a first application of the method, we study the concentration driven reentrant collapse and swelling transition of poly­(<i>N</i>-isopropylacrylamide) (PNIPAm) in aqueous methanol and demonstrate the role of the delicate interplay of the different intermolecular interactions

Primitive Path Analysis and Stress Distribution in Highly Strained Macromolecules

Polymer material properties are strongly affected by entanglement effects. For long polymer chains and composite materials, they are expected to be at the origin of many technically important phenomena, such as shear thinning or the Mullins effect, which microscopically can be related to topological constraints between chains. Starting from fully equilibrated highly entangled polymer melts, we investigate the effect of isochoric elongation on the entanglement structure and force distribution of such systems. Theoretically, the related viscoelastic response usually is discussed in terms of the tube model. We relate stress relaxation in the linear and nonlinear viscoelastic regimes to a primitive path analysis (PPA) and show that tension forces both along the original paths and along primitive paths, that is, the backbone of the tube, in the stretching direction correspond to each other. Unlike homogeneous relaxation along the chain contour, the PPA reveals a so far not observed long-lived clustering of topological constraints along the chains in the deformed state

Nuclear Quantum Effects in Water: A Multiscale Study

We outline a method to investigate the role of nuclear quantum effects in liquid water making use of a force field derived from ab initio simulations. Starting from a first-principles molecular dynamics simulation, we obtain an effective force field for bulk liquid water using the force-matching technique. After validating that our effective model reproduces the key structural and dynamic properties of the reference system, we use it to perform path integral simulations to investigate the role played by nuclear quantum effects on bulk water, probing radial distribution functions, vibrational spectra, and hydrogen bond fluctuations. Our approach offers a practical route to derive ab initio quality molecular models to study quantum effects at a low computational cost

Spatially Resolved Thermodynamic Integration: An Efficient Method To Compute Chemical Potentials of Dense Fluids

Many popular methods for the calculation of chemical potentials rely on the insertion of test particles into the target system. In the case of liquids and liquid mixtures, this procedure increases in difficulty upon increasing density or concentration, and the use of sophisticated enhanced sampling techniques becomes inevitable. In this work, we propose an alternative strategy, spatially resolved thermodynamic integration, or SPARTIAN for short. Here, molecules are described with atomistic resolution in a simulation subregion and as ideal gas particles in a larger reservoir. All molecules are free to diffuse between subdomains adapting their resolution on the fly. To enforce a uniform density profile across the simulation box, a single-molecule external potential is computed, applied, and identified with the difference in chemical potential between the two resolutions. Since the reservoir is represented as an ideal gas bath, this difference exactly amounts to the excess chemical potential of the target system. The present approach surpasses the high density/concentration limitation of particle insertion methods because the ideal gas molecules entering the target system region spontaneously adapt to the local environment. The ideal gas representation contributes negligibly to the computational cost of the simulation, thus allowing one to make use of large reservoirs at minimal expenses. The method has been validated by computing excess chemical potentials for pure Lennard-Jones liquids and mixtures, SPC and SPC/E liquid water, and aqueous solutions of sodium chloride. The reported results well reproduce literature data for these systems

Derivation of Coarse Grained Models for Multiscale Simulation of Liquid Crystalline Phase Transitions

We present a systematic derivation of a coarse grained (CG) model for molecular dynamics (MD) simulations of a liquid crystalline (LC) compound containing an azobenzene mesogen. The model aims at a later use in a multiscale modeling approach to study liquid crystalline phase transitions that are (photo)­induced by the trans/cis photoisomerization of the mesogen. One of the major challenges in the coarse graining process is the development of models that are for a given chemical system structurally consistent with for example an all-atom reference model and reproduce relevant thermodynamic properties such as the LC phase behavior around the state point of interest. The reduction of number of degrees of freedom makes the resulting coarse models by construction state point dependent; that is, they cannot easily be transferred to a range of temperatures, densities, system compositions, etc. These are significant challenges, in particular if one wants to study LC phase transitions (thermally or photoinduced). In the present paper we show how one can systematically derive a CG model for a LC molecule that is highly consistent with an atomistic description by choosing an appropriate state point for the reference simulation. The reference state point is the supercooled liquid just below the smectic-isotropic phase transition which is characterized by a high degree of local nematic order while being overall isotropic. With the resulting CG model it is possible to switch between the atomistic and the CG levels (and vice versa) in a seamless manner maintaining values of all the relevant order parameters which describe the smectic A (smA) state. This model will allow us in the future to link large length scale and long time scale CG simulations of the LC state with chemically accurate QM/MM simulations of the photoisomerization process

Multiscale Simulations of Self-Assembling Peptides: Surface and Core Hydrophobicity Determine Fibril Stability and Amyloid Aggregation

Assemblies of peptides and proteins through specific intermolecular interactions set the basis for macroscopic materials found in nature. Peptides provide easily tunable hydrogen-bonding interactions, which can lead to the formation of ordered structures such as highly stable β-sheets that can form amyloid-like supramolecular peptide nanofibrils (PNFs). PNFs are of special interest, as they could be considered as mimics of various fibrillar structures found in nature. In their ability to serve as supramolecular scaffolds, they could mimic certain features of the extracellular matrix to provide stability, interact with pathogens such as virions, and transduce signals between the outside and inside of cells. Many PNFs have been reported that reveal rich bioactivities. PNFs supporting neuronal cell growth or lentiviral gene transduction have been studied systematically, and their material properties were correlated to bioactivities. However, the impact of the structure of PNFs, their dynamics, and stabilities on their unique functions is still elusive. Herein, we provide a microscopic view of the self-assembled PNFs to unravel how the amino acid sequence of self-assembling peptides affects their secondary structure and dynamic properties of the peptides within supramolecular fibrils. Based on sequence truncation, amino acid substitution, and sequence reordering, we demonstrate that peptide–peptide aggregation propensity is critical to form bioactive β-sheet-rich structures. In contrast to previous studies, a very high peptide aggregation propensity reduces bioactivity due to intermolecular misalignment and instabilities that emerge when fibrils are in close proximity to other fibrils in solution. Our multiscale simulation approach correlates changes in biological activity back to single amino acid modifications. Understanding these relationships could lead to future material discoveries where the molecular sequence predictably determines the macroscopic properties and biological activity. In addition, our studies may provide new insights into naturally occurring amyloid fibrils in neurodegenerative diseases

Multiscale Simulations of Self-Assembling Peptides: Surface and Core Hydrophobicity Determine Fibril Stability and Amyloid Aggregation

Assemblies of peptides and proteins through specific intermolecular interactions set the basis for macroscopic materials found in nature. Peptides provide easily tunable hydrogen-bonding interactions, which can lead to the formation of ordered structures such as highly stable β-sheets that can form amyloid-like supramolecular peptide nanofibrils (PNFs). PNFs are of special interest, as they could be considered as mimics of various fibrillar structures found in nature. In their ability to serve as supramolecular scaffolds, they could mimic certain features of the extracellular matrix to provide stability, interact with pathogens such as virions, and transduce signals between the outside and inside of cells. Many PNFs have been reported that reveal rich bioactivities. PNFs supporting neuronal cell growth or lentiviral gene transduction have been studied systematically, and their material properties were correlated to bioactivities. However, the impact of the structure of PNFs, their dynamics, and stabilities on their unique functions is still elusive. Herein, we provide a microscopic view of the self-assembled PNFs to unravel how the amino acid sequence of self-assembling peptides affects their secondary structure and dynamic properties of the peptides within supramolecular fibrils. Based on sequence truncation, amino acid substitution, and sequence reordering, we demonstrate that peptide–peptide aggregation propensity is critical to form bioactive β-sheet-rich structures. In contrast to previous studies, a very high peptide aggregation propensity reduces bioactivity due to intermolecular misalignment and instabilities that emerge when fibrils are in close proximity to other fibrils in solution. Our multiscale simulation approach correlates changes in biological activity back to single amino acid modifications. Understanding these relationships could lead to future material discoveries where the molecular sequence predictably determines the macroscopic properties and biological activity. In addition, our studies may provide new insights into naturally occurring amyloid fibrils in neurodegenerative diseases

Multiscale Simulations of Self-Assembling Peptides: Surface and Core Hydrophobicity Determine Fibril Stability and Amyloid Aggregation

Assemblies of peptides and proteins through specific intermolecular interactions set the basis for macroscopic materials found in nature. Peptides provide easily tunable hydrogen-bonding interactions, which can lead to the formation of ordered structures such as highly stable β-sheets that can form amyloid-like supramolecular peptide nanofibrils (PNFs). PNFs are of special interest, as they could be considered as mimics of various fibrillar structures found in nature. In their ability to serve as supramolecular scaffolds, they could mimic certain features of the extracellular matrix to provide stability, interact with pathogens such as virions, and transduce signals between the outside and inside of cells. Many PNFs have been reported that reveal rich bioactivities. PNFs supporting neuronal cell growth or lentiviral gene transduction have been studied systematically, and their material properties were correlated to bioactivities. However, the impact of the structure of PNFs, their dynamics, and stabilities on their unique functions is still elusive. Herein, we provide a microscopic view of the self-assembled PNFs to unravel how the amino acid sequence of self-assembling peptides affects their secondary structure and dynamic properties of the peptides within supramolecular fibrils. Based on sequence truncation, amino acid substitution, and sequence reordering, we demonstrate that peptide–peptide aggregation propensity is critical to form bioactive β-sheet-rich structures. In contrast to previous studies, a very high peptide aggregation propensity reduces bioactivity due to intermolecular misalignment and instabilities that emerge when fibrils are in close proximity to other fibrils in solution. Our multiscale simulation approach correlates changes in biological activity back to single amino acid modifications. Understanding these relationships could lead to future material discoveries where the molecular sequence predictably determines the macroscopic properties and biological activity. In addition, our studies may provide new insights into naturally occurring amyloid fibrils in neurodegenerative diseases