Pairwise-additive Force Fields Research Articles

Formation of degraded LDPE surfaces using mechanical cleavage and shock compression analyzed by means of molecular dynamics simulations

Low Density Polyethylene, LDPE, samples were subjected to analysis based on application of molecular dynamics simulations. The samples were composed of polymer molecules with molecular weight 30 000 amu and 2% of branched carbon atoms. The calculations were carried out using various force fields i.e. Amber pairwise additive force field and four types of many body reactive force fields like AIREBO, AIREBO-M, ReaxFF and ci-ReaxFF. For each force field the material properties of the samples were determined i.e.: Young's modulus, Poisson's ratio, yield strength and share modulus using the determined stress–strain curves. The samples were subjected to mechanical cleaving by applying an external forces in order to generate surfaces of LDPE. These surfaces were analyzed in terms of their chemical composition and number and types of radical ends formed on the surfaces. Additionally, the samples were subjected to shock compression in order to produce more deeper chemical transformations which should be more representative to naturally degraded nanoplastics. We conclude that the CHO ReaxFF force field is the most reliable for generating degraded LDPE surfaces which can be next used in standard molecular dynamics involving interaction of nanoplastic with proteins or lipid membranes, for instance.

Beyond single-crystal surfaces: The GAL21 water/metal force field.

Solvent effects are notoriously difficult to describe for metallic nanoparticles (NPs). Here, we introduce GAL21 which is the first pairwise additive force field that is specifically designed to modulate the near chemisorption energy of water as a function of the coordination numbers of the metallic atoms. We find a quadratic dependence to be most suitable for capturing the dependence of the adsorption energy of water on the generalized coordination number (GCN) of the metal atoms. GAL21 has been fitted against DFT adsorption energies for Cu, Ag, Au, Ni, Pd, Pt, and Co on 500 configurations and validated on about 3000 configurations for each metal, constructed on five surfaces with GCNs varying from 2.5 to 11.25. Depending on the metals, the root mean square deviation is found between 0.7kcal mol-1 (Au) to 1.6kcal mol-1 (Ni). Using GAL21, as implemented in the open-source code CP2K, we then evaluate the solvation energy of Au55 and Pt55 NPs in water using thermodynamic integration. The solvation free energy is found to be larger for Pt than for Au and systematically larger than 200kcal mol-1, demonstrating the large impact of solvent on the surface energetics of NPs. Still, given that the amorphous NPs are both, the most stable and the most solvated ones, we do not predict a change in the preferred morphology between the gas-phase and in water. Finally, based on a linear regression on three sizes of NPs (from 38 to 147), the solvation energy for Au and Pt surface atoms is found to be -5.2 and -9.9kcal mol-1, respectively.

Hydration Structure of Na+ and K+ Ions in Solution Predicted by Data-Driven Many-Body Potentials.

The hydration structure of Na+ and K+ ions in solution is systematically investigated using a hierarchy of molecular models that progressively include more accurate representations of many-body interactions. We found that a conventional empirical pairwise additive force field that is commonly used in biomolecular simulations is unable to reproduce the extended X-ray absorption fine structure (EXAFS) spectra for both ions. In contrast, progressive inclusion of many-body effects rigorously derived from the many-body expansion of the energy allows the MB-nrg potential energy functions (PEFs) to achieve nearly quantitative agreement with the experimental EXAFS spectra, thus enabling the development of a molecular-level picture of the hydration structure of both Na+ and K+ in solution. Since the MB-nrg PEFs have already been shown to accurately describe isomeric equilibria and vibrational spectra of small ion-water clusters in the gas phase, the present study demonstrates that the MB-nrg PEFs effectively represent the long-sought-after models able to correctly predict the properties of ionic aqueous systems from the gas to the liquid phase, which has so far remained elusive.

Performing Molecular Dynamics Simulations and Computing Hydration Free Energies on the B3LYP-D3(BJ) Potential Energy Surface with Adaptive Force Matching: A Benchmark Study with Seven Alcohols and One Amine.

The potential energy surfaces at the B3LYP-D3(BJ) level for eight solutes in dilute aqueous solutions were mapped into simple pairwise additive force field expressions using the adaptive force matching (AFM) method. The quality of the fits was validated by computing the hydration free energy (HFE), enthalpy of hydration, and diffusion constant for each solute. By force matching B3LYP-D3(BJ), the predictions from the models agree with the closest experimental HFE and enthalpy of hydration within chemical accuracy. The diffusion constants from the models are also in good agreement with experimental references. The good agreement provides confidence on the quality of B3LYP-D3(BJ) in producing potential energy surfaces for thermodynamic property calculations through AFM for the molecules studied. Accurate computational predictions could potentially provide validations to experimental measurements in cases where experimental measurements from different sources do not agree.

Configurational Entropy of Folded Proteins and Its Importance for Intrinsically Disordered Proteins.

Many pairwise additive force fields are in active use for intrinsically disordered proteins (IDPs) and regions (IDRs), some of which modify energetic terms to improve the description of IDPs/IDRs but are largely in disagreement with solution experiments for the disordered states. This work considers a new direction—the connection to configurational entropy—and how it might change the nature of our understanding of protein force field development to equally well encompass globular proteins, IDRs/IDPs, and disorder-to-order transitions. We have evaluated representative pairwise and many-body protein and water force fields against experimental data on representative IDPs and IDRs, a peptide that undergoes a disorder-to-order transition, for seven globular proteins ranging in size from 130 to 266 amino acids. We find that force fields with the largest statistical fluctuations consistent with the radius of gyration and universal Lindemann values for folded states simultaneously better describe IDPs and IDRs and disorder-to-order transitions. Hence, the crux of what a force field should exhibit to well describe IDRs/IDPs is not just the balance between protein and water energetics but the balance between energetic effects and configurational entropy of folded states of globular proteins.

Simple corrections for the static dielectric constant of liquid mixtures from model force fields.

Pair-wise additive force fields provide fairly accurate predictions, through classical molecular simulations, for a wide range of structural, thermodynamic, and dynamical properties of many materials. However, one key property that has not been well captured is the static dielectric constant, which characterizes the response of a system to an applied electric field and is important in determining the screening of electrostatic interactions through a system. A simple correction has been found to provide a relatively robust method to improve the estimate of the static dielectric constant from molecular simulations for a broad range of compounds. This approach accounts for the electronic contribution to molecular polarizability and assumes that the charges that couple a molecule to an applied electric field are proportional to the effective force field charges. In this work, we examine how this correction performs for systems at different temperatures and for binary mixtures. Using a value for the electronic polarizability, based on the experimental index of refraction, and a charge scaling factor, determined at a single temperature, we find that the static dielectric constant can be predicted remarkably well, in comparison to the experimentally measured values. This provides good evidence that the effective charges that appear in pair-wise additive force fields developed to reproduce the potential energy surface of a system are not the same as those that determine the static dielectric constant; however, they can be captured in a relatively simple manner, which is dependent on the particular force field.

Using Small-Angle Scattering Data and Parametric Machine Learning to Optimize Force Field Parameters for Intrinsically Disordered Proteins.

Intrinsically disordered proteins (IDPs) and proteins with intrinsically disordered regions (IDRs) play important roles in many aspects of normal cell physiology, such as signal transduction and transcription, as well as pathological states, including Alzheimer's, Parkinson's, and Huntington's disease. Unlike their globular counterparts that are defined by a few structures and free energy minima, IDP/IDR comprise a large ensemble of rapidly interconverting structures and a corresponding free energy landscape characterized by multiple minima. This aspect has precluded the use of structural biological techniques, such as X-ray crystallography and nuclear magnetic resonance (NMR) for resolving their structures. Instead, low-resolution techniques, such as small-angle X-ray or neutron scattering (SAXS/SANS), have become a mainstay in characterizing coarse features of the ensemble of structures. These are typically complemented with NMR data if possible or computational techniques, such as atomistic molecular dynamics, to further resolve the underlying ensemble of structures. However, over the past 10–15 years, it has become evident that the classical, pairwise-additive force fields that have enjoyed a high degree of success for globular proteins have been somewhat limited in modeling IDP/IDR structures that agree with experiment. There has thus been a significant effort to rehabilitate these models to obtain better agreement with experiment, typically done by optimizing parameters in a piecewise fashion. In this work, we take a different approach by optimizing a set of force field parameters simultaneously, using machine learning to adapt force field parameters to experimental SAXS scattering profiles. We demonstrate our approach in modeling three biologically IDP ensembles based on experimental SAXS profiles and show that our optimization approach significantly improve force field parameters that generate ensembles in better agreement with experiment.

Calculations of the Electric Field in Solutions and Proteins with Polarizable Force Fields

A molecule in solution or a ligand bound to a protein experiences a complex range of electrostatic interactions from its environment. One way of describing these interactions in a collective fashion is to consider the total electric field they exert on the molecule of interest. A key advantage of this picture is that it provides a unifying language for comparing the relative importance of diverse specific interactions (e.g., hydrogen bonds) and nonspecific interactions (e.g., dipole-dipole and dipole-induced dipole), and it facilitates direct comparison to experimental observables (like vibrational Stark effects). We develop and demonstrate methods to calculate electric fields using molecular dynamics simulations with the AMOEBA polarizable force field, which enables us to describe the electrostatic characteristics of non-polar, polar, and hydrogen bonding environments in a consistent fashion. This consistency is not achievable with the more widely used continuum models or pair-wise additive force fields. The connection to experiment further enables us to test predictions from simulation and to benchmark the force fields employed. By offering a high-level description of electrostatics, polarizable force fields present a physically realistic picture of the organized environments of proteins and how functional properties emerge from them.

Molecular Dynamics and Quantum Mechanics of RNA: Conformational and Chemical Change We Can Believe In

Structure and dynamics are both critical to RNA’s vital functions in biology. Numerous techniques can elucidate the structural dynamics of RNA, but computational approaches based on experimental data arguably hold the promise of providing the most detail. In this Account, we highlight areas wherein molecular dynamics (MD) and quantum mechanical (QM) techniques are applied to RNA, particularly in relation to complementary experimental studies.We have expanded on atomic-resolution crystal structures of RNAs in functionally relevant states by applying explicit solvent MD simulations to explore their dynamics and conformational changes on the submicrosecond time scale. MD relies on simplified atomistic, pairwise additive interaction potentials (force fields). Because of limited sampling, due to the finite accessible simulation time scale and the approximated force field, high-quality starting structures are required.Despite their imperfection, we find that currently available force fields empower MD to provide meaningful and predictive information on RNA dynamics around a crystallographically defined energy minimum. The performance of force fields can be estimated by precise QM calculations on small model systems. Such calculations agree reasonably well with the Cornell et al. AMBER force field, particularly for stacking and hydrogen-bonding interactions. A final verification of any force field is accomplished by simulations of complex nucleic acid structures.The performance of the Cornell et al. AMBER force field generally corresponds well with and augments experimental data, but one notable exception could be the capping loops of double-helical stems. In addition, the performance of pairwise additive force fields is obviously unsatisfactory for inclusion of divalent cations, because their interactions lead to major polarization and charge-transfer effects neglected by the force field. Neglect of polarization also limits, albeit to a lesser extent, the description accuracy of other contributions, such as interactions with monovalent ions, conformational flexibility of the anionic sugar−phosphate backbone, hydrogen bonding, and solute polarization by solvent. Still, despite limitations, MD simulations are a valid tool for analyzing the structural dynamics of existing experimental structures. Careful analysis of MD simulations can identify problematic aspects of an experimental RNA structure, unveil structural characteristics masked by experimental constraints, reveal functionally significant stochastic fluctuations, evaluate the structural role of base ionization, and predict structurally and potentially functionally important details of the solvent behavior, including the presence of tightly bound water molecules. Moreover, combining classical MD simulations with QM calculations in hybrid QM/MM approaches helps in the assessment of the plausibility of chemical mechanisms of catalytic RNAs (ribozymes).In contrast, the reliable prediction of structure from sequence information is beyond the applicability of MD tools. The ultimate utility of computational studies in understanding RNA function thus requires that the results are neither blindly accepted nor flatly rejected, but rather considered in the context of all available experimental data, with great care given to assessing limitations through the available starting structures, force field approximations, and sampling limitations. The examples given in this Account showcase how the judicious use of basic MD simulations has already served as a powerful tool to help evaluate the role of structural dynamics in biological function of RNA.