The Determination of Free Energy of Hydration of Water Ions from First Principles.

We model the autoionization of water by determining the free energy of hydration of the major intermediate species of water ions. We represent the smallest ions─the hydroxide ion OH-, the hydronium ion H3O+, and the Zundel ion H5O2+─by bonded models and the more extended ionic structures by strong nonbonded interactions (e.g., the Eigen H9O4+ = H3O+ + 3(H2O) and the Stoyanov H13O6+ = H5O2+ + 4(H2O)). Our models are faithful to the precise QM energies and their components to within 1% or less. Using the calculated free energies and atomization energies, we compute the pKa of pure water from first principles as a consistency check and arrive at a value within 1.3 log units of the experimental one. From these calculations, we conclude that the hydronium ion, and its hydrated state, the Eigen cation, are the dominant species in the water autoionization process.

Combining Force Fields and Neural Networks for an Accurate Representation of Bonded Interactions.

We present a formalism of a neural network encoding bonded interactions in molecules. This intramolecular encoding is consistent with the models of intermolecular interactions previously designed by this group. Variants of the encoding fed into a corresponding neural network may be used to economically improve the representation of torsional degrees of freedom in any force field. We test the accuracy of the reproduction of the ab initio potential energy surface on a set of conformations of two dipeptides, methyl-capped ALA and ASP, in several scenarios. The encoding, either alone or in conjunction with an analytical potential, improves agreement with ab initio energies that are on par with those of other neural network-based potentials. Using the encoding and neural nets in tandem with an analytical model places the agreements firmly within "chemical accuracy" of ±0.5 kcal/mol.

Structural characterization of the antimicrobial peptides myxinidin and WMR in bacterial membrane mimetic micelles and bicelles.

Antimicrobial peptides are a promising class of potential antibiotics that interact selectively with negatively charged lipid bilayers. This paper presents the structural characterization of the antimicrobial peptides myxinidin and WMR associated with bacterial membrane mimetic micelles and bicelles by NMR, CD spectroscopy, and molecular dynamics simulations. Both peptides adopt a different conformation in the lipidic environment than in aqueous solution. The location of the peptides in micelles and bicelles has been studied by paramagnetic relaxation enhancement experiments with paramagnetic tagged 5- and 16-doxyl stearic acid (5-/16-SASL). Molecular dynamics simulations of multiple copies of the peptides were used to obtain an atomic level of detail on membrane-peptide and peptide-peptide interactions. Our results highlight an essential role of the negatively charged membrane mimetic in the structural stability of both myxinidin and WMR. The peptides localize predominantly in the membrane's headgroup region and have a noticeable membrane thinning effect on the overall bilayer structure. Myxinidin and WMR show a different tendency to self-aggregate, which is also influenced by the membrane composition (DOPE/DOPG versus DOPE/DOPG/CL) and can be related to the previously observed difference in the ability of the peptides to disrupt different types of model membranes.

Combining Force Fields and Neural Networks for an Accurate Representation of Chemically Diverse Molecular Interactions.

A key goal of molecular modeling is the accurate reproduction of the true quantum mechanical potential energy of arbitrary molecular ensembles with a tractable classical approximation. The challenges are that analytical expressions found in general purpose force fields struggle to faithfully represent the intermolecular quantum potential energy surface at close distances and in strong interaction regimes; that the more accurate neural network approximations do not capture crucial physics concepts, e.g., nonadditive inductive contributions and application of electric fields; and that the ultra-accurate narrowly targeted models have difficulty generalizing to the entire chemical space. We therefore designed a hybrid wide-coverage intermolecular interaction model consisting of an analytically polarizable force field combined with a short-range neural network correction for the total intermolecular interaction energy. Here, we describe the methodology and apply the model to accurately determine the properties of water, the free energy of solvation of neutral and charged molecules, and the binding free energy of ligands to proteins. The correction is subtyped for distinct chemical species to match the underlying force field, to segment and reduce the amount of quantum training data, and to increase accuracy and computational speed. For the systems considered, the hybrid ab initio parametrized Hamiltonian reproduces the two-body dimer quantum mechanics (QM) energies to within 0.03 kcal/mol and the nonadditive many-molecule contributions to within 2%. Simulations of molecular systems using this interaction model run at speeds of several nanoseconds per day.

Computer simulations of a heterogeneous membrane with enhanced sampling techniques.

Computational determination of the equilibrium state of heterogeneous phospholipid membranes is a significant challenge. We wish to explore the rich phase diagram of these multi-component systems. However, the diffusion and mixing times in membranes are long compared to typical time scales of computer simulations. Here, we evaluate the combination of the enhanced sampling techniques molecular dynamics with alchemical steps and Monte Carlo with molecular dynamics with a coarse-grained model of membranes (Martini) to reduce the number of steps and force evaluations that are needed to reach equilibrium. We illustrate a significant gain compared to straightforward molecular dynamics of the Martini model by factors between 3 and 10. The combination is a useful tool to enhance the study of phase separation and the formation of domains in biological membranes.

NMR- and MD simulation-based structural characterization of the membrane-associating FATC domain of ataxia telangiectasia mutated.

The Ser/Thr protein kinase ataxia telangiectasia mutated (ATM) plays an important role in the DNA damage response, signaling in response to redox signals, the control of metabolic processes, and mitochondrial homeostasis. ATM localizes to the nucleus and at the plasma membrane, mitochondria, peroxisomes, and other cytoplasmic vesicular structures. It has been shown that the C-terminal FATC domain of human ATM (hATMfatc) can interact with a range of membrane mimetics and may thereby act as a membrane-anchoring unit. Here, NMR structural and 15N relaxation data, NMR data using spin-labeled micelles, and MD simulations of micelle-associated hATMfatc revealed that it binds the micelle by a dynamic assembly of three helices with many residues of hATMfatc located in the headgroup region. We observed that none of the three helices penetrates the micelle deeply or makes significant tertiary contacts to the other helices. NMR-monitored interaction experiments with hATMfatc variants in which two conserved aromatic residues (Phe3049 and Trp3052) were either individually or both replaced by alanine disclosed that the double substitution does not abrogate the interaction with micelles and bicelles at the high concentrations at which these aggregates are typically used, but impairs interactions with small unilamellar vesicles, usually used at much lower lipid concentrations and considered a better mimetic for natural membranes. We conclude that the observed dynamic structure of micelle-associated hATMfatc may enable it to interact with differently composed membranes or membrane-associated interaction partners and thereby regulate ATM's kinase activity. Moreover, the FATC domain of ATM may function as a membrane-anchoring unit for other biomolecules.

Interaction of C60 fullerenes with asymmetric and curved lipid membranes: a molecular dynamics study.

Interaction of fullerenes with asymmetric and curved DOPC/DOPS bicelles is studied by means of coarse-grained molecular dynamics simulations. The effects caused by asymmetric lipid composition of the membrane leaflets and the curvature of the membrane are analyzed. It is shown that the aggregates of fullerenes prefer to penetrate into the membrane in the regions of the moderately positive mean curvature. Upon penetration into the hydrophobic core of the membrane fullerenes avoid the regions of the extreme positive or the negative curvature. Fullerenes increase the ordering of lipid tails, which are in direct contact with them, but do not influence other lipids significantly. Our data suggest that the effects of the membrane curvature should be taken into account in the studies concerning permeability of the membranes to fullerenes and fullerene-based drug delivery systems.