Quantum mechanics of proteins in explicit water: The role of plasmon-like solute-solvent interactions

in Science Advances (2019), 5(12), 0024

Quantum-mechanical van der Waals dispersion interactions play an essential role in intraprotein and protein-water interactions—the two main factors affecting the structure and dynamics of proteins in ... [more ▼]

Quantum-mechanical van der Waals dispersion interactions play an essential role in intraprotein and protein-water interactions—the two main factors affecting the structure and dynamics of proteins in water. Typically, these interactions are only treated phenomenologically, via pairwise potential terms in classical force fields. Here, we use an explicit quantum-mechanical approach of density-functional tight-binding combined with the many-body dispersion formalism and demonstrate the relevance of many-body van der Waals forces both to protein energetics and to protein-water interactions. In contrast to commonly used pairwise approaches, many-body effects substantially decrease the relative stability of native states in the absence of water. Upon solvation, the protein-water dispersion interaction counteracts this effect and stabilizes native conformations and transition states. These observations arise from the highly delocalized and collective character of the interactions, suggesting a remarkable persistence of electron correlation through aqueous environments and providing the basis for long-range interaction mechanisms in biomolecular systems. [less ▲]

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

in Journal of Chemical Theory and Computation (2019), 15

SchNetPack is a toolbox for the development and application of deep neural networks that predict potential energy surfaces and other quantum-chemical properties of molecules and materials. It contains ... [more ▼]

SchNetPack is a toolbox for the development and application of deep neural networks that predict potential energy surfaces and other quantum-chemical properties of molecules and materials. It contains basic building blocks of atomistic neural networks, manages their training, and provides simple access to common benchmark datasets. This allows for an easy implementation and evaluation of new models. For now, SchNetPack includes implementations of (weighted) atom-centered symmetry functions and the deep tensor neural network SchNet, as well as ready-to-use scripts that allow one to train these models on molecule and material datasets. Based on the PyTorch deep learning framework, SchNetPack allows one to efficiently apply the neural networks to large datasets with millions of reference calculations, as well as parallelize the model across multiple GPUs. Finally, SchNetPack provides an interface to the Atomic Simulation Environment in order to make trained models easily accessible to researchers that are not yet familiar with neural networks. [less ▲]

sGDML: Constructing accurate and data efficient molecular force fields using machine learning

in Computer Physics Communications (2019), 240

We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global potential energy surfaces ... [more ▼]

We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global potential energy surfaces (PES) for molecules with a few dozen atoms from a limited number of user-provided reference molecular conformations and the associated atomic forces. Here, we introduce a Python software package to reconstruct and evaluate custom sGDML force fields (FFs), without requiring in-depth knowledge about the details of the model. A user-friendly command-line interface offers assistance through the complete process of model creation, in an effort to make this novel machine learning approach accessible to broad practitioners. Our paper serves as a documentation, but also includes a practical application example of how to reconstruct and use a PBE0+MBD FF for paracetamol. Finally, we show how to interface sGDML with the FF simulation engines ASE (Larsen et al., 2017) and i-PI (Kapil et al., 2019) to run numerical experiments, including structure optimization, classical and path integral molecular dynamics and nudged elastic band calculations. [less ▲]

Computational polymorph screening reveals late-appearing and poorly-soluble form of rotigotine

in Communications Chemistry (2019), 2

The active pharmaceutical ingredient rotigotine—a dopamine agonist for the treatment of Parkinson’s and restless leg diseases—was known to exist in only one polymorphic form since 1985. In 2008, the ... [more ▼]

The active pharmaceutical ingredient rotigotine—a dopamine agonist for the treatment of Parkinson’s and restless leg diseases—was known to exist in only one polymorphic form since 1985. In 2008, the appearance of a thermodynamically more stable and significantly less soluble polymorph led to a massive batch recall followed by economic and public health implications. Here, we carry out state-of-the-art computational crystal structure prediction, revealing the late-appearing polymorph without using any prior information. In addition, we predict a third crystalline form of rotigotine having thermodynamic stability between forms I and II. We provide quantitative description of the relative stability and solubility of the rotigotine polymorphs. Our study offers new insights into a challenging polymorphic system and highlights the robustness of contemporary computational crystal structure prediction during pharmaceutical development. [less ▲]

Quantitative imaging of electric surface potentials with single-atom sensitivity

in Nature Materials (2019), 18

Because materials consist of positive nuclei and negative electrons, electric potentials are omnipresent at the atomic scale. However, due to the long range of the Coulomb interaction, large-scale ... [more ▼]

Because materials consist of positive nuclei and negative electrons, electric potentials are omnipresent at the atomic scale. However, due to the long range of the Coulomb interaction, large-scale structures completely outshine small ones. This makes the isolation and quantification of the electric potentials that originate from nanoscale objects such as atoms or molecules very challenging. Here we report a non-contact scanning probe technique that addresses this challenge. It exploits a quantum dot sensor and the joint electrostatic screening by tip and surface, thus enabling quantitative surface potential imaging across all relevant length scales down to single atoms. We apply the technique to the characterization of a nanostructured surface, thereby extracting workfunction changes and dipole moments for important reference systems. This authenticates the method as a versatile tool to study the building blocks of materials and devices down to the atomic scale. [less ▲]

Ion-Hydroxyl Interactions: From High-Level Quantum Benchmarks to Transferable Polarizable Force Fields

in Journal of Chemical Theory and Computation (2019), 154

Ion descriptors in molecular mechanics models are calibrated against reference data on ion–water interactions. It is then typically assumed that these descriptors will also satisfactorily describe ... [more ▼]

Ion descriptors in molecular mechanics models are calibrated against reference data on ion–water interactions. It is then typically assumed that these descriptors will also satisfactorily describe interactions of ions with other functional groups, such as those present in biomolecules. However, several studies now demonstrate that this transferability assumption produces, in many different cases, large errors. Here we address this issue in a representative polarizable model and focus on transferability of cationic interactions from water to a series of alcohols. Both water and alcohols use hydroxyls for ion-coordination, and, therefore, this set of molecules constitutes the simplest possible case of transferability. We obtain gas phase reference data systematically from “gold-standard” quantum Monte Carlo and CCSD(T) methods, followed by benchmarked vdW-corrected DFT. We learn that the original polarizable model yields large gas phase water → alcohol transferability errors – the RMS and maximum errors are 2.3 and 5.1 kcal/mol, respectively. These errors are, nevertheless, systematic in that ion-alcohol interactions are overstabilized, and systematic errors typically imply that some essential physics is either missing or misrepresented. A comprehensive analysis shows that when both low- and high-field responses of ligand dipole polarization are described accurately, then transferability improves significantly – the RMS and maximum errors in the gas phase reduce, respectively, to 0.9 and 2.5 kcal/mol. Additionally, predictions of condensed phase transfer free energies also improve. Nevertheless, within the limits of the extrathermodynamic assumptions necessary to separate experimental estimates of salt dissolution into constituent cationic and anionic contributions, we note that the error in the condensed phase is systematic, which we attribute, at least, partially to the parametrization in long-range electrostatics. Overall, this work demonstrates a rational approach to boosting transferability of ionic interactions that will be applicable broadly to improving other polarizable and nonpolarizable models. [less ▲]

Understanding non-covalent interactions in larger molecular complexes from first principles

in Journal of Chemical Physics (2019), 150

Non-covalent interactions pervade all matter and play a fundamental role in layered materials, biological systems, and large molecular complexes. Despite this, our accumulated understanding of non ... [more ▼]

Non-covalent interactions pervade all matter and play a fundamental role in layered materials, biological systems, and large molecular complexes. Despite this, our accumulated understanding of non-covalent interactions to date has been mainly developed in the tens-of-atoms molecular regime. This falls considerably short of the scales at which we would like to understand energy trends, structural properties, and temperature dependencies in materials where non-covalent interactions have an appreciable role. However, as more reference information is obtained beyond moderately sized molecular systems, our understanding is improving and we stand to gain pertinent insights by tackling more complex systems, such as supramolecular complexes, molecular crystals, and other soft materials. In addition, accurate reference information is needed to provide the drive for extending the predictive power of more efficient workhorse methods, such as density functional approximations that also approximate van der Waals dispersion interactions. In this perspective, we discuss the first-principles approaches that have been used to obtain reference interaction energies for beyond modestly sized molecular complexes. The methods include quantum Monte Carlo, symmetry-adapted perturbation theory, non-canonical coupled cluster theory, and approaches based on the random-phase approximation. By considering the approximations that underpin each method, the most accurate theoretical references for supramolecular complexes and molecular crystals to date are ascertained. With these, we also assess a handful of widely used exchange-correlation functionals in density functional theory. The discussion culminates in a framework for putting into perspective the accuracy of high-level wavefunction-based methods and identifying future challenges. [less ▲]

Stability of functionalized platform molecules on Au(111)

in Journal of Chemical Physics (2018), 149

Trioxatriangulenium (TOTA) platform molecules were functionalized with methyl, ethyl, ethynyl, propynyl, and hydrogen and sublimated onto Au(111) surfaces. Low-temperature scanning tunneling microscopy ... [more ▼]

Trioxatriangulenium (TOTA) platform molecules were functionalized with methyl, ethyl, ethynyl, propynyl, and hydrogen and sublimated onto Au(111) surfaces. Low-temperature scanning tunneling microscopy data reveal that >99% of ethyl-TOTA and methyl-TOTA remain intact, whereas 60% of H-TOTA and >99% of propynyl-TOTA and ethynyl-TOTA decompose. The observed tendency toward fragmentation on Au(111) is opposite to the sequence of gas-phase stabilities of the molecules. Although Au(111) is the noblest of all metal surfaces, the binding energies of the decomposition products to Au(111) destabilize the functionalized platforms by 2 to 3.9 eV (190–370 kJ/mol) and even render some of them unstable as revealed by density functional theory calculations. Van der Waals forces are important, as they drive the adsorption of the platform molecules. [less ▲]

i-PI 2.0: A Universal Force Engine for Advanced Molecular Simulations

in Computer Physics Communications (2018)

Progress in the atomic-scale modeling of matter over the past decade has been tremendous. This progress has been brought about by improvements in methods for evaluating interatomic forces that work by ... [more ▼]

Progress in the atomic-scale modeling of matter over the past decade has been tremendous. This progress has been brought about by improvements in methods for evaluating interatomic forces that work by either solving the electronic structure problem explicitly, or by computing accurate approximations of the solution and by the development of techniques that use the Born–Oppenheimer (BO) forces to move the atoms on the BO potential energy surface. As a consequence of these developments it is now possible to identify stable or metastable states, to sample configurations consistent with the appropriate thermodynamic ensemble, and to estimate the kinetics of reactions and phase transitions. All too often, however, progress is slowed down by the bottleneck associated with implementing new optimization algorithms and/or sampling techniques into the many existing electronic-structure and empirical-potential codes. To address this problem, we are thus releasing a new version of the i-PI software. This piece of software is an easily extensible framework for implementing advanced atomistic simulation techniques using interatomic potentials and forces calculated by an external driver code. While the original version of the code (Ceriotti et al., 2014) was developed with a focus on path integral molecular dynamics techniques, this second release of i-PI not only includes several new advanced path integral methods, but also offers other classes of algorithms. In other words, i-PI is moving towards becoming a universal force engine that is both modular and tightly coupled to the driver codes that evaluate the potential energy surface and its derivatives. [less ▲]

Capturing intensive and extensive DFT/TDDFT molecular properties with machine learning

in European Physical Journal B -- Condensed Matter (2018), 91

Machine learning has been successfully applied to the prediction of chemical properties of small organic molecules such as energies or polarizabilities. Compared to these properties, the electronic ... [more ▼]

Machine learning has been successfully applied to the prediction of chemical properties of small organic molecules such as energies or polarizabilities. Compared to these properties, the electronic excitation energies pose a much more challenging learning problem. Here, we examine the applicability of two existing machine learning methodologies to the prediction of excitation energies from time-dependent density functional theory. To this end, we systematically study the performance of various 2- and 3-body descriptors as well as the deep neural network SchNet to predict extensive as well as intensive properties such as the transition energies from the ground state to the rst and second excited state. As perhaps expected current state-of-the-art machine learning techniques are more suited to predict extensive as opposed to intensive quantities. We speculate on the need to develop global descriptors that can describe both extensive and intensive properties on equal footing. [less ▲]