Dirk Reith

On the nature of interactions between ionic liquids and small amino-acid-based biomolecules.

During the last decade, ionic liquids (ILs) have revealed promising properties and applications in many research fields, including biotechnology and biological sciences. The focus of this contribution is to give a critical review of the phenomena observed and current knowledge of the interactions occurring on a molecular basis. As opposed to the huge advances made in understanding the properties of proteins in ILs, complementary investigations dealing with interactions between ILs and peptides or oligopeptides are underrepresented and are mostly only of phenomenological nature. However, the field has received more attention in the last few years. This Review features a meta-analysis of the available data and findings and should, therefore, provide a basis for a scientifically profound understanding of the nature and mechanisms of interactions between ILs and structured or nonstructured peptides. Fundamental aspects of the interactions between different peptides/oligopeptides and ILs are complemented by sections on the experimental (spectroscopy, structural biology) and theoretical (computational chemistry) possibilities to explain the phenomena reported so far in the literature. In effect, this should lead to the development of novel applications and support the understanding of IL-solute interactions in general.

GROW: A gradient-based optimization workflow for the automated development of molecular models

Abstract The concept, issues of implementation and file formats of the GRadient-based Optimization Workflow for the Automated Development of Molecular Models ‘GROW’ (version 1.0) software tool are described. It enables users to perform automated optimizations of force field parameters for atomistic molecular simulations by an iterative, gradient-based optimization workflow. The modularly constructed tool consists of a main control script, specific implementations and secondary control scripts for each numerical algorithm, as well as analysis scripts. Taken together, this machinery is able to automatically optimize force fields and it is extensible by developers with regard to further optimization algorithms and simulation tools. Results on nitrogen are briefly reported as a proof of concept.

Comparison of Force Fields on the Basis of Various Model Approaches—How To Design the Best Model for the [CnMIM][NTf2] Family of Ionic Liquids

In this contribution, we present two new united-atom force fields (UA-FFs) for 1-alkyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide [C(n)MIM][NTf(2)] (n=1, 2, 4, 6, 8) ionic liquids (ILs). One is parametrized manually, and the other is developed with the gradient-based optimization workflow (GROW). By doing so, we wanted to perform a hard test to determine how researchers could benefit from semiautomated optimization procedures. As with our already published all-atom force field (AA-FF) for [C(n)MIM][NTf(2)] (T. Köddermann, D. Paschek, R. Ludwig, ChemPhysChem- 2007, 8, 2464), the new force fields were derived to fit experimental densities, self-diffusion coefficients, and NMR rotational correlation times for the IL cation and for water molecules dissolved in [C(2)MIM][NTf(2)]. In the manual force field, the alkyl chains of the cation and the CF3 groups of the anion were treated as united atoms. In the GROW force field, only the alkyl chains of the cation were united. All other parts of the structures of the ions remained unchanged to prevent any loss of physical information. Structural, dynamic, and thermodynamic properties such as viscosity, cation rotational correlation times, and heats of vaporization calculated with the new force fields were compared with values simulated with the previous AA-FF and the experimental data. All simulated properties were in excellent agreement with the experimental values. Altogether, the UA-FFs are slightly superior for speed-up reasons. The UA-FF speeds up the simulation by about 100 % and reduces the demanded disk space by about 78 %. More importantly, real time and efforts to generate force fields could be significantly reduced by utilizing GROW. The real time for the GROW parametrization in this work was 2 months. Manual parametrization, in contrast, may take up to 12 months, and this is, therefore, a significant increase in speed, though it is difficult to estimate the duration of manual parametrization.

Assessment of numerical optimization algorithms for the development of molecular models

Abstract In the pursuit to study the parameterization problem of molecular models with a broad perspective, this paper is focused on an isolated aspect: It is investigated, by which algorithms parameters can be best optimized simultaneously to different types of target data (experimental or theoretical) over a range of temperatures with the lowest number of iteration steps. As an example, nitrogen is regarded, where the intermolecular interactions are well described by the quadrupolar two-center Lennard-Jones model that has four state-independent parameters. The target data comprise experimental values for saturated liquid density, enthalpy of vaporization, and vapor pressure. For the purpose of testing algorithms, molecular simulations are entirely replaced by fit functions of vapor–liquid equilibrium (VLE) properties from the literature to assess efficiently the diverse numerical optimization algorithms investigated, being state-of-the-art gradient-based methods with very good convergency qualities. Additionally, artificial noise was superimposed onto the VLE fit results to evaluate the numerical optimization algorithms so that the calculation of molecular simulation data was mimicked. Large differences in the behavior of the individual optimization algorithms are found and some are identified to be capable to handle noisy function values.

Automated force field optimisation of small molecules using a gradient-based workflow package

In this study, the recently developed gradient-based optimisation workflow for the automated development of molecular models is for the first time applied to the parameterisation of force fields for molecular dynamics simulations. As a proof-of-concept, two small molecules (benzene and phosgene) are considered. In order to optimise the underlying intermolecular force field (described by the (12,6)-Lennard-Jones and the Coulomb potential), the energetic and diameter parameters ε and σ are fitted to experimental physical properties by gradient-based numerical optimisation techniques. Thereby, a quadratic loss function between experimental and simulated target properties is minimised with respect to the force field parameters. In this proof-of-concept, the considered physical target properties are chosen to be diverse: density, enthalpy of vapourisation and self-diffusion coefficient are optimised simultaneously at different temperatures. We found that in both cases, the optimisation could be successfully concluded by fulfillment of a pre-defined stopping criterion. Since a fairly small number of iterations were needed to do so, this study will serve as a good starting point for more complex systems and further improvements of the parametrisation task.

A modern workflow for force-field development – Bridging quantum mechanics and atomistic computational models

In this article we present our recent efforts in designing a comprehensive consistent scientific workflow, nicknamed Wolf2 Pack, for force-field optimization in the field of computational chemistry. Atomistic force fields represent a multiscale bridge that connects high-resolution quantum mechanics knowledge to coarser molecular mechanics-based models. Force-field optimization has so far been a time-consuming and error-prone process, and is a topic where the use of a scientific workflow can provide obvious great benefits. As a case study we generate a gas-phase force field for methanol using Wolf2 Pack, with special attention given toward deriving partial atomic charges.

Wolf2Pack – Portal Based Atomistic Force-Field Development

In this contribution we introduce the technical concept and implementation details concerning the front end of our force-field optimization workflow package for intramolecular degrees of freedom, called Wolf2Pack. The packages design follows our belief that parameter optimization should be a user-driven, but program guided, workflow with specific modular tasks that reduce human errors and save time. Through this design, parameter optimization becomes more reliable and reproducible. Wolf2Pack can integrate common force fields from different research areas, allowing the user to optimize balanced parameters; alternatively users can develop highly specialized force fields that suite their chemical systems. Included in the packages front end is a force-field and molecular database whose contents facilitate parameter optimization. Wolf2Pack can be accessed at www.wolf2pack.com.

A Sales Forecast Model for the German Automobile Market Based on Time Series Analysis and Data Mining Methods

In this contribution, various sales forecast models for the German automobile market are developed and tested. Our most important criteria for the assessment of these models are the quality of the prediction as well as an easy explicability. Yearly, quarterly and monthly data for newly registered automobiles from 1992 to 2007 serve as the basis for the tests of these models. The time series model used consists of additive components: trend, seasonal, calendar and error component. The three latter components are estimated univariately while the trend component is estimated multivariately by Multiple Linear Regression as well as by a Support Vector Machine. Possible influences which are considered include macro-economic and market-specific factors. These influences are analysed by a feature selection. We found the non-linear model to be superior. Furthermore, the quarterly data provided the most accurate results.

SpaGrOW—A Derivative-Free Optimization Scheme for Intermolecular Force Field Parameters Based on Sparse Grid Methods

Molecular modeling is an important subdomain in the field of computational modeling, regarding both scientific and industrial applications. This is because computer simulations on a molecular level are a virtuous instrument to study the impact of microscopic on macroscopic phenomena. Accurate molecular models are indispensable for such simulations in order to predict physical target observables, like density, pressure, diffusion coefficients or energetic properties, quantitatively over a wide range of temperatures. Thereby, molecular interactions are described mathematically by force fields. The mathematical description includes parameters for both intramolecular and intermolecular interactions. While intramolecular force field parameters can be determined by quantum mechanics, the parameterization of the intermolecular part is often tedious. Recently, an empirical procedure, based on the minimization of a loss function between simulated and experimental physical properties, was published by the authors. Thereby, efficient gradient-based numerical optimization algorithms were used. However, empirical force field optimization is inhibited by the two following central issues appearing in molecular simulations: firstly, they are extremely time-consuming, even on modern and high-performance computer clusters, and secondly, simulation data is affected by statistical noise. The latter provokes the fact that an accurate computation of gradients or Hessians is nearly impossible close to a local or global minimum, mainly because the loss function is flat. Therefore, the question arises of whether to apply a derivative-free method approximating the loss function by an appropriate model function. In this paper, a new Sparse Grid-based Optimization Workflow (SpaGrOW) is presented, which accomplishes this task robustly and, at the same time, keeps the number of time-consuming simulations relatively small. This is achieved by an efficient sampling procedure for the approximation based on sparse grids, which is described in full detail: in order to counteract the fact that sparse grids are fully occupied on their boundaries, a mathematical transformation is applied to generate homogeneous Dirichlet boundary conditions. As the main drawback of sparse grids methods is the assumption that the function to be modeled exhibits certain smoothness properties, it has to be approximated by smooth functions first. Radial basis functions turned out to be very suitable to solve this task. The smoothing procedure and the subsequent interpolation on sparse grids are performed within sufficiently large compact trust regions of the parameter space. It is shown and explained how the combination of the three ingredients leads to a new efficient derivative-free algorithm, which has the additional advantage that it is capable of reducing the overall number of simulations by a factor of about two in comparison to gradient-based optimization methods. At the same time, the robustness with respect to statistical noise is maintained. This assertion is proven by both theoretical considerations and practical evaluations for molecular simulations on chemical example substances.

Folding and unfolding characteristics of short beta strand peptides under different environmental conditions and starting configurations

We analyze the effect of different environmental conditions, sequence lengths and starting configurations on the folding and unfolding pathways of small peptides exhibiting beta turns. We use chignolin and a sequence of peptide G as examples. A variety of different analysis tools allows us to characterize the changes in the folding pathways. It is observed that different harmonic modes dominate not only for different conditions but also for different starting points. The modes remain essentially very similar but their relative importance varies. A detailed analysis from diverse viewpoints including the influence of the particular amino acid sequence, conformational aspects as well as the associated motions yields a global picture that is consistent with experimental evidence and theoretical studies published elsewhere. Patterns of modes that remain stable over a range of temperatures might serve as an additional diagnostic to identify conformations that have reliably adopted a native fold. This could aid in reconstructing the folding process of a complete protein by identifying conformationally determined regions.