Dynamic framework for large-scale modeling of membranes and peripheral proteins.

In this chapter, we present a novel computational framework to study the dynamic behavior of extensive membrane systems, potentially in interaction with peripheral proteins, as an alternative to conventional simulation methods. The framework effectively describes the complex dynamics in protein-membrane systems in a mesoscopic particle-based setup. Furthermore, leveraging the hydrodynamic coupling between the membrane and its surrounding solvent, the coarse-grained model grounds its dynamics in macroscopic kinetic properties such as viscosity and diffusion coefficients, marrying the advantages of continuum- and particle-based approaches. We introduce the theoretical background and the parameter-space optimization method in a step-by-step fashion, present the hydrodynamic coupling method in detail, and demonstrate the application of the model at each stage through illuminating examples. We believe this modeling framework to hold great potential for simulating membrane and protein systems at biological spatiotemporal scales, and offer substantial flexibility for further development and parametrization.

Slicing and Dicing: Optimal Coarse-Grained Representation to Preserve Molecular Kinetics.

The aim of molecular coarse-graining approaches is to recover relevant physical properties of the molecular system via a lower-resolution model that can be more efficiently simulated. Ideally, the lower resolution still accounts for the degrees of freedom necessary to recover the correct physical behavior. The selection of these degrees of freedom has often relied on the scientist's chemical and physical intuition. In this article, we make the argument that in soft matter contexts desirable coarse-grained models accurately reproduce the long-time dynamics of a system by correctly capturing the rare-event transitions. We propose a bottom-up coarse-graining scheme that correctly preserves the relevant slow degrees of freedom, and we test this idea for three systems of increasing complexity. We show that in contrast to this method existing coarse-graining schemes such as those from information theory or structure-based approaches are not able to recapitulate the slow time scales of the system.

Machine learning of consistent thermodynamic models using automatic differentiation.

We propose a data-driven method to describe consistent equations of state (EOS) for arbitrary systems. Complex EOS are traditionally obtained by fitting suitable analytical expressions to thermophysical data. A key aspect of EOS is that the relationships between state variables are given by derivatives of the system free energy. In this work, we model the free energy with an artificial neural network and utilize automatic differentiation to directly learn the derivatives of the free energy. We demonstrate this approach on two different systems, the analytic van der Waals EOS and published data for the Lennard-Jones fluid, and we show that it is advantageous over direct learning of thermodynamic properties (i.e., not as derivatives of the free energy but as independent properties), in terms of both accuracy and the exact preservation of the Maxwell relations. Furthermore, the method implicitly provides the free energy of a system without explicit integration.

Modeling of Peptides with Classical and Novel Machine Learning Force Fields: A Comparison.

The replacement of classical force fields (FFs) with novel neural-network-based frameworks is an emergent topic in molecular dynamics (MD) simulations. In contrast to classical FFs, which have proven their capability to provide insights into complex soft matter systems at an atomistic resolution, the machine learning (ML) potentials have yet to demonstrate their applicability for soft materials. However, the underlying philosophy, which is learning the energy of an atom in its surrounding chemical environment, makes this approach a promising tool. In particular for the exploration of novel chemical compounds, which have not been considered in the original parametrization of classical FFs. In this article, we study the performance of the ANI-2x ML model and compare the results with those of two classical FFs, namely, CHARMM27 and the GROMOS96 43a1 FF. We explore the performance of these FFs for bulk water and two model peptides, trialanine and a 9-mer of the α-aminoisobutyric acid, in vacuum and water. The results for water describe a highly ordered water structure, with a structure similar to those using ab initio molecular dynamics simulations. The energy landscape of the peptides described by Ramachandran maps show secondary structure basins similar to those of the classical FFs but differ in the position and relative stability of the basins. Details of the sampled structures show a divergent performance of the different models, which can be related either to the short-ranged nature of the ML potentials or to shortcomings of the underlying data set used for training. These findings highlight the current state of the applicability of ANI-2x ML potential for MD simulations of soft matter systems. Simultaneously, they provide insights for future improvements of current ML potentials.

Relative entropy indicates an ideal concentration for structure-based coarse graining of binary mixtures.

Many methodological approaches have been proposed to improve systematic or bottom-up coarse-graining techniques to enhance the representability and transferability of the derived interaction potentials. Transferability describes the ability of a coarse-grained (CG) model to be predictive, i.e., to describe a system at state points different from those chosen for parametrization. Whereas the representability characterizes the accuracy of a CG model to reproduce target properties of the underlying reference or fine-grained model at a given state point. In this article, we shift the focus away from methodological aspects and rather raise the question whether we can overcome the disadvantages of a given method in terms of representability and transferability by systematically selecting the state point at which the CG model gets parametrized. We answer this question by applying the inverse Monte Carlo (IMC) approach-a structure-based coarse-graining method-to derive effective interactions for binary mixtures of simple Lennard-Jones (LJ) particles, which are different in size. For such simple systems we indeed can identify a concentration where the derived potentials show the best performance in terms of structural representability and transferability. This specific concentration is identified by computing the relative entropy which quantifies the information loss between different IMC models and the reference LJ model at varying mixture compositions. Further, we show that an IMC model for mixtures of n-hexane and n-perfluorohexane shows the same trend in transferability as the IMC models for the LJ system. All derived models are more transferable in the direction of increasing concentration of the larger-sized compound.

Transferability of Local Density-Assisted Implicit Solvation Models for Homogeneous Fluid Mixtures.

The application of bottom-up coarse grained (CG) models to study the equilibrium mixing behavior of liquids is rather challenging, since these models can be significantly influenced by the density or the concentration of the state chosen during parametrization. This dependency leads to low transferability in density/concentration space and has been one of the major limitations in bottom-up coarse graining. Recent approaches proposed to tackle this shortcoming range from the addition of thermodynamic constraints, to an extended ensemble parametrization, to the addition of supplementary terms to the system's Hamiltonian. To study fluid phase equilibria with bottom-up CG models, the application of local density (LD) potentials appears to be a promising approach, as shown in previous work by Sanyal and Shell [T. Sanyal, M. S. Shell, J. Phys. Chem. B, 2018, 122, 5678]. Here, we want to further explore this method and test its ability to model a system which contains structural inhomogeneities only on the molecular scale, namely, solutions of methanol and water. We find that a water-water LD potential improves the transferability of an implicit-methanol CG model toward high water concentration. Conversely, a methanol-methanol LD potential does not significantly improve the transferability of an implicit-water CG model toward high methanol concentration. These differences appear due to the presence of cooperative interactions in water at high concentrations that the LD potentials can capture. In addition, we compare two different approaches to derive our CG models, namely, relative entropy optimization and the Inverse Monte Carlo method, and formally demonstrate under which analytical and numerical assumptions these two methods yield equivalent results.

Phase Equilibria Modeling with Systematically Coarse-Grained Models-A Comparative Study on State Point Transferability.

Coarse-grained (CG) models for soft matter systems can be parameterized using a variety of approaches. In systematic coarse-graining procedures, effective interactions are calculated using a reference fine-grained simulation. The degree to which thermodynamical and structural properties of the reference system are reproduced depends critically on the coarse-graining method used to parameterize the model. In this article, a comparative study is presented on the capability of different CG models to reproduce vapor-liquid equilibrium thermodynamics of hexane and perfluorohexane systems. CG models are developed using coarse-graining methods based on the reproduction of (a) structure and (b) coupling free energies. Although the structure-based models show an overestimation of the pressure and therefore perform relatively poorly in reproducing the vapor-liquid thermodynamics, it is shown that methods based on coupling free energies, in particular the conditional reversible work method, are capable of better reproducing the vapor-liquid phase diagram of the united-atom reference simulations, while reproducing the liquid structure almost as accurately as the structure-based CG model. This illustrates the state point transferability of the interaction potentials calculated using these methods, as the resulting CG model (which has been parameterized at 300 K) is capable of accurately modeling the thermodynamic properties up to the critical point.

Addressing the temperature transferability of structure based coarse graining models.

Systematically derived coarse grained (CG) models for molecular liquids do not inherently guarantee transferability to a state point different from its reference, especially when derived on the basis of structure based CG methods like Inverse Monte Carlo (IMC). Several efforts made in the past years to improve the transferability of these models focused on including thermodynamic constraints or on the application of multistate parametrization. Das and Andersen (DA) [Das et al., J. Chem. Phys., 2010, 132, 164106.] proposed a different Ansatz. They derived a correction term added to the system's Hamiltonian to reproduce the virial pressure and the volume fluctuations of the reference system in the CG resolution which does not require further adjustment of the effective pair potential. Herein, we discuss the possibility to achieve temperature transferability with IMC models for selected alkanes following the optimization of the DA approach as proposed by Dunn and Noid (DN) [Dunn et al., J. Chem. Phys., 2015, 143, 243148.]. The work focuses on a novel approach to determine the DN correction term for different state points by linear interpolation.

Do some IPM concepts contribute to the development of fungicide resistance? Lessons learned from the apple scab pathosystem in the United States.

One goal of integrated pest management (IPM) as it is currently practiced is an overall reduction in fungicide use in the management of plant disease. Repeated and long-term success of the early broad-spectrum fungicides led to optimism about the capabilities of fungicides, but to an underestimation of the risk of fungicide resistance within agriculture. In 1913, Paul Ehrlich recognized that it was best to 'hit hard and hit early' to prevent microbes from evolving resistance to treatment. This tenet conflicts with the fungicide reduction strategies that have been widely promoted over the past 40 years as integral to IPM. The authors hypothesize that the approaches used to implement IPM have contributed to fungicide resistance problems and may still be driving that process in apple scab management and in IPM requests for proposals. This paper also proposes that IPM as it is currently practiced for plant diseases of perennial systems has been based on the wrong model, and that conceptual shifts in thinking are needed to address the problem of fungicide resistance.

Variability Among Forecast Models for the Apple Sooty Blotch/Flyspeck Disease Complex.

Several disease forecast models have been developed to guide treatment of the sooty blotch and flyspeck (SBFS) disease complex of apple. Generally, these empirical models are based on the accumulation of hours of leaf wetness (leaf wetness duration [LWD]) from a biofix at or near the phenological growth stage petal fall, when apple flower petals senesce and drop. The models recommend timing of the initial fungicide application targeting SBFS. However, there are significant differences among SBFS forecast models in terms of biofix and the length of LWD thresholds. A comparison of models using a single input data set generated recommendations for the first SBFS fungicide application that differed by up to 5 weeks. In an attempt to improve consistency among models, potential sources for differences were examined. Leaf wetness (LW) is a particularly variable parameter among models, depending on whether on-site or remote weather data were used, the types of sensors and their placement for on-site monitors, and the models used to estimate LW remotely. When SBFS models are applied in the field, recommended treatment thresholds do not always match the method of data acquisition, leading to potential failures. Horticultural factors, such as tree size, canopy density, and cultivar, and orchard site factors such as the distance to potential inoculum sources can impact risk of SBFS and should also be considered in forecast models. The number of fungal species identified as contributors to the SBFS disease complex has expanded tremendously in recent years. A lack of understanding of key epidemiological factors for different fungi in the complex, and which fungi represent the most challenging management problems, are obvious issues in the development of improved SBFS models. If SBFS forecast models are to be adopted, researchers will need to address these issues.

Diversity and biogeography of sooty blotch and flyspeck fungi on apple in the eastern and midwestern United States.

Sooty blotch and flyspeck (SBFS) fungi on apple fruit were sampled from nine orchards in four midwestern U.S. states during 2000 and 30 orchards in 10 eastern U.S. states during 2005 in order to estimate taxonomic diversity and discern patterns of geographic distribution. Forty apple fruit per orchard were arbitrarily sampled and colonies of each mycelial phenotype were counted on each apple. Representative colonies were isolated, cultures were purified, and DNA was extracted. For representative isolates, the internal transcribed spacer (ITS) and large subunit (LSU) regions of ribosomal DNA were amplified and sequenced. In total, 60 SBFS putative species were identified based on ITS sequences and morphological characteristics; 30 of these were discovered in the 2005 survey. Modified Koch's postulates were fulfilled for all 60 species in an Iowa orchard; colonies resulting from inoculation of apple fruit were matched to the original isolates on the basis of mycelial type and ITS sequence. Parsimony analysis for LSU sequences from both surveys revealed that 58 putative SBFS species were members of the Dothideomycetes, 52 were members of the Capnodiales, and 36 were members of the Mycosphaerellaceae. The number of SBFS species per orchard varied from 2 to 15. Number of SBFS species and values of the Margalef and Shannon indexes were significantly (P < 0.05) lower in 21 orchards that had received conventional fungicide sprays during the fruit maturation period than in 14 unsprayed orchards. Several SBFS species, including Schizothyrium pomi, Peltaster fructicola, and Pseudocercosporella sp. RH1, were nearly ubiquitous, whereas other species, such as Stomiopeltis sp. RS5.2, Phialophora sessilis, and Geastrumia polystigmatis, were found only within restricted geographic regions. The results document that the SBFS complex is far more taxonomically diverse than previously recognized and provide strong evidence that SBFS species differ in geographic distribution. To achieve more efficient management of SBFS, it may be necessary to understand the environmental biology of key SBFS species in each geographic region.

A Comparison of Methods Used to Estimate the Maturity and Release of Ascospores of Venturia inaequalis.

Maturation and release of ascospores of Venturia inaequalis were assessed at Geneva and Highland, NY, and at Durham, NH, by microscopic examination of crushed pseudothecia excised from infected apple leaves that were collected weekly from orchards (squash mounts) in 14 siteyear combinations. Airborne ascospore dose was monitored at each location in each year of the study by volumetric spore traps. Additional laboratory assessments were made at Geneva to quantify release from infected leaf segments upon wetting (discharge tests). Finally, ascospore maturity was estimated for each location using a degree-day model developed in an earlier study. Ascospore maturation and release determined by squash mounts and discharge tests lagged significantly behind cumulative ascospore release as measured by volumetric spore traps in the field. The mean date of 98% ascospore discharge as determined by squash mounts or discharge tests occurred from 23 to 28 days after the mean date on which 98% cumulative ascospore release had been detected by volumetric traps. In contrast, cumulative ascospore maturity estimated by the degree-day model was highly correlated (r2 = 0.82) with observed cumulative ascospore release as monitored by the volumetric traps. Although large differences between predicted maturity and observed discharge were common during the exponential phase of ascospore development, the date of 98% cumulative ascospore maturity predicted by the model was generally within 1 to 9 calendar days of the date of 98% cumulative ascospore recovery in the volumetric traps. Cumulative ascospore discharge as monitored by the volumetric traps always exceeded 98% at 600 degree days (base = 0°C) after green tip. Estimating the relative quantity of primary inoculum indirectly by means of a degree-day model was more closely aligned with observed ascospore release, as measured by volumetric traps, than actual assessments of ascospore maturity and discharge obtained through squash mounts and discharge tests. The degree-day model, therefore, may be a more accurate predictor of ascospore depletion than squash mounts or discharged tests, and has the added advantage that it can be widely applied to generate site-specific estimates of ascospore maturity for any location where daily temperature data are available.