Construction of Multiscale Dissipative Particle Dynamics (DPD) Models from Other Coarse-Grained Models.

We present a general scheme for converting coarse-grained models into Dissipative Particle Dynamics (DPD) models. We build the corresponding DPD models by analogy with the de novo DPD coarse-graining scheme suggested by Groot and Warren (J. Chem. Phys., 1997). Electrostatic interactions between charged DPD particles are represented though the addition of a long-range Slater Coulomb potential as suggested by González-Melchor et al. (J. Chem. Phys., 2006). The construction is illustrated by converting MARTINI models for various proteins into a DPD representation, but it not restricted to the usual potential form in the MARTINI model-viz., Lennard-Jones potentials. We further extended the DPD scheme away from the typical use of homogeneous particle sizes, therefore faithfully representing the variations in the particle sizes seen in the underlying MARTINI model. The accuracy of the resulting construction of our generalized DPD models with respect to several structural observables has been benchmarked favorably against all-atom and MARTINI models for a selected set of peptides and proteins, and variations in the scales of the coarse-graining of the water solvent.

Optimized Bags of Artificial Neural Networks Can Predict the Viability of Organisms Exposed to Nanoparticles.

Prediction of organismal viability upon exposure to a nanoparticle in varying environments─as fully specified at the molecular scale─has emerged as a useful figure of merit in the design of engineered nanoparticles. We build on our earlier finding that a bag of artificial neural networks (ANNs) can provide such a prediction when such machines are trained with a relatively small data set (with ca. 200 examples). Therein, viabilities were predicted by consensus using the weighted means of the predictions from the bags. Here, we confirm the accuracy and precision of the prediction of nanoparticle viabilities using an optimized bag of ANNs over sets of data examples that had not previously been used in the training and validation process. We also introduce the viability strip, rather than a single value, as the prediction and construct it from the viability probability distribution of an ensemble of ANNs compatible with the data set. Specifically, the ensemble consists of the ANNs arising from subsets of the data set corresponding to different splittings between training and validation, and the different bags (k-folds). A k-1k machine uses a single partition (or bag) of k - 1 ANNs each trained on 1/k of the data to obtain a consensus prediction, and a k-bag machine quorum samples the k possible k-1k machines available for a given partition. We find that with increasing k in the k-bag or k-1k machines, the viability strips become more normally distributed and their predictions become more precise. Benchmark comparisons between ensembles of 4-bag machines and 34 fraction machines suggest that the 34 fraction machine has similar accuracy while overcoming some of the challenges arising from divergent ANNs in the 4-bag machines.

Tertiary Plasticity Drives the Efficiency of Enterocin 7B Interactions with Lipid Membranes.

The ability of antimicrobial peptides to efficiently kill their bacterial targets depends on the efficiency of their binding to the microbial membrane. In the case of enterocins, there is a three-part interaction: initial binding, unpacking of helices on the membrane surface, and permeation of the lipid bilayer. Helical unpacking is driven by disruption of the peptide hydrophobic core when in contact with membranes. Enterocin 7B is a leaderless enterocin antimicrobial peptide produced from Enterococcus faecalis that functions alone, or with its cognate partner enterocin 7A, to efficiently kill a wide variety of Gram-stain positive bacteria. To better characterize the role that tertiary structural plasticity plays in the ability of enterocin 7B to interact with the membranes, a series of arginine single-site mutants were constructed that destabilize the hydrophobic core to varying degrees. A series of experimental measures of structure, stability, and function, including CD spectra, far UV CD melting profiles, minimal inhibitory concentrations analysis, and release kinetics of calcein, show that decreased stabilization of the hydrophobic core is correlated with increased efficiency of a peptide to permeate membranes and in killing bacteria. Finally, using the computational technique of adaptive steered molecular dynamics, we found that the atomistic/energetic landscape of peptide mechanical unfolding leads to free energy differences between the wild type and its mutants, whose trends correlate well with our experiment.

Representability and Dynamical Consistency in Coarse-Grained Models.

We address the challenge of representativity and dynamical consistency when unbonded fine-grained particles are collected together into coarse-grained particles. We implement a hybrid procedure for identifying and tracking the underlying fine-grained particles─e.g., atoms or molecules─by exchanging them between the coarse-grained particles periodically at a characteristic time. The exchange involves a back-mapping of the coarse-grained particles into fine-grained particles and a subsequent reassignment to coarse-grained particles conserving total mass and momentum. We find that an appropriate choice of the characteristic exchange time can lead to the correct effective diffusion rate of the fine-grained particles when simulated in hybrid coarse-grained dynamics. In the compressed (supercritical) fluid regime, without the exchange term, fine-grained particles remain associated with a given coarse-grained particle, leading to substantially lower diffusion rates than seen in all-atom molecular dynamics of the fine-grained particles. Thus, this work confirms the need for addressing the representativity of fine-grained particles within coarse-grained particles and offers a simple exchange mechanism so as to retain dynamical consistency between the fine- and coarse-grained scales.

NestedAE: interpretable nested autoencoders for multi-scale materials characterization.

We introduce an interpretable machine learning architecture, NestedAE, for multiscale materials using nested supervised autoencoders. We benchmarked the performance of NestedAE on two databases: (1) a synthetic dataset created from nested analytical functions whose dimensionality is therefore known a priori, and (2) a multiscale MHP dataset that is a combination of an open source dataset containing atomic and ionic properties, and a second dataset containing device characterization using current density-voltage (J-V) analysis. The NestedAE architecture was found to have higher noise robustness and lower reconstruction losses when compared to a vanilla autoencoder (AE). Its application on the MHP dataset revealed links between crystal scale properties and device performance in agreement with earlier experimental observations.

Heat Transfer Enhancement in Tree-Structured Polymer Linked Gold Nanoparticle Networks.

Human brains use a tree-like neuron network for information processing at high efficiency and low energy consumption. Tree-like structures have also been engineered to enhance mass and heat transfer in various applications. In this work, we reveal the heat transfer mechanism in tree-structured polymer linked gold nanoparticle (AuNP) networks using atomistic simulations. We report both upward and downward heat fluxes between root and leaf nodes in tree-structured polyethylene (PE) and poly(p-phenylene) (PPP) linked AuNP networks at tree levels from 1 to 5. We found that the heat conductance increases with an increasing polymer tree level. The heat transfer enhancement is due to the resulting increase in the low-frequency vibrational modes. This and other thermal properties are affected by the location of the AuNPs in the tree. Moreover, complex tree structures with at least five levels were found to be robust in the sense that disabling half of the leaves did not change the overall heat conductance.

Nanoscale CuO charge and morphology control Fusarium suppression and nutrient biofortification in field-grown tomato and watermelon.

Limited data exist on how surface charge and morphology impact the effectiveness of nanoscale copper oxide (CuO) as an agricultural amendment under field conditions. This study investigated the impact of these factors on tomatoes and watermelons following foliar treatment with CuO nanosheets (NS-) or nanospikes (NP+ and NP-) exhibiting positive or negative surface charge. Results showed plant species-dependent benefits. Notably, tomatoes infected with Fusarium oxysporum had significantly reduced disease progression when treated with NS-. Watermelons benefited similarly from NP+. Although disease suppression was significant and trends indicated increased yield, the yield effects weren't statistically significant. However, several nanoscale treatments significantly enhanced the fruit's nutritional value, and this nano-enabled biofortification was a function of particle charge and morphology. Negatively charged nanospikes significantly increased the Fe content of healthy watermelon and tomato (20-28 %) and Ca in healthy tomato (66 %), compared to their positively charged counterpart. Negatively charged nanospikes also outperformed negatively charged nanosheets, leading to significant increases in the content of S and Mg in infected watermelon (37-38 %), Fe in healthy watermelon (58 %), and Ca (42 %) in healthy tomato. These findings highlight the potential of tuning nanoscale CuO chemistry for disease suppression and enhanced food quality under field conditions.

Atomically precise nanoclusters predominantly seed gold nanoparticle syntheses.

Seed-mediated synthesis strategies, in which small gold nanoparticle precursors are added to a growth solution to initiate heterogeneous nucleation, are among the most prevalent, simple, and productive methodologies for generating well-defined colloidal anisotropic nanostructures. However, the size, structure, and chemical properties of the seeds remain poorly understood, which partially explains the lack of mechanistic understanding of many particle growth reactions. Here, we identify the majority component in the seed solution as an atomically precise gold nanocluster, consisting of a 32-atom Au core with 8 halide ligands and 12 neutral ligands constituting a bound ion pair between a halide and the cationic surfactant: Au32X8[AQA+•X-]12 (X = Cl, Br; AQA = alkyl quaternary ammonium). Ligand exchange is dynamic and versatile, occurring on the order of minutes and allowing for the formation of 48 distinct Au32 clusters with AQAX (alkyl quaternary ammonium halide) ligands. Anisotropic nanoparticle syntheses seeded with solutions enriched in Au32X8[AQA+•X-]12 show narrower size distributions and fewer impurity particle shapes, indicating the importance of this cluster as a precursor to the growth of well-defined nanostructures.

Bottom-Up Construction of the Interaction between Janus Particles.

While the interaction between two uniformly charged spheres─viz colloids─is well-known, the interaction between nonuniformly charged spheres such as Janus particles is not. Specifically, the Derjaguin approximation relates the potential energy between two spherical particles with the interaction energy Vpl per unit area between two planar surfaces. The formalism has been extended to obtain a quadrature expression for the screened electrostatic interaction between Janus colloids with variable relative orientations. The interaction is decomposed into three zones in the parametric space, distinguished by their azimuthal symmetry. Different specific situations are examined to estimate the contributions of these zones to the total energy. The effective potential Vpl is renormalized such that the resulting potential energy is identical with the actual one for the most preferable relative orientations between the Janus particles. The potential energy as a function of the separation distance and the mutual orientation of a pair of particles compares favorably between the analytical (but approximate) form and the rigorous point-wise computational model used earlier. Coarse-grained models of Janus particles can thus implement this potential model efficiently without loss of generality.

Discipline-Based Diversity Research in Chemistry.

ConspectusWe introduce the term discipline-based diversity research (DBDR) to capture the emerging field of research advancing diversity, equity, inclusion, and belonging with specificity to a given discipline. Contextualizing a human dynamic through a disciplinary lens has already given rise to discipline-based education research (DBER). The modalities through which students and practitioners think and process information are a reflection of a given discipline, and it tends to give rise to its professional practices. Through DBER, such specification is necessary in addressing evidence-based practices that are effective for teaching a particular subject. Likewise, the inequities and opportunities within a given field (and its professional culture) must be addressed within a disciplinary lens. Thus, the findings from social science in diversity in arbitrary contexts must be analyzed, interpreted, applied, and researched within a given discipline.One specific challenge to academic chemistry is the lack of inclusion in the sense that the faculties in research-active chemistry departments are far from diverse. We recapitulate the percentage of women and under-represented person of color (URPOC) professors over the past 20 years reported by us and other sources. The data admits to linear fits with high confidence. Assuming this linearity holds, the gender gap in representation would be bridged only in 2062, and the threshold of 20% of the faculty as URPOC would be reached only in 2113. While the community has actively engaged in modifying practices and procedures to redress this grim projection, it should be clear that more needs to be done.Toward this objective, we have been driven by the top-down hypothesis that solutions must be led intentionally through the top─that is, by department heads and chairs─because they are the stewards of the infrastructure. Department chairs and the chemistry community have engaged in DBDR through biennial workshops─that is, through the Open Chemistry Collaborative in Diversity Equity (OXIDE)'s National Diversity Equity Workshops (NDEWs)─to survey and evaluate existing policies and practices aimed at advancing inclusive excellence. This has led to research-based recommendations for the implementation of solutions in chemistry departments. This includes (i) engaging in community, (ii) conducting authentic and open searches, and (iii) recognizing and rewarding inclusive excellence. What makes them DBDR in chemistry is that we have to articulate and contextualize these solutions in terms of practices and procedures that we conduct in chemistry, assess their efficacy, and promote them across our discipline. Furthermore, we must offer theories of change for reforming them while offering frameworks that fit within how chemists think and practice.In this Account, we demonstrate how DBDR has taken root in chemistry, forecast where this emerging field may go, and provide a blueprint for how it might be replicated in other disciplines.

Correlation between chemical denaturation and the unfolding energetics of Acanthamoeba actophorin.

The actin filament network is in part remodeled by the action of a family of filament severing proteins that are responsible for modulating the ratio between monomeric and filamentous actin. Recent work on the protein actophorin from the amoeba Acanthamoeba castellani identified a series of site-directed mutations that increase the thermal stability of the protein by 22°C. Here, we expand this observation by showing that the mutant protein is also significantly stable to both equilibrium and kinetic chemical denaturation, and employ computer simulations to account for the increase in thermal or chemical stability through an accounting of atomic-level interactions. Specifically, the potential of mean force (PMF) can be obtained from steered molecular dynamics (SMD) simulations in which a protein is unfolded. However, SMD can be inefficient for large proteins as they require large solvent boxes, and computationally expensive as they require increasingly many SMD trajectories to converge the PMF. Adaptive steered molecular dynamics (ASMD) overcomes the second of these limitations by steering the particle in stages, which allows for convergence of the PMF using fewer trajectories compared with SMD. Use of the telescoping water scheme within ASMD partially overcomes the first of these limitations by reducing the number of waters at each stage to only those needed to solvate the structure within a given stage. In the PMFs obtained from ASMD, the work of unfolding Acto-2 was found to be higher than the Acto-WT by approximately 120 kCal/mol and reflects the increased stability seen in the chemical denaturation experiments. The evolution of the average number of hydrogen bonds and number of salt bridges during the pulling process provides a mechanistic view of the structural changes of the actophorin protein as it is unfolded, and how it is affected by the mutation in concert with the energetics reported through the PMF.

Machine Learning-Assisted Carbon Dot Synthesis: Prediction of Emission Color and Wavelength.

Carbon dots (CDs) have attracted great attention in a range of applications due to their bright photoluminescence, high photostability, and good biocompatibility. However, it is challenging to design CDs with specific emission properties because the syntheses involve many parameters, and it is not clear how each parameter influences the CD properties. To help bridge this gap, machine learning, specifically an artificial neural network, is employed in this work to characterize the impact of synthesis parameters on and make predictions for the emission color and wavelength for CDs. The machine reveals that the choice of reaction method, purification method, and solvent relate more closely to CD emission characteristics than the reaction temperature or time, which are frequently tuned in experiments. After considering multiple models, the best performing machine learning classification model achieved an accuracy of 94% in predicting relative to actual color. In addition, hybrid (two-stage) models incorporating both color classification and an artificial neural network k-ensemble model for wavelength prediction through regression performed significantly better than either a standard artificial neural network or a single-stage artificial neural network k-ensemble regression model. The accuracy of the model predictions was evaluated against CD emission wavelengths measured from experiments, and the minimum mean average error is 25.8 nm. Overall, the models developed in this work can effectively predict the photoluminescence emission of CDs and help design CDs with targeted optical properties.

Thermal Transport through Polymer-Linked Gold Nanoparticles.

Polymer-nanoparticle networks have potential applications in molecular electronics and nanophononics. In this work, we use all-atom molecular dynamics to reveal the fundamental mechanisms of thermal transport in polymer-linked gold nanoparticle (AuNP) dimers at the molecular level. Attachment of the polymers to AuNPs of varying sizes allows the determination of effects from the flexibility of the chains when their ends are not held fixed. We report heat conductance (G) values for six polymers-viz. polyethylene, poly(p-phenylene), polyacene, polyacetylene, polythiophene, and poly(3,4-ethylenedioxythiophene)-that represent a broad range of stiffness. We address the multimode effects of polymer type, AuNP size, polymer chain length, polymer conformation, system temperature, and number of linking polymers on G. The combination of the mechanisms for phonon boundary scattering and intrinsic phonon scattering has a strong effect on G. We find that the values of G are larger for conjugated polymers because of the stiffness in their backbones. They are also larger in the low-temperature region for all polymers owing to the quenching of segmental rotations at low temperature. Our simulations also suggest that the total G is additive as the number of linking polymers in the AuNP dimer increases from 1 to 2 to 3.

Nanoscale 3D spatial addressing and valence control of quantum dots using wireframe DNA origami.

Control over the copy number and nanoscale positioning of quantum dots (QDs) is critical to their application to functional nanomaterials design. However, the multiple non-specific binding sites intrinsic to the surface of QDs have prevented their fabrication into multi-QD assemblies with programmed spatial positions. To overcome this challenge, we developed a general synthetic framework to selectively attach spatially addressable QDs on 3D wireframe DNA origami scaffolds using interfacial control of the QD surface. Using optical spectroscopy and molecular dynamics simulation, we investigated the fabrication of monovalent QDs of different sizes using chimeric single-stranded DNA to control QD surface chemistry. By understanding the relationship between chimeric single-stranded DNA length and QD size, we integrated single QDs into wireframe DNA origami objects and visualized the resulting QD-DNA assemblies using electron microscopy. Using these advances, we demonstrated the ability to program arbitrary 3D spatial relationships between QDs and dyes on DNA origami objects by fabricating energy-transfer circuits and colloidal molecules. Our design and fabrication approach enables the geometric control and spatial addressing of QDs together with the integration of other materials including dyes to fabricate hybrid materials for functional nanoscale photonic devices.

Implementation of Telescoping Boxes in Adaptive Steered Molecular Dynamics.

Long-time dynamical processes, such as those involving protein unfolding and ligand interactions, can be accelerated and realized through steered molecular dynamics (SMD). The challenge has been the extraction of information from such simulations that generalize for complex nonequilibrium processes. The use of Jarzynski's equality opened the possibility of determining the free energy along the steered coordinate, but sampling over the nonequilibrium trajectories is slow to converge. Adaptive steered molecular dynamics (ASMD) and other related techniques have been introduced to overcome this challenge through the use of stages. Here, we take advantage of these stages to address the numerical cost that arises from the required use of very large solvent boxes. We introduce telescoping box schemes within adaptive steered molecular dynamics (ASMD) in which we adjust the solvent box between stages and thereby vary (and optimize) the required number of solvent molecules. We have benchmarked the method on a relatively long α-helical peptide, Ala30, with respect to the potential of mean force and hydrogen bonds. We show that the use of telescoping boxes introduces little numerical error while significantly reducing the computational cost.

Unconventional aliphatic fluorophores discovered as the luminescence origin in citric acid-urea carbon dots.

Carbon dots (CDs) are emerging as the material of choice in a range of applications due to their excellent photoluminescence properties, ease of preparation from inexpensive precursors, and low toxicity. However, the precise nature of the mechanism for the fluorescence is still under debate, and several molecular fluorophores have been reported. In this work, a new blue fluorophore, 5-oxopyrrolidine-3-carboxylic acid, was discovered in carbon dots synthesized from the most commonly used precursors: citric acid and urea. The molecular product alone has demonstrated interesting aggregation-enhanced emission (AEE), making it unique compared to other fluorophores known to be generated in CDs. We propose that this molecular fluorophore is associated with a polymer backbone within the CDs, and its fluorescence behavior is largely dependent on intermolecular interactions with the polymers or other fluorophores. Thus, a new class of non-traditional fluorophores is now relevant to the consideration of the CD fluorescence mechanism, providing both an additional challenge to the community in resolving the mechanism and an opportunity for a greater range of CD design schemes and applications.

Biosynthesis, Gene Expression, and Pharmacological Properties of Triterpenoids of Ganoderma Species (Agaricomycetes): A Review.

For more than 6 millennia, Ganoderma species have been used in traditional Asian medicine due to their health benefits. Ganoderma synthesizes several compounds with biological activity, including lanostane-type triterpenoids like ganoderic acids (GAs), lucidones, and colosolactones. These triterpenoids have been investigated for their antiviral, hypoglycemic, and anticancer effects. GAs are highly oxygenated triterpenoids with different functional groups attached to lanostane skeleton. Their great chemical diversity makes GAs prospects for the development of new drugs to treat multiple illnesses such as cancer. The effect of GAs against cancer cells has been associated with their capability to inhibit specific targets such as STAT3, to induce apoptosis and cell cycle blockage, and to increase natural killer cell activity. Due to the biological activity of these molecules, novel strategies are being developed for Ganoderma production mainly by liquid cultivation, gene overexpression (HMGR, SQS, LS) by elicitors, and modified growing conditions (carbon and nitrogen sources, pH, temperature), which induce reactive oxygen species production, key compounds for secondary metabolism. In addition, some transcription factors are mainly expressed under stress conditions, such as cytochrome P450 genes, which participate in the regulation of triterpenoid synthesis. The fermentation process has been scaled up to a 300-L bioreactor, which shows good GA production. This article reviews current knowledge on bioactive triterpenoids of Ganoderma and their production, biosynthesis, and pharmacological properties, emphasizing gene expression in liquid culture. It also discusses the lack of information regarding other species with high potential.

Benchmarking Adaptive Steered Molecular Dynamics (ASMD) on CHARMM Force Fields.

The potentials of mean force (PMFs) along the end-to-end distance of two different helical peptides have been obtained and benchmarked using the adaptive steered molecular dynamics (ASMD) method. The results depend strongly on the choice of force field driving the underlying all-atom molecular dynamics, and are reported with respect to the three most popular CHARMM force field versions: c22, c27 and c36. Two small peptides, ALA 10 and 1PEF, serve as the particular case studies. The comparisons between the versions of the CHARMM force fields provides both a qualitative and quantitative look at their performance in forced unfolding simulations in which peptides undergo large changes in structural conformations. We find that ASMD with the underlying c36 force field provides the most robust results for the selected benchmark peptides.

Molecular Structure of Single-Stranded DNA on the ZnS Surface of Quantum Dots.

DNA-based nanoparticle assemblies have emerged as leading candidates in the development of bioimaging materials, photonic devices, and computing materials. Here, we combine atomistic simulations and experiments to characterize the wrapping mechanism of chimeric single-stranded DNA (ssDNA) on CdSe-ZnS (core-shell) quantum dots (QDs) at different ratios of the phosphorothioate (PS) modification of the bases. We use an implicit solvent, all-atom ssDNA model to match the experimentally calculated ssDNA conformation at low salt concentrations. Through simulation, we find that 3-mercaptopropionic acid (MPA) induces electrostatic repulsion and O-(2-mercaptoethyl)-Ó-methyl-hexa (ethylene glycol) (mPEG) induces steric exclusion, and both reduce the binding affinity of ssDNA. In both simulation and experiment, we find that ssDNA is closer to the QD surface when the QD size is larger. The effect of the PS-base ratio on the conformation of ssDNA is also elaborated in this work. We found through MD simulations, and confirmed by transmission electron microscopy, that the maximum valence numbers are 1, 2, and 3 on QDs of 6, 9, and 14 nm in diameter, respectively. We conclude that the maximum ssDNA valence number is linearly related to the QD size, n ∝ R, and justify this finding through an electrostatic repulsion mechanism.

Phenolic compounds of Phellinus spp. with antibacterial and antiviral activities.

Phellinus Quél is one of the largest genera of Hymenochaetaceae; it comprises about 220 species widely distributed on Earth. Most Phellinus species are lignicolous mushrooms that accumulate bioactive compounds. This research studied the phenolic composition of Phellinus spp. and their relationship with antibacterial and antiviral capacity. Phenolics were extracted from Phellinus badius, P. fastuosus, and P. grenadensis; their antiviral and antibacterial activities were evaluated against Listeria monocytogenes, Staphylococcus aureus, Salmonella enterica, and Escherichia coli O157: H7; and the bacteriophages MS2 and Φ- × 174. Gallic acid, chlorogenic acid, caffeic acid, epicatechin, ferulic acid, catechin, 1,3-dicaffeoylquinic acid, p-coumaric acid, and rutin were found in different proportions among Phellinus spp. Total phenolic content ranged from 96 to 209 mg GAE/g, and total flavonoids from 10 to 27 QE/g. The minimum inhibitory concentrations of P. badius, P. grenadensis, and P. fastuosus against E. coli O157: H7 were 13, 20, and 27 mg/mL, against S. enterica were 20, 30, and 15 mg/mL, and against L. monocytogenes were 10, 15, and 25 mg/mL, respectively. The phenolic content was better correlated with the antibacterial effect against E. coli O157: H7 and L. monocytogenes (r = 0.8-0.9), but not against S. enterica (r = 0.05). The antiviral activity of the extracts (0.9 mg/mL) was 29 to 41% against MS2 and 27 to 38% for Φ-X174 virus (r = 0.8-0.9). In silico analysis showed binding energy values of - 7.9 and - 4.8 kcal/mol between the identified phenolic compounds and the M and G proteins of each virus. The antibacterial and antiviral properties of Phellinus species were correlated with the phenolic content.