Photoactivated Multivariate Metal-Organic Frameworks for On-Demand Drug Release: The Role of Host-Guest Interactions.

The development of smart drug delivery vehicles capable of controlled release upon application of an external stimulus is of paramount interest for the next generation of personalized medicine. Herein, we report a series of six multivariate (MTV) MOFs capable of visible light-activated drug delivery. The drug loading capacity and release rates were systematically tuned through variation of the linker ratio between 4,4'-azobenzene dicarboxylic acid (HABDA) and 4,4'-(diazene-1,2-diyl)bis(3,5-difluorobenzoic acid) (HABDA(3,5-F)).

Systematic Evaluation of Macromolecular Carbohydrate-Lectin Recognition Using Precision Glycopolymers.

The precise modulation of protein-carbohydrate interactions is critical in glycobiology, where multivalent binding governs key cellular processes. As such, synthetic glycopolymers are useful for probing these interactions. Herein, we developed precision glycopolymers (PGPs) with unambiguous local chemical composition and well-defined global structure and systematically evaluated the effect of polymer length, hydrophobicity, and backbone hybridization as well as glycan density and identity on the binding to both mammalian and plant lectins.

Evaluation of Sampling Algorithms Used for Bayesian Uncertainty Quantification of Molecular Dynamics Force Fields.

New Bayesian parameter estimation methods have the capability to enable more physically realistic and reliable molecular dynamics (MD) simulations by providing accurate estimates of uncertainties of force-field (FF) parameters and associated properties. However, the choice of which Bayesian parameter estimation algorithm to use has not been widely investigated, despite its impact on the effective exploration of parameter space. Here, using a case example of the Embedded Atom Method (EAM) FF parameters, we investigated the ramifications of several of the algorithm choices.

Probing the role of protein conformational changes in the mechanism of prenylated-FMN-dependent phenazine-1-carboxylic acid decarboxylase.

Phenazine-1-carboxylic acid decarboxylase (PhdA) is a prenylated-FMN-dependent (prFMN) enzyme belonging to the UbiD family of decarboxylases. Many UbiD-like enzymes catalyze (de)carboxylation reactions on aromatic rings and conjugated double bonds and are potentially valuable industrial catalysts. We have investigated the mechanism of PhdA using a slow turnover substrate, 2,3-dimethylquinoxaline-5-carboxylic acid (DQCA).

Data Driven Computational Design and Experimental Validation of Drugs for Accelerated Mitigation of Pandemic-like Scenarios.

Emerging pathogens are a historic threat to public health and economic stability. Current trial-and-error approaches to identify new therapeutics are often ineffective due to their inefficient exploration of the enormous small molecule design space. Here, we present a data-driven computational framework composed of hybrid evolutionary algorithms for evolving functional groups on existing drugs to improve their binding affinity toward the main protease (M) of SARS-CoV-2.

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption.

Hydrogen gas (H) is a clean and renewable energy source, but the lack of efficient and cost-effective storage materials is a challenge to its widespread use. Metal-organic frameworks (MOFs), a class of porous materials, have been extensively studied for H storage due to their tunable structural and chemical features. However, the large design space offered by MOFs makes it challenging to select or design appropriate MOFs with a high H storage capacity.

Development, Validation, and Applications of Nonbonded Interaction Parameters between Coarse-Grained Amino Acid and Water Models.

Interactions between amino acids and water play an important role in determining the stability and folding/unfolding, in aqueous solution, of many biological macromolecules, which affects their function. Thus, understanding the molecular-level interactions between water and amino acids is crucial to tune their function in aqueous solutions. Herein, we have developed nonbonded interaction parameters between the coarse-grained (CG) models of 20 amino acids and the one-site CG water model.

Supramolecular Peptide Nanostructures Regulate Catalytic Efficiency and Selectivity.

We report three constitutionally isomeric tetrapeptides, each comprising one glutamic acid (E) residue, one histidine (H) residue, and two lysine (K ) residues functionalized with side-chain hydrophobic S-aroylthiooxime (SATO) groups. Depending on the order of amino acids, these amphiphilic peptides self-assembled in aqueous solution into different nanostructures:nanoribbons, a mixture of nanotoroids and nanoribbons, or nanocoils. Each nanostructure catalyzed hydrolysis of a model substrate, with the nanocoils exhibiting the greatest rate enhancement and the highest enzymatic efficiency.

A review of recent advances and applications of machine learning in tribology.

In tribology, a considerable number of computational and experimental approaches to understand the interfacial characteristics of material surfaces in motion and tribological behaviors of materials have been considered to date. Despite being useful in providing important insights on the tribological properties of a system, at different length scales, a vast amount of data generated from these state-of-the-art techniques remains underutilized due to lack of analysis methods or limitations of existing analysis techniques. In principle, this data can be used to address intractable tribological problems including structure-property relationships in tribological systems and efficient lubricant design in a cost and time effective manner with the aid of machine learning.

A combined experimental and computational approach reveals how aromatic peptide amphiphiles self-assemble to form ion-conducting nanohelices.

Reported here is a combined experimental-computational strategy to determine structure-property-function relationships in persistent nanohelices formed by a set of aromatic peptide amphiphile (APA) tetramers with the general structure , where K= -aroylthiooxime modified lysine, X = glutamic acid or citrulline, and E = glutamic acid. In low phosphate buffer concentrations, the APAs self-assembled into flat nanoribbons, but in high phosphate buffer concentrations they formed nanohelices with regular twisting pitches ranging from 9-31 nm. Coarse-grained molecular dynamics simulations mimicking low and high salt concentrations matched experimental observations, and analysis of simulations revealed that increasing strength of hydrophobic interactions under high salt conditions compared with low salt conditions drove intramolecular collapse of the APAs, leading to nanohelix formation.

Machine learning approach for accurate backmapping of coarse-grained models to all-atom models.

Four different machine learning (ML) regression models: artificial neural network, k-nearest neighbors, Gaussian process regression and random forest were built to backmap coarse-grained models to all-atom models. The ML models showed better predictions than the existing backmapping approaches for selected structures, suggesting the applications of the ML models for backmapping.

Solvation dynamics of N-substituted acrylamide polymers and the importance for phase transition behavior.

Functional groups present in thermo-responsive polymers are known to play an important role in aqueous solutions by manifesting their coil-to-globule conformational transition in a specific temperature range. Understanding the role of these functional groups and their interactions with water is of great interest as it may allow us to control both the nature and temperature of this coil-to-globule transition. In this work, polyacrylamide (PAAm), poly(N-isopropylacrylamide) (PNIPAm), and poly(N-isopropylmethacrylamide) (PNIPMAm) solvated in water are studied with the goal of discovering the structure of the solvent and its interaction with these polymers in determining the polymer conformations.

Machine-Learning Based Stacked Ensemble Model for Accurate Analysis of Molecular Dynamics Simulations.

Accurate, faster, and on-the-fly analysis of the molecular dynamics (MD) simulations trajectory becomes very critical during the discovery of new materials or while developing force-field parameters due to automated nature of these processes. Here to overcome the drawbacks of algorithm based analysis approaches, we have developed and utilized an approach that integrates machine-learning (ML) based stacked ensemble model (SEM) with MD simulations, for the first time. As a proof-of-concept, two SEMs were developed to analyze two dynamical properties of a water droplet, its contact angle, and hydrogen bonds.

Development of Transferable Nonbonded Interactions between Coarse-Grained Hydrocarbon and Water Models.

Interactions between water and hydrocarbons play a significant role in chemical, physical, and biological processes. Here, we present a set of force-field (FF) parameters that define the interactions between coarse-grained (CG) hydrocarbon models ( An , Y. J.

Machine-Learning Enabled New Insights into the Coil-to-Globule Transition of Thermosensitive Polymers Using a Coarse-Grained Model.

We present a computational framework that integrates coarse-grained (CG) molecular dynamics (MD) simulations and a data-driven machine-learning (ML) method to gain insights into the conformations of polymers in solutions. We employ this framework to study conformational transition of a model thermosensitive polymer, poly( N-isopropylacrylamide) (PNIPAM). Here, we have developed the first of its kind, a temperature-independent CG model of PNIPAM that can accurately predict its experimental lower critical solution temperature (LCST) while retaining the tacticity in the presence of an explicit water model.

Machine-Learned Coarse-Grained Models.

Optimizing force-field (FF) parameters to perform molecular dynamics (MD) simulations is a challenging and time-consuming process. We present a novel FF optimization framework that integrates MD simulations with particle swarm optimization (PSO) algorithm and artificial neural network (ANN). This new ANN-assisted PSO framework was used to develop transferable coarse-grained (CG) models for DO and DMF as a proof of concept.

Development of New Transferable Coarse-Grained Models of Hydrocarbons.

We have utilized an approach that integrates molecular dynamics (MD) simulations with particle swarm optimization (PSO) to accelerate the development of coarse-grained (CG) models of hydrocarbons. Specifically, we have developed new transferable CG beads, which can be used to model the hydrocarbons (C5 to C17) and reproduce their experimental properties with good accuracy. First, the PSO method was used to develop the CG beads of the decane model represented with a 2:1 (2-2-2-2-2) mapping scheme.

PSO-Assisted Development of New Transferable Coarse-Grained Water Models.

We have employed two-to-one mapping scheme to develop three coarse-grained (CG) water models, namely, 1-, 2-, and 3-site CG models. Here, for the first time, particle swarm optimization (PSO) and gradient descent methods were coupled to optimize the force-field parameters of the CG models to reproduce the density, self-diffusion coefficient, and dielectric constant of real water at 300 K. The CG MD simulations of these new models conducted with various timesteps, for different system sizes, and at a range of different temperatures are able to predict the density, self-diffusion coefficient, dielectric constant, surface tension, heat of vaporization, hydration free energy, and isothermal compressibility of real water with excellent accuracy.

Development of non-bonded interaction parameters between graphene and water using particle swarm optimization.

New Lennard-Jones parameters have been developed to describe the interactions between atomistic model of graphene, represented by REBO potential, and five commonly used all-atom water models, namely SPC, SPC/E, SPC/Fw, SPC/Fd, and TIP3P/Fs by employing particle swarm optimization (PSO) method. These new parameters were optimized to reproduce the macroscopic contact angle of water on a graphene sheet. The calculated line tension was in the order of 10 J/m for the droplets of all water models.

Water ordering controls the dynamic equilibrium of micelle-fibre formation in self-assembly of peptide amphiphiles.

Understanding the role of water in governing the kinetics of the self-assembly processes of amphiphilic peptides remains elusive. Here, we use a multistage atomistic-coarse-grained approach, complemented by circular dichroism/infrared spectroscopy and dynamic light scattering experiments to highlight the dual nature of water in driving the self-assembly of peptide amphiphiles (PAs). We show computationally that water cage formation and breakage near the hydrophobic groups control the fusion dynamics and aggregation of PAs in the micellar stage.

In Situ 3D Imaging of Catalysis Induced Strain in Gold Nanoparticles.

Multielectron transfer processes are crucially important in energy and biological science but require favorable catalysts to achieve fast kinetics. Nanostructuring catalysts can dramatically improve their properties, which can be difficult to understand due to strain- and size-dependent thermodynamics, the influence of defects, and substrate-dependent activities. Here, we report three-dimensional (3D) imaging of single gold nanoparticles during catalysis of ascorbic acid decomposition using Bragg coherent diffractive imaging (BCDI).

Metal-induced rapid transformation of diamond into single and multilayer graphene on wafer scale.

The degradation of intrinsic properties of graphene during the transfer process constitutes a major challenge in graphene device fabrication, stimulating the need for direct growth of graphene on dielectric substrates. Previous attempts of metal-induced transformation of diamond and silicon carbide into graphene suffers from metal contamination and inability to scale graphene growth over large area. Here, we introduce a direct approach to transform polycrystalline diamond into high-quality graphene layers on wafer scale (4 inch in diameter) using a rapid thermal annealing process facilitated by a nickel, Ni thin film catalyst on top.

Subnanometre ligand-shell asymmetry leads to Janus-like nanoparticle membranes.

Self-assembly of nanoparticles at fluid interfaces has emerged as a simple yet efficient way to create two-dimensional membranes with tunable properties. In these membranes, inorganic nanoparticles are coated with a shell of organic ligands that interlock as spacers and provide tensile strength. Although curvature due to gradients in lipid-bilayer composition and protein scaffolding is a key feature of many biological membranes, creating gradients in nanoparticle membranes has been difficult.

Friction. Macroscale superlubricity enabled by graphene nanoscroll formation.

Friction and wear remain as the primary modes of mechanical energy dissipation in moving mechanical assemblies; thus, it is desirable to minimize friction in a number of applications. We demonstrate that superlubricity can be realized at engineering scale when graphene is used in combination with nanodiamond particles and diamondlike carbon (DLC). Macroscopic superlubricity originates because graphene patches at a sliding interface wrap around nanodiamonds to form nanoscrolls with reduced contact area that slide against the DLC surface, achieving an incommensurate contact and substantially reduced coefficient of friction (~0.