Thermodynamics of Self-Assembly and Supramolecular Transitions Using Enhanced Sampling

Langmuir, Год журнала: 2025, Номер unknown

Опубликована: Июнь 2, 2025

Computational studies of self-assembly have the potential to provide rich insights into their underlying thermodynamics and identify optimal system conditions for applications such as nanomaterial synthesis or drug delivery. However, both supramolecular transitions can be hindered by free energy barriers, rendering them rare events on molecular time scales making it challenging sample them. Here, we show that use enhanced sampling techniques, when combined with a judiciously chosen set order parameters, offers an efficient robust route characterizing transitions. Specifically, between states different periodicities symmetries reversibly sampled biasing relatively small number Fourier components particle density. We illustrate our approach computing required cleave liquid slab estimating corresponding liquid-vapor surface tension. also characterize energetics transition spherical rod-shaped droplets. These results serve first step toward development systematic computational framework exploring in diverse systems, surfactants block copolymers, self-assembly.

Язык: Английский

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Confinement of folding motifs within central blocks improves single chain polymer nanoparticle folding

Polymer Chemistry, Год журнала: 2023, Номер 15(4), С. 332 - 340

Опубликована: Дек. 19, 2023

Controlling the location of a folding motif within backbone polymer chain can dramatically improve into single nanoparticles (SCNPs).

Язык: Английский

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Property-Guided Generation of Complex Polymer Topologies Using Variational Autoencoders

Опубликована: Фев. 20, 2024

The complexity and diversity of polymer topologies, or chain architectures, present substantial challenges in predicting engineering properties. Although machine learning is increasingly used science, applications to address architecturally complex polymers are nascent. Here, we use a generative model based on variational autoencoders data generated from molecular dynamics simulations design topologies that exhibit target Following the construction dataset featuring 1,342 with linear, cyclic, branch, comb, star, dendritic structures, employ multi-task framework effectively reconstructs classifies while their dilute-solution radii gyration. This enables generation novel size, which subsequently validated through simulation. These capabilities then exploited contrast rheological properties topologically distinct otherwise similar behavior. research opens new avenues for more intricate tailored learning.

Язык: Английский

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A High-Throughput Workflow to Analyze Sequence-Conformation Relationships and Explore Hydrophobic Patterning in Disordered Peptoids

Опубликована: Авг. 12, 2024

Understanding how a macromolecule’s primary sequence governs its conformational landscape is crucial for elucidating function, yet these design principles are still emerging macromolecules with intrinsic disorder. Herein, we introduce high-throughput workflow that implements practical colorimetric assay, introduces semi-automated sequencing protocol using MALDI-MS/MS, and develops generalizable sequence-structure algorithm. Using model system of 20mer peptidomimetics containing polar glycine hydrophobic N-butylglycine residues, identified nine classifications disorder isolated 122 unique sequences across varied compositions conformations. Conformational distributions three compositionally identical library were corroborated through atomistic simulations ion mobility spectrometry coupled liquid chromatography. A data-driven strategy was developed existing variables data-derived ‘motifs’ to inform machine learning algorithm towards conformation prediction. This multifaceted approach enhances our understanding sequence-conformation relationships offers powerful tool accelerating the discovery materials control.

Язык: Английский

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A High-Throughput Workflow to Analyze Sequence-Conformation Relationships and Explore Hydrophobic Patterning in Disordered Peptoids

Опубликована: Окт. 4, 2023

Understanding how a macromolecule’s primary sequence governs its conformational landscape is crucial for elucidating function, yet these design principles are still emerging macromolecules with intrinsic disorder. While parameters describing subsets of disordered proteins and synthetic materials have been established, they often tailored to specific chemical interactions monomer classes, limiting their broader applicability. To address this gap, we present high-throughput workflow that implements versatile colorimetric assay, introduces semi-automated sequencing protocol using MALDI-MS/MS, pioneers data-driven parameterization methodology integrates into predictive algorithm. Using model system consisting two-component peptidomimetics (20mer peptoids) containing polar glycine hydrophobic N-butylglycine residues in one-bead one-compound (OBOC) library, visually identified nine classifications From 122 unique sequences across varied compositions conformations, developed an image analysis tool ultimately characterized order magnitude larger fraction the complete library. Low-throughput techniques, atomistic simulations ion mobility spectrometry coupled liquid chromatography mass separations (LC-IMS-MS) purified peptoids, yielded quantitative descriptors ensembles formed by three compositionally identical selected from Finally, technique was exploits ‘motifs’ within 20mer inform gradient-boosted tree machine learning algorithm towards conformation prediction. This multifaceted approach enhances our understanding sequence-conformation relationships offers powerful accelerating discovery development advanced precise control.

Язык: Английский

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Uniting Sequence, Structure, and Function in Amphiphilic Di(phenylalanine)-Based Copolymers for Rare Earth Element Sequestration

Опубликована: Ноя. 10, 2023

Sequence, structure, and function are inherently intertwined. While well-established relationships exist in proteins, they more challenging to define for synthetic polymer nanoparticles due their molecular weight, sequence, conformational dispersities. To explore the impact of sequence on nanoparticle structure function, we synthesized a set sixteen compositionally identical, yet distinct, block copolymers comprising dimethyl acrylamide bioinspired, structure-driving di(phenylalanine) (FF) monomer. Systematic analysis global (tertiary- qua-ternary-like) this amphiphilic copolymer series revealed effect multiple descriptors. The number blocks, hydropathy terminal patchiness (density) FF within impacted both chain collapse distribution single- multi-chain assemblies. Further, freedom segments local-scale, β-sheet-like inter-actions were sensitive FF. connect target evaluated an additional nine sequence-controlled as sequestrants rare earth elements (REEs) by incorporating functional acrylic acid monomer into scaffolds, guided original series. We identified key variables that influence binding affinity, capacity, selectivity polymers REEs. Collectively, these results highlight potential control tune hierarchical structures ultimately dictate functionality polymeric materials, without altering composition.

Язык: Английский

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Development of photoresponsive metal based single-chain nanoparticles

Опубликована: Янв. 1, 2024

A precise interplay exists between the macromolecular scaffold and catalytically active metal centres within enzymes, resulting in nature's ultimate catalysts. Although replicating precision of enzymes laboratory is challenging, quest for enzyme-like catalysis has led to catalytic —often metal-based — single-chain nanoparticles (SCNPs). SCNPs can be enhanced with functionalities not found critically, photoresponsivity. The current thesis focuses on incorporating photoswitches into metal-containing SCNPs. Here, photo-induced isomerization leads distinct morphological changes, affecting access that act as either or structure-forming elements

Язык: Английский

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A high-throughput workflow to analyze sequence-conformation relationships and explore hydrophobic patterning in disordered peptoids

Chem, Год журнала: 2024, Номер 10(11), С. 3444 - 3458

Опубликована: Сен. 6, 2024

Язык: Английский

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Einzelkettennanopartikel in der Katalyse

Angewandte Chemie, Год журнала: 2023, Номер 136(7)

Опубликована: Окт. 19, 2023

Abstract In den letzten sechs Jahrzehnten haben sich gefaltete Polymerketten, sogenannte Einzelketten‐Nanopartikel (EKNPs), von dem bloßen Konzept intramolekular vernetzter Polymerketten zu maßgeschneiderten Nanoreaktoren entwickelt. Diese Entwicklung wurde durch eine Vielzahl Techniken und chemischen Verfahren zur Anpassung Analyse ihrer Morphologie Funktion unterstützt. monomolekularen Polymere bieten in einer breiten Palette Anwendungen ein vielversprechendes Potenzial. diesem Beitrag diskutieren wir die faszinierenden Fortschritte, Jahren im Bereich der katalytisch aktiven EKNPs erzielt wurden.

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