Significant effect of intra-chain distribution of catalytic sites on catalytic activity in “clickase” single-chain nanoparticles

Asenjo-Sanz, Isabel; Claros, Tatiana; González, Edurne; Pinacho-Olaciregui, Jokin; Verde‐Sesto, Ester; Pomposo, José A.

doi:10.1016/j.matlet.2021.130622

Cited by 8 publications

(14 citation statements)

References 15 publications

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“…The Pomposo team investigated the effect of the catalytic site distribution in so-called 'clickase' SCNPs in 2021. [53] They crosslinked a linear poly(oligothylene glycol methacrylate)-co-(acetoacetoxy ethyl methacrylate) with Cu II ions via two different pathways which resulted in an SCNP with a homogeneous distribution of catalytic sites (SCNP1) and an SCNP with local clusters of catalytic sites (SCNP2). Investigating the difference in catalytic performance via a fluorogenic click reaction between 9-(azidomethyl)anthracene and phenylacetylene demonstrated a remarkably increased activity of SCNP2 (95 %) over SCNP1 (65 %).…”

Section: Current State Of the Art In Scnp Catalysismentioning

confidence: 99%

“…Nuclear Overhauser Effect Spectroscopy (NOESY) can be utilised to observe the location within an SCNP where a substrate binds, owing to the proximity of catalyst bound [53] ). Elsevier.…”

Section: Introductionmentioning

confidence: 99%

“… Overview of single chain nanoparticle (SCNP) design considerations for catalysis. Elsevier [53] . American Chemical Society [38,54] …”

Section: Introductionmentioning

confidence: 99%

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Single Chain Nanoparticles in Catalysis

Mundsinger,

Izuagbe,

Tuten

et al. 2023

Angew Chem Int Ed

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Over the last six decades folded polymer chains – so‐called Single Chain Nanoparticles (SCNPs) – have evolved from the mere concept of intramolecularly crosslinked polymer chains to tailored nanoreactors, underpinned by a plethora of techniques and chemistries to tailor and analyze their morphology and function. These monomolecular polymer entities hold critical promise in a wide range of applications. Herein, we highlight the exciting progress that has been made in the field of catalytically active SCNPs within recent years.

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Section: Current State Of the Art In Scnp Catalysismentioning

confidence: 99%

“…Nuclear Overhauser Effect Spectroscopy (NOESY) can be utilised to observe the location within an SCNP where a substrate binds, owing to the proximity of catalyst bound [53] ). Elsevier.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Single Chain Nanoparticles in Catalysis

Mundsinger,

Izuagbe,

Tuten

et al. 2023

Angew Chem Int Ed

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“…Das Team von Pomposo untersuchte 2021 den Effekt der Verteilung katalytischer Zentren in sogenannten "Clickase" EKNPs. [41] Sie vernetzten auf zwei unterschiedliche Arten ein lineares Poly(oligoethylenglycolmethacrylat)-co-(Acetoacetoxyethylmethacrylat) mit Cu II -Ionen, was zu einem EKNP mit einer homogenen Verteilung der katalytischen Zentren (SCNP1) und einem EKNP mit lokalen Clustern von katalytischen Zentren (SCNP2) führte. Die Untersuchung der Unterschiede in der katalytischen Aktivität anhand einer fluorogenen Click-Reaktion zwischen 9-(Azidomethyl)anthracen und Phenylacetylen zeigte eine bemerkenswerte gesteigerte Aktivität von SCNP2 (95 %) gegenüber SCNP1 (65 %).…”

Section: Stand Der Technik In Der Eknp Katalyseunclassified

Einzelkettennanopartikel in der Katalyse

Mundsinger,

Izuagbe,

Tuten

et al. 2023

Angewandte Chemie

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“…A class of synthetic macromolecules that mimic proteins are referred to as single-chain polymer nanoparticles (SCNPs) which can collapse into higher ordered structures. [10][11][12][13][14][15][16][17][18] These nanoparticles have potential as enzyme 19,20 or protein mimetics, 16,21,22 sensing nanomaterials, 23 and drug delivery systems. [24][25][26] Of particular interest is the use of SCNPs as multivalent therapeutics because of their biophysical tunability and therefore ligand presentation.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Design of Protein-Like Single-Chain Polymer Nanoparticles

Upadhya

Tamasi

Mare

et al. 2022

Preprint

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The functional structure of proteins is heavily influenced by their folding behavior. AlphaFold, a powerful artificial intelligence (AI) program trained on information from the Protein Data Bank (PDB), was developed to predict the 3D structure of proteins from its amino acid sequence. Inspired by this, we aim to elucidate structural features of synthetic single-chain polymer nanoparticles (SCNPs) based on compositional information (monomers, chain length, molecular weight, charge, and valency) by machine learning (ML). Specifically, we demonstrate the effectiveness of ML to improve the efficiency of SCNP design and uncover important polymer design attributes to mimic protein-like structural features. To start, we randomly screened over 1000 synthesized SCNPs through a combination of high-throughput dynamic light scattering (DLS) and small-angle X-ray scattering (SAXS) and compared these results to simulated protein data from the PDB. Then, utilizing evidential neural networks (ENets), we predicted, synthesized, and characterized 30 novel compact SCNPs. Incredibly, this data-driven approach yielded 58% of the predicted SCNPs with Porod exponent ≥ 3.5 as opposed to 5% of SCNPs from the random screen. Using Shapely additive explanation (SHAP) values, we further uncovered interesting contributions of monomer content on Porod exponent and radius of gyration. From this work, we have shown that an ML-guided approach proves effective for the challenging, unintuitive problem of nanoparticle design.

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Significant effect of intra-chain distribution of catalytic sites on catalytic activity in “clickase” single-chain nanoparticles

Cited by 8 publications

References 15 publications

Single Chain Nanoparticles in Catalysis

Single Chain Nanoparticles in Catalysis

Einzelkettennanopartikel in der Katalyse

Data-Driven Design of Protein-Like Single-Chain Polymer Nanoparticles

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