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- Effect of Molecular Crowding on Conformation and Interactions of Single-Chain Nanoparticles hal link

Auteur(s): Oberdisse J.(Corresp.), Colmenero Juan, Arbe Arantxa

Conference: ECIS 2019 (European colloid and interface society) (Leuven, BE, 2019-09-11)


Ref HAL: hal-02293156_v1
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Résumé:

Single-chain nanoparticles (SCNPs) are soft disordered nano-objects synthesized by intra-molecular bonding of linear polymer chains with a finite fraction of reactive groups allowing internal crosslinking of the molecule. They may be investigated as a mimick of, e.g., intrinsically disordered proteins, in particular with respect to the effect of the crowded cellular environment on macromolecular properties. Here the conformation of synthetic SCNPs in presence of linear polystyrene crowding molecules has been studied by small-angle neutron scattering under contrast-matching of the crowders[1]. A model describing the scattering of aggregating polydisperse SCNPs (see Figure 1) has been developed, resulting in the determination of the potentially squeezed size of the individual SCNPs within aggregates, their local chain statistics, and the average aggregation number, as a function of crowding. Two different crowders – of low and high molecular weight, respectively – are shown to have a different effect: while long chains tend to impede their aggregation above their overlap concentration, short ones are found to mediate depletion interactions leading to aggregation. Self-imposed crowding within the aggregates has a similar impact on chain conformation independently of the crowding of the surrounding medium. Our results are compared to recent simulations and is hoped to contribute to the microscopic understanding of the phase behavior of soft intrinsically disordered nano-objects, and in particular the effect of crowding on biomacromolecules. Figure 1: Schematic representation of the aggregation process of two SCNPs of monomer numbers i1 and i2 described by the addition of their individual SCNP masses, and obeying the same local chain statistics.References[1] Julian Oberdisse, Marina González-Burgos, Ander Mendia, Arantxa Arbe, Angel J. Moreno, José A. Pomposo, Aurel Radulescu and Juan Colmenero, under (favorable) revision in Macromolecules.