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The coarse-grained models of poly(ethylene oxide) and poly(propylene oxide) homopolymers and poloxamers in big multipole water (BMW) and MARTINI frameworks
Stockholm University, Faculty of Science, Department of Materials and Environmental Chemistry (MMK).
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Number of Authors: 52020 (English)In: Physical Chemistry, Chemical Physics - PCCP, ISSN 1463-9076, E-ISSN 1463-9084, Vol. 22, no 28, p. 15976-15985Article in journal (Refereed) Published
Abstract [en]

Polyethylene oxide (PEO) and poly(propylene oxide) (PPO), especially their tri-block copolymers PEO-PPO-PEO (poloxamers), have a broad range of applications in biotechnology and medical science. Understanding their specific interactions with biomembranes is the key to unveil the unique features of poloxamers either as membrane-healing or membrane pore-forming agents. Based on the coarse-graining convention of the MARTINI force field and the big multipole water (BMW) model, which has a three charged site topology and can reproduce the correct dipole moment of four-water clusters, we generated coarse-grained (CG) models with analytical and numerical potentials for PEO and PPO homopolymers and poloxamers in dilute solution. The effective bonded interaction potentials between CG beads were determined from the probability distributions of bond lengths, angles and dihedrals that are determined from atomistic simulations. The nonbonded interaction parameters were fine-tuned to reproduce the conformational properties of atomistic PEO and PPO homopolymers and poloxamersviaextensive CG simulations of PEO and PPO homopolymers and poloxamers in a BMW water environment. The reported CG models provide a promising framework for a comprehensive understanding of the microstructural, conformational, and dynamic properties of poloxamers and their delicate interactions with other species in an explicit water environment.

Place, publisher, year, edition, pages
2020. Vol. 22, no 28, p. 15976-15985
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Chemical Sciences
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URN: urn:nbn:se:su:diva-184368DOI: 10.1039/d0cp01006eISI: 000552193500013PubMedID: 32632434OAI: oai:DiVA.org:su-184368DiVA, id: diva2:1473382
Available from: 2020-10-06 Created: 2020-10-06 Last updated: 2022-03-07Bibliographically approved

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Wang, Yong-Lei

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