2022
DOI: 10.1021/acs.biomac.2c00170
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Design of Polypeptides Self-Assembling into Antifouling Coatings: Exploiting Multivalency

Abstract: We propose to exploit multivalent binding of solid-binding peptides (SBPs) for the physical attachment of antifouling polypeptide brushes on solid surfaces. Using a silica-binding peptide as a model SBP, we find that both tandem-repeated SBPs and SBPs repeated in branched architectures implemented via a multimerization domain work very well to improve the binding strength of polypeptide brushes, as compared to earlier designs with a single SBP. At the same time, for many of the designed sequences, either the s… Show more

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Cited by 4 publications
(25 citation statements)
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“…The M domain has previously been reported to be highly thermostable . We have also found this to be the case for the M domain in the context of our previously designed silica-coating proteins Figure d shows that, as expected, the same holds for the M domain in the gold-coating proteins: the spectra do not change when heating from 20 to 95 °C and cooling back to 20 °C (20 °C rev).…”
Section: Resultssupporting
confidence: 83%
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“…The M domain has previously been reported to be highly thermostable . We have also found this to be the case for the M domain in the context of our previously designed silica-coating proteins Figure d shows that, as expected, the same holds for the M domain in the gold-coating proteins: the spectra do not change when heating from 20 to 95 °C and cooling back to 20 °C (20 °C rev).…”
Section: Resultssupporting
confidence: 83%
“…To determine the layer thickness of the B - M - E polypeptide brushes self-assembled onto gold surfaces, we used dynamic light scattering (DLS). As shown earlier for silica nanoparticles and the silica-coating B - M - E polypeptides, bridging interactions may lead to particle clustering at very low concentrations, but at higher concentrations, a fully saturated layer develops, leading to a small but measurable increase in the particle hydrodynamic diameter, from which we can estimate the layer thickness.…”
Section: Resultsmentioning
confidence: 64%
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