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Subject: Scaffolds (biology) × WGL Institute: DWI ×

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    Bicyclic RGD peptides enhance nerve growth in synthetic PEG-based Anisogels
    (Cambridge : RSC, 2021) Vedaraman, Sitara; Bernhagen, Dominik; Haraszti, Tamas; Licht, Christopher; Castro Nava, Arturo; Omidinia Anarkoli, Abdolrahman; Timmerman, Peter; De Laporte, Laura
    Nerve regeneration scaffolds often consist of soft hydrogels modified with extracellular matrix (ECM) proteins or fragments, as well as linear and cyclic peptides. One of the commonly used integrin-mediated cell adhesive peptide sequences is Arg-Gly-Asp (RGD). Despite its straightforward coupling mechanisms to artificial extracellular matrix (aECM) constructs, linear RGD peptides suffer from low stability towards degradation and lack integrin selectivity. Cyclization of RGD improves the affinity towards integrin subtypes but lacks selectivity. In this study, a new class of short bicyclic peptides with RGD in a cyclic loop and 'random screened' tri-amino acid peptide sequences in the second loop is investigated as a biochemical cue for cell growth inside three-dimensional (3D) synthetic poly(ethylene glycol) (PEG)-based Anisogels. These peptides impart high integrin affinity and selectivity towards either αvβ3 or α5β1 integrin subunits. Enzymatic conjugation of such bicyclic peptides to the PEG backbone enables the formulation of an aECM hydrogel that supports nerve growth. Furthermore, different proteolytic cleavable moieties are incorporated and compared to promote cell migration and proliferation, resulting in enhanced cell growth with different degradable peptide crosslinkers. Mouse fibroblasts and primary nerve cells from embryonic chick dorsal root ganglions (DRGs) show superior growth in bicyclic RGD peptide conjugated gels selective towards αvβ3 or α5β1, compared to monocyclic or linear RGD peptides, with a slight preference to αvβ3 selective bicyclic peptides in the case of nerve growth. Synthetic Anisogels, modified with bicyclic RGD peptides and containing short aligned, magneto-responsive fibers, show oriented DRG outgrowth parallel to the fibers. This report shows the potential of PEG hydrogels coupled with bicyclic RGD peptides as an aECM model and paves the way for a new class of integrin selective biomolecules for cell growth and nerve regeneration.
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    De novo rational design of a freestanding, supercharged polypeptide, proton-conducting membrane
    (Washington : American Association for the Advancement of Science (A A A S), 2020) Ma, Chao; Dong, Jingjin; Viviani, Marco; Tulini, Isotta; Pontillo, Nicola; Maity, Sourav; Zhou, Yu; Roos, Wouter H.; Liu, Kai; Herrmann, Andreas; Portale, Giuseppe
    Proton translocation enables important processes in nature and man-made technologies. However, controlling proton conduction and fabrication of devices exploiting biomaterials remains a challenge. Even more difficult is the design of protein-based bulk materials without any functional starting scaffold for further optimization. Here, we show the rational design of proton-conducting, protein materials exceeding reported proteinaceous systems. The carboxylic acid-rich structures were evolved step by step by exploring various sequences from intrinsically disordered coils over supercharged nanobarrels to hierarchically spider β sheet containing protein-supercharged polypeptide chimeras. The latter material is characterized by interconnected β sheet nanodomains decorated on their surface by carboxylic acid groups, forming self-supportive membranes and allowing for proton conduction in the hydrated state. The membranes showed an extraordinary proton conductivity of 18.5 ± 5 mS/cm at RH = 90%, one magnitude higher than other protein devices. This design paradigm offers great potential for bioprotonic device fabrication interfacing artificial and biological systems. Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).