Dataset Information

Synthetic Receptors Based on Abiotic Cyclo(pseudo)peptides.

ABSTRACT: Work on the use of cyclic peptides or pseudopeptides as synthetic receptors started even before the field of supramolecular chemistry was firmly established. Research initially focused on the development of synthetic ionophores and involved the use of macrocycles with a repeating sequence of subunits along the ring to facilitate the correlation between structure, conformation, and binding properties. Later, nonnatural amino acids as building blocks were also considered. With growing research in this area, cyclopeptides and related macrocycles developed into an important and structurally diverse receptor family. This review provides an overview of these developments, starting from the early years. The presented systems are classified according to characteristic structural elements present along the ring. Wherever possible, structural aspects are correlated with binding properties to illustrate how natural or nonnatural amino acids affect binding properties.

SUBMITTER: Kubik S

PROVIDER: S-EPMC9103335 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Synthetic Receptors Based on Abiotic Cyclo(pseudo)peptides.

Kubik Stefan S

Molecules (Basel, Switzerland) 20220428 9

Work on the use of cyclic peptides or pseudopeptides as synthetic receptors started even before the field of supramolecular chemistry was firmly established. Research initially focused on the development of synthetic ionophores and involved the use of macrocycles with a repeating sequence of subunits along the ring to facilitate the correlation between structure, conformation, and binding properties. Later, nonnatural amino acids as building blocks were also considered. With growing research in ...[more]

PMID: 35566168

Similar Datasets

Project description: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.

Dataset Information

Synthetic Receptors Based on Abiotic Cyclo(pseudo)peptides.

Publications

Synthetic Receptors Based on Abiotic Cyclo(pseudo)peptides.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets