Abstract
The development of novel building blocks which are capable to self-assemble and the design of artificial receptors for a given substrate are two main goals in supramolecular chemistry. Herein, we describe the synthesis and evaluation of a de novo designed new binding motif for carboxylates in polar solution, the guanidiniocarbonyl pyrroles. The thermodynamic properties of this new receptor class were intensively studied both experimentally by NMR, X-ray and ESI MS, and theoretically using molecular modelling. These studies led to a detailled structural and thermodynamic understanding of the recognition event. Futhermore, a comparative thermodynamic study allowed us to arrive at a supramolecular structure-binding relationship (SSBR) for the complexation of carboxylates by this receptor class. Based on this novel recognition motif, new supramolecular structures can be designed. Self-complementary guanidiniocarbonyl pyrrole / carboxylate zwitterions form intramolecular loops, dimers or supramolecular oligomers depending on the molecular structure of the zwitterion and the experimental conditions. Guanidiniocarbonyl pyrrole cations can also be used as receptors for the side chain selective- and stereoselective complexation of amino acids in aqueous solvents. By incorporating such guanidiniocarbonyl pyrrole units into a combinatorial receptor library, one can also identify receptors for the effective complexation of biologically relevant oligopetides.
Call for Papers in Thematic Issues
Advances of Heterocyclic Chemistry with Pesticide Activity
Global food safety and security will continue to be a global concern for the next 50 years and beyond. Plant diseases have had a significant impact on food safety and security throughout the entire food chain, from primary production to consumption. While conventional chemical pesticides have been traditionally used for ...read more
Calculation design of covalent/metal organic framework based catalysts
This research area combines theoretical computation and screening with machine learning for the design of covalent/metal organic framework-based catalysts, bridging the disciplines of organic chemistry, physical chemistry, computational chemistry, materials science, and machine learning. It covers several critical aspects: designing and synthesizing organic catalysts for improved performance, applying computational methods ...read more
Carbohydrates conversion in biofuels and bioproducts
Biomass pretreatment, hydrolysis, and saccharification of carbohydrates, and sugars bioconversion in biofuels and bioproducts within a biorefinery framework. Carbohydrates derived from woody biomass, agricultural wastes, algae, sewage sludge, or any other lignocellulosic feedstock are included in this issue. Simulation, techno-economic analysis, and life cycle analysis of a biorefinery process are ...read more
Catalytic C-H bond activation as a tool for functionalization of heterocycles
The major topic is the functionalization of heterocycles through catalyzed C-H bond activation. The strategies based on C-H activation not only provide straightforward formation of C-C or C-X bonds but, more importantly, allow for the avoidance of pre-functionalization of one or two of the cross-coupling partners. The beneficial impact of ...read more
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