Synthetic Diversity and Catalytic Mechanism of Peptide Dendrimers

Peptide dendrimers composed of alternating sequences of natural amino acids and branching diamino acids are investigated as synthetic enzyme models. The dendrimers can be prepared by solid-phase peptide synthesis and are obtained pure in yields of 5–35%. Peptide dendrimers with surface histidine residues catalyze ester hydrolysis reaction with enzyme-like kinetics, including substrate binding (KM), catalytic turnover (kcat), and rate acceleration kcat/kuncat = 1000–20'000. Mechanistic investigation by substrate variation, pH-profile, and isothermaltitration calorimetry show that the catalytic effect is caused by positive interaction between the histidine side-chains and creation of a hydrophobic microenvironment for substrate binding

Synthetic Diversity and Catalytic Mechanism of Peptide Dendrimers

Peptide dendrimers composed of alternating sequences of natural amino acids and branching diamino acids are investigated as synthetic enzyme models. The dendrimers can be prepared by solid-phase peptide synthesis and are obtained pure in yields of 5–35%. Peptide dendrimers with surface histidine residues catalyze ester hydrolysis reaction with enzyme-like kinetics, including substrate binding (KM), catalytic turnover (kcat), and rate acceleration kcat/kuncat = 1000–20'000. Mechanistic investigation by substrate variation, pH-profile, and isothermaltitration calorimetry show that the catalytic effect is caused by positive interaction between the histidine side-chains and creation of a hydrophobic microenvironment for substrate binding

Volatile Composition of Oyster Leaf (Mertensia maritima (L.) Gray)

Oyster leaf (Mertensia maritima), also called vegetarian oyster, has a surprising oyster-like aroma. Its volatile composition was investigated here for the first time. In total, 109 compounds were identified by gas chromatography–mass spectrometry (GC-MS) and quantified by GC-FID. The use of GC–olfactometry on both polar and nonpolar columns allowed the detection of the molecules having an oyster-like, marine odor. Four compounds were identified and confirmed by synthesis: (<i>Z</i>)-3-nonenal, (<i>Z</i>)-1,5-octadien-3-ol, (<i>Z</i>,<i>Z</i>)-3,6-nonadienal, and (<i>Z</i>)-1,5-octadien-3-one. After evaluation of freshly prepared reference samples, these compounds were confirmed to be reminiscent of the oyster-like marine notes perceived in the tasting of cut leaves

Integrating metabolomic data from multiple analytical platforms for a comprehensive characterisation of lemon essential oils

Citrus cold pressed oils are of great importance to the flavour and fragrance industry. Because of their high added value, careful attention must be paid to ensure the oils' genuineness and authenticity. Characterising their chemical complexity in a holistic perspective constitutes a potent way to relate specific compounds to the organoleptic properties of interest and to assess their quality. In this context, a complete characterisation using untargeted metabolomics represents an analytical challenge. The present study takes advantage of multiblock data modelling to integrate heterogeneous signals collected from GC-FID, 1H-NMR, UHPLC-TOF/MS- (negative mode) and UHPLC-TOF/MS+ (positive mode) platforms to obtain a complete characterisation of 64 samples of cold pressed lemon oil (CPLO). Two statistical approaches (MB-PLS-DA and Consensus OPLS-DA) were used to classify the samples according to their extraction processes [i.e. Sfumatrice, Food Machinery Corporation Inline Extractor (FMC), Brown Oil Extractor (BOE), Pelatrice, or mixed FMC + Pelatrice]. Furthermore, the multiblock strategy allows links between variables from different analytical methods to be drawn easily and facilitates the identification of compounds. Because citrus oils extracted using the Sfumatrice process constitute the reference quality from an organoleptic point of view, these samples were characterised thoroughly and are reported to contain less fatty acids but more sesquiterpenes and furocoumarins compared with products obtained using other extraction processes

Comprehensive profiling and marker identification in non-volatile citrus oil residues by mass spectrometry and nuclear magnetic resonance

The detailed characterization of cold-pressed lemon oils (CPLOs) is of great importance for the flavor and fragrance (F&F) industry. Since a control of authenticity by standard analytical techniques can be bypassed using elaborated adulterated oils to pretend a higher quality, a combination of advanced orthogonal methods has been developed. The present study describes a combined metabolomic approach based on UHPLC–TOF-MS profiling and 1H NMR fingerprinting to highlight metabolite differences on a set of representative samples used in the F&F industry. A new protocol was set up and adapted to the use of CPLO residues. Multivariate analysis based on both fingerprinting methods showed significant chemical variations between Argentinian and Italian samples. Discriminating markers identified in mixtures belong to furocoumarins, flavonoids, terpenoids and fatty acids. Quantitative NMR revealed low citropten and high bergamottin content in Italian samples. The developed metabolomic approach applied to CPLO residues gives some new perspectives for authenticity assessment

Differentiation of lemon essential oil based on volatile and non-volatile fractions with various analytical techniques: a metabolomic approach

Due to the importance of citrus lemon oil for the industry, fast and reliable analytical methods that allow the authentication and/or classification of such oil, using the origin of production or extraction process, are necessary. To evaluate the potential of volatile and non-volatile fractions for classification purposes, volatile compounds of cold-pressed lemon oils were analyzed, using GC-FID/MS and FT-MIR, while the non-volatile residues were studied, using FT-MIR, 1H-NMR and UHPLC-TOF-MS. 64 Lemon oil samples from Argentina, Spain and Italy were considered. Unsupervised and supervised multivariate analyses were sequentially performed on various data blocks obtained by the above techniques. Successful data treatments led to statistically significant models that discriminated and classified cold-pressed lemon oils according to their geographic origin, as well as their production processes. Studying the loadings allowed highlighting of important classes of discriminant variables that corresponded to putative or identified chemical functions and compounds