Matching cavities in g protein-coupled receptors to infer ligand-binding sites

To understand the activity and cross reactivity of ligands and G protein-coupled receptors, we take stock of relevant existing receptor mutation, sequence, and structural data to develop a statistically robust and transparent scoring system. Our method evaluates the viability of binding of any ligand for any GPCR sequence of amino acids. This enabled us to explore the binding repertoire of both receptors and ligands, relying solely on correlations between carefully identified receptor features and without requiring any chemical information about ligands. This study suggests that sequence similarity at specific binding pockets can predict relative affinity of ligands; enabling recovery of over 80% of known ligands for a withheld receptor and almost 80% of known receptors for a ligand. The method enables qualitative prediction of ligand binding for all nonredundant human G protein-coupled receptors.     

Bis(salicylaldiminate) copper(II) and palladium(II) complexes: towards columnar mesophases

Copper and palladium complexes of new salicylaldimines derived from 3,4,5-tridecyloxyaniline, 2,3,4-tridecyloxyaniline and 4-decyloxyaniline have been synthesized and characterized. All the ligands bear four or more aliphatic chains with the aim of inducing columnar mesophases at low temperatures. In particular, metal complexes derived from 4-(3,4,5-tridecyloxybenzoyloxy)-salicyliden-3,4,5-tridecyloxyaninile display rectangular columnar mesophases at (or near) room temperature. These mesophase assignments have been confirmed by X-ray diffraction. A significant decrease of the melting points of the compounds is observed in the tridecyloxyaniline derivatives compared with those of similar complexes derived from mono- or di-decyloxyaniline.     

The Short and Long of it: Shorter Chromatographic Analysis Suffice for Sample Classification During UHPLC-MS-Based Metabolic Fingerprinting

Ultra high-performance liquid chromatography hyphenated to mass spectrometry (UHPLC-MS) technologies has been widely applied in metabolomics, and the high resolution and peak capacity thereof are only some of the key aspects that are exploited in such and related fields. In the current study, we investigated if low resolution chromatography, with the aid of multivariate data analyses, could be sufficient for a metabolic fingerprinting study that aims at discriminating between samples of different biological status or origin. UHPLC-MS data from chemically-treated Arabidopsis thaliana plants were used and chromatograms with different gradient lengths were compared. MarkerLynx? technology was employed for data mining, followed by principal component analysis (PCA) and orthogonal projections to latent structure discriminant analysis (OPLS-DA) as multivariate statistical interpretations. The results showed that, despite the congestion in low resolution chromatograms (of 5 and 10 min), samples could be classified based on their respective biological background in a similar manner as when using chromatograms with better resolution (of 20 and 40 min). This paper thus underlines that, in a metabolic fingerprinting study, low resolution chromatography together with multivariate data analyses suffice for biological classification of samples. The results also suggest that, depending on the initial objective of the undertaken study, optimisation in chromatographic resolution prior to full scale metabolomics studies is mandatory.