Phylogeny of Greya (Lepidoptera: Prodoxidae), based on nucleotide sequence variation in mitochondrial cytochrome oxidase I and II: congruence with morphological data

The phylogeny of Greya Busck (Lepidoptera: Prodoxidae) was inferred from nucleotide sequence variation across a 765-bp region in the cytochrome oxidase I and II genes of the mitochondrial genome. Most parsimonious relationships of 25 haplotypes from 16 Greya species and two outgroup genera (Tetragma and Prodoxus) showed substantial congruence with the species relationships indicated by morphological variation. Differences between mitochondrial and morphological trees were found primarily in the positions of two species, G. variabilis and G. pectinifera, and in the branching order of the three major species groups in the genus. Conflicts between the data sets were examined by comparing levels of homoplasy in characters supporting alternative hypotheses. The phylogeny of Greya species suggests that host-plant association at the family level and larval feeding mode are conservative characters. Transition/transversion ratios estimated by reconstruction of nucleotide substitutions on the phylogeny had a range of 2.0-9.3, when different subsets of the phylogeny were used. The decline of this ratio with the increase in maximum sequence divergence among taxa indicates that transitions are masked by transversions along deeper internodes or long branches of the phylogeny. Among transitions, substitutions of A-->G and T-->C outnumbered their reciprocal substitutions by 2-6 times, presumably because of the approximately 4:1 (77%) A+T-bias in nucleotide base composition. Of all transversions, 73%-80% were A<-->T substitutions, 85% of which occurred at third positions of codons; these estimates did not decrease with an increase in maximum sequence divergence of taxa included in the analysis. The high frequency of A<-->T substitutions is either a reflection or an explanation of the 92% A+T bias at third codon positions.      

Sequential Application of Ligand and Structure Based Modeling Approaches to Index Chemicals for Their hH4R Antagonism

The human histamine H₄ receptor (hH₄R), a member of the G-protein coupled receptors (GPCR) family, is an increasingly attractive drug target. It plays a key role in many cell pathways and many hH₄R ligands are studied for the treatment of several inflammatory, allergic and autoimmune disorders, as well as for analgesic activity. Due to the challenging difficulties in the experimental elucidation of hH₄R structure, virtual screening campaigns are normally run on homology based models. However, a wealth of information about the chemical properties of GPCR ligands has also accumulated over the last few years and an appropriate combination of these ligand-based knowledge with structure-based molecular modeling studies emerges as a promising strategy for computer-assisted drug design. Here, two chemoinformatics techniques, the Intelligent Learning Engine (ILE) and Iterative Stochastic Elimination (ISE) approach, were used to index chemicals for their hH₄R bioactivity. An application of the prediction model on external test set composed of more than 160 hH₄R antagonists picked from the chEMBL database gave enrichment factor of 16.4. A virtual high throughput screening on ZINC database was carried out, picking ∼4000 chemicals highly indexed as H₄R antagonists'' candidates. Next, a series of 3D models of hH₄R were generated by molecular modeling and molecular dynamics simulations performed in fully atomistic lipid membranes. The efficacy of the hH₄R 3D models in discrimination between actives and non-actives were checked and the 3D model with the best performance was chosen for further docking studies performed on the focused library. The output of these docking studies was a consensus library of 11 highly active scored drug candidates. Our findings suggest that a sequential combination of ligand-based chemoinformatics approaches with structure-based ones has the potential to improve the success rate in discovering new biologically active GPCR drugs and increase the enrichment factors in a synergistic manner.