Combining Molecular Dynamics and Docking Simulations to Develop Targeted Protocols for Performing Optimized Virtual Screening Campaigns on The hTRPM8 Channel

BACKGROUND

There is an increasing interest in TRPM8 ligands of medicinal interest, the rational design of which can be nowadays supported by structure-based in silico studies based on the recently resolved TRPM8 structures. Methods: The study involves the generation of a reliable hTRPM8 homology model, the reliability of which was assessed by a 1.0 μs MD simulation which was also used to generate multiple receptor conformations for the following structure-based virtual screening (VS) campaigns; docking simulations utilized different programs and involved all monomers of the selected frames; the so computed docking scores were combined by consensus approaches based on the EFO algorithm. Results: The obtained models revealed very satisfactory performances; LiGen™ provided the best results among the tested docking programs; the combination of docking results from the four monomers elicited a markedly beneficial effect on the computed consensus models. Conclusions: The generated hTRPM8 model appears to be amenable for successful structure-based VS studies; cross-talk modulating effects between interacting monomers on the binding sites can be accounted for by combining docking simulations as performed on all the monomers; this strategy can have general applicability for docking simulations involving quaternary protein structures with multiple identical binding pockets.

Linking rare and common disease: mapping clinical disease-phenotypes to ontologies in therapeutic target validation

BACKGROUND

The Centre for Therapeutic Target Validation (CTTV - https://www.targetvalidation.org/) was established to generate therapeutic target evidence from genome-scale experiments and analyses. CTTV aims to support the validity of therapeutic targets by integrating existing and newly-generated data. Data integration has been achieved in some resources by mapping metadata such as disease and phenotypes to the Experimental Factor Ontology (EFO). Additionally, the relationship between ontology descriptions of rare and common diseases and their phenotypes can offer insights into shared biological mechanisms and potential drug targets. Ontologies are not ideal for representing the sometimes associated type relationship required. This work addresses two challenges; annotation of diverse big data, and representation of complex, sometimes associated relationships between concepts.

METHODS

Semantic mapping uses a combination of custom scripting, our annotation tool 'Zooma', and expert curation. Disease-phenotype associations were generated using literature mining on Europe PubMed Central abstracts, which were manually verified by experts for validity. Representation of the disease-phenotype association was achieved by the Ontology of Biomedical AssociatioN (OBAN), a generic association representation model. OBAN represents associations between a subject and object i.e., disease and its associated phenotypes and the source of evidence for that association. The indirect disease-to-disease associations are exposed through shared phenotypes. This was applied to the use case of linking rare to common diseases at the CTTV.

RESULTS

EFO yields an average of over 80% of mapping coverage in all data sources. A 42% precision is obtained from the manual verification of the text-mined disease-phenotype associations. This results in 1452 and 2810 disease-phenotype pairs for IBD and autoimmune disease and contributes towards 11,338 rare diseases associations (merged with existing published work [Am J Hum Genet 97:111-24, 2015]). An OBAN result file is downloadable at http://sourceforge.net/p/efo/code/HEAD/tree/trunk/src/efoassociations/. Twenty common diseases are linked to 85 rare diseases by shared phenotypes. A generalizable OBAN model for association representation is presented in this study.

CONCLUSIONS

Here we present solutions to large-scale annotation-ontology mapping in the CTTV knowledge base, a process for disease-phenotype mining, and propose a generic association model, 'OBAN', as a means to integrate disease using shared phenotypes.

AVAILABILITY

EFO is released monthly and available for download at http://www.ebi.ac.uk/efo/.

Effects of epimedium polysaccharide-propolis flavone oral liquid on mucosal immunity in chickens

A previous study found that epimedium polysaccharide (EP)-propolis flavonoid (PF) injection (EPI) produced reliable immunoenhancement. In this study, we investigate the effects of EP-PF oral liquid (EFO) on mucosal immunity in the chicken small intestine while using EPI, EP and PF as controls. Groups of fourteen-day-old chickens were given EFO orally at one of the three doses when they were vaccinated with ND vaccine. On days 7, 21 and 35 after the first vaccination, six chickens were selected randomly from each group for measurements of the sIgA and IL-17 contents of the washing liquors of the duodenum and jejunum, counts of the lymphocytes in the duodenal endothelium and counts of the IgA(+) cells in the jejunal endothelium and cecum tonsil. The results indicated that EFO significantly promoted the secretion of sIgA and IL-17 and increased the numbers of lymphocyte and IgA(+) cells. Furthermore, EFO was more efficient than EPI at the high and medium doses. These findings indicate that EPO may enhance intestinal mucosal immunity and may be exploited as an oral immunopotentiator.