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Pons, J.-L.

Publications and source records attributed to Pons, J.-L..

2 recordsLinked to original sources

Exploring the conformational space of a receptor for drugdesign: an ER α case study

MotivationProtein flexibility is challenging for both experimentalists and modellers, especially in the field of drug design. Estrogen Receptor alpha (ER) is an extensively studied Nuclear Receptor (NR) and a well-known therapeutic target with an important role in development and physiology. It is also a frequent off-target in standard toxicity tests for endocrine disruption. Here, we aim to evaluate the degree to which the conformational space and macromolecular flexibility of this well-characterized drug target can be described. Our approach exploits hundreds of crystallographic structures by means of molecular dynamics simulations and of virtual screening. ResultsThe analysis of hundreds of crystal structures confirms the presence of two main conformational states, known as agonist and antagonist, that mainly differ by the orientation of the C-terminal helix H12 which serves to close the binding pocket. ER also shows some loop flexibility, as well as variable side-chain orientations in its active site. We scrutinized the extent to which standard molecular dynamics simulations or crystallographic refinement as ensemble recapitulate most of the variability features seen by the array of available crystal structure. In parallel, we investigated on the kind and extent of flexibility that is required to achieve convincing docking for all high-affinity ER ligands present in BindingDB. Using either only one conformation with a few side-chains set flexible, or static structure ensembles in parallel during docking led to good quality and similar pose predictions. These results suggest that the several hundreds of crystal structures already known can properly describe the whole conformational universe of ERs ligand binding domain. This opens the road for better drug design and affinity computation. Contactlabesse@cbs.cnrs.fr

bioinformatics↗

Towards accurate high-throughput ligand affinity prediction by exploiting structural ensembles, docking metrics and ligand similarity

MotivationNowadays, virtual screening (VS) plays a major role in the process of drug development. Nonetheless, an accurate estimation of binding affinities, which is crucial at all stages, is not trivial and may require target-specific fine-tuning. Furthermore, drug design also requires improved predictions for putative secondary targets among which is Estrogen Receptor alpha (ER).\n\nResultsVS based on combinations of Structure-Based VS (SBVS) and Ligand-Based VS (LBVS) is gaining momentum to help characterizing secondary targets of xenobiotics (including drugs and pollutants). In this study, we propose an integrated approach using ligand docking based on multiple structural en-sembles to reflect the conformational flexibility of the receptor. Then, we investigate the impact of the two different types of features (structure-based docking descriptors and ligand-based molecular descriptors) for affinity predictions based on a random forest algorithm. We find that ligand-based features have limited predictive power (rP =0.69, R2=0.47), compared to structure-based features (rP =0.78, R2=0.60) while their combination maintains the overall accuracy (rP =0.77, R2=0.56). Extending the training dataset to include xenobiotics, leads to a novel high-throughput affinity prediction method for ER ligands (rP =0.85, R2=0.71). Methods robustness is tested on several ligand databases and performances are compared with existing rescoring procedures. The presented prediction tool is provided to the community as a dedicated satellite of the @TOME server.\n\nAvailabilityhttp://atome4.cbs.cnrs.fr/ATOME_V3/SERVER/EDMon_v3.html\n\nContactschneider@cbs.cnrs.fr, labesse@cbs.cnrs.fr

bioinformatics↗