Nuclear receptors (NRs) constitute an important class of drug targets. We created the most exhaustive NR-focused benchmarking database to date, the NRLiSt BDB (NRs ligands and structures benchmarking database). The 9905 compounds and 339 structures of the NRLiSt BDB are ready for structure-based and ligand-based virtual screening. In the present study, we detail the protocol used to generate the NRLiSt BDB and its features. We also give some examples of the errors that we found in ChEMBL that convinced us to manually review all original papers. Since extensive and manually curated experimental data about NR ligands and structures are provided in the NRLiSt BDB, it should become a powerful tool to assess the performance of virtual screening methods on NRs, to assist the understanding of NR's function and modulation, and to support the discovery of new drugs targeting NRs. NRLiSt BDB is freely available online at http://nrlist.drugdesign.fr .
[a] 1Introduction Nuclear receptors (NRs) are transcription factors naturally switched on and off by small-molecule hormones that monitor aw ide range of physiological functions. NRs can be targeted in numerous diseases [1] and synthetic ligands can artificially modulate the action of NRs,m ainly by activating (agonist ligands) or inhibiting (antagonist ligands) its activity.I nt his context, identifying the best ligand of ag iven targeti sn ot sufficient,i ti sn ecessary to find al igand with the suitablep harmacological profilea nd thus to be able to predict the agonist or antagonist behaviour of aN Rl igand. Virtual screening methods are widely used to predict the activity of small compounds [2] and can also be applied to the prediction of the pharmacologicalp rofile of NRs ligands using the knowledgeo ft he molecular bases of NRs agonism and antagonism.[3] However,t he different virtual screening tools evaluated in this purpose lead to mixed results [4] and being able to predict the pharmacological profile of NRs ligands remains ac hallenge.In this study, we review the ability of currently available virtual screeningm ethodst oc haracterize specifically the pharmacologicalp rofile of NRs ligands using the 27 NRLiSt BDB datasets.[5] After at horough description of the results we obtained using molecular descriptors and ar ecall of the results we obtainedu sing ad ocking method [4i] and a3 D ligand-based (LB) and structure-based (SB) pharmacophore modeling method, [6] we will emphasize on the advantages and drawbacks of each approach. 2Material and MethodsNuclearR eceptors Ligands and Structures Benchmarking DataBase (NRLiStB DB). The NRLiSt BDB [5] is ap ublic benchmarkingd atabase dedicated to the NRs and constructed to be used for the evaluation of both SB and LB methods. The NRLiSt BDB includest he 27 NRs (out of the 48 known human NRs) for whichm ore thano ne agonist ligand, one antagonistl igand, and at least one experimental structure were described. For each NR, all of the ligands foundt ob e agonisto ra ntagonist in the scientific literature are provided in two separated datasets and all available human holo PDB structures are provided (except for RXRg,f or which Abstract:N uclear receptors (NRs) constitute an important class of therapeutic targets. During the last 4years, we tackled the pharmacological profilea ssessment of NR ligands for which we constructedt he NRLiSt BDB. We evaluated and compared the performance of different virtual screeninga pproaches:m ean of molecular descriptor distribution values, molecular dockinga nd 3D pharmacophore models. The simple comparison of the distributionp rofiles of 4885 molecular descriptors between the agonista nd antagonist datasets didn'tp rovide satisfying results. We obtained an overall good performance with the docking method we used, Surflex-Dock which was able to discriminate agonist from antagonistl igands. But the availability of PDB structures in the "pharmacological-profile-to-predictbound-state" (agonist-bound or antagonist-bound) and the a...
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