A desirability function-based scoring scheme for selecting fragment-like class A aminergic GPCR ligands

Kelemen, Ádám Andor; Ferenczy, György G.; Keserü, György M.

doi:10.1007/s10822-014-9804-5

Cited by 8 publications

(13 citation statements)

References 12 publications

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“…In our recent study 1 we developed a ligand-based desirability function to enrich libraries with compounds potentially active on aminergic G-protein coupled receptor (GPCR) targets. The fragment aminergic GPCR score (FrAGS) provides computationally inexpensive, physicochemical property-based filtering of extensive public or proprietary databases consisting of hundreds of thousands to millions of entries.…”

Section: ■ Introductionmentioning

confidence: 99%

Structure-Based Consensus Scoring Scheme for Selecting Class A Aminergic GPCR Fragments

Kelemen¹,

Kingsford-Adaboh²,

Ferenczy³

et al. 2016

J. Chem. Inf. Model.

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Aminergic G-protein coupled receptors (GPRCs) represent well-known targets of central nervous-system related diseases. In this study a structure-based consensus virtual screening scheme was developed for designing targeted fragment libraries against class A aminergic GPCRs. Nine representative aminergic GPCR structures were selected by first clustering available X-ray structures and then choosing the one in each cluster that performs best in self-docking calculations. A consensus scoring protocol was developed using known promiscuous aminergic ligands and decoys as a training set. The consensus score (FrACS-fragment aminergic consensus score) calculated for the optimized protein ensemble showed improved enrichments in most cases as compared to stand-alone structures. Retrospective validation was carried out on public screening data for aminergic targets (5-HT1 serotonin receptor, TA1 trace-amine receptor) showing 8-17-fold enrichments using an ensemble of aminergic receptor structures. The performance of the structure based FrACS in combination with our ligand-based prefilter (FrAGS) was investigated both in a retrospective validation on the ChEMBL database and in a prospective validation on an in-house fragment library. In prospective validation virtual fragment hits were tested on 5-HT6 serotonin receptors not involved in the development of FrACS. Six out of the 36 experimentally tested fragments exhibited remarkable antagonist efficacies, and 4 showed IC50 values in the low micromolar or submicromolar range in a cell-based assay. Both retrospective and prospective validations revealed that the methodology is suitable for designing focused class A GPCR fragment libraries from large screening decks, commercial compound collections, or virtual databases.

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Section: ■ Introductionmentioning

confidence: 99%

Structure-Based Consensus Scoring Scheme for Selecting Class A Aminergic GPCR Fragments

Kelemen¹,

Kingsford-Adaboh²,

Ferenczy³

et al. 2016

J. Chem. Inf. Model.

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“…Fragment screening provides diverse chemotypes and significant operational freedom for the further optimization of promising hits. Inspired by these advantages in a recent study we developed a strategy for aminergic focused fragment libraries using a sequential filtering methodology applying ligand- and structure-based scoring functions [ 4 , 5 ]. The prospective validation was performed on our in-house library of 1183 fragments.…”

Section: Resultsmentioning

confidence: 99%

“…Although these compounds have a common tryptamine derived motif, a ChEMBL-analysis [ 3 ] revealed that no representatives have been ever tested against serotonergic targets. Developing ligand-(FrAGs) [ 4 ] and structure-based (FrACS) [ 5 ] methods for the identification of aminergic receptor ligands we identified 6 as a micromolar 5-hydroxytryptamine receptor 6 (5-HT 6 R) ligand.…”

Section: Introductionmentioning

confidence: 99%

Spiro[pyrrolidine-3,3′-oxindoles] and Their Indoline Analogues as New 5-HT6 Receptor Chemotypes

et al. 2017

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Synthetic derivatives of spiro[pyrrolidinyl-3,3′-oxindole] alkaloids (coerulescine analogues) were investigated as new ligands for aminergic G-protein coupled receptors (GPCRs). The chemical starting point 2′-phenylspiro[indoline-3,3′-pyrrolidin]-2-one scaffold was identified by virtual fragment screening utilizing ligand- and structure based methods. As a part of the hit-to-lead optimization a structure-activity relationship analysis was performed to explore the differently substituted 2′-phenyl-derivatives, introducing the phenylsulphonyl pharmacophore and examining the corresponding reduced spiro[pyrrolidine-3,3′-indoline] scaffold. The optimization process led to ligands with submicromolar affinities towards the 5-HT6 receptor that might serve as viable leads for further optimization.

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“…Thus, modification of the scoring function should take into account structural differences among ligands. Recent pose scoring based on ligand physicochemical properties [52] may be the way to explore these possibilities.…”

Section: Discussionmentioning

confidence: 99%

Towards predictive docking at aminergic G-protein coupled receptors

2015

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G protein-coupled receptors (GPCRs) are hard to crystallize. However, attempts to predict their structure have boomed as a result of advancements in crystallographic techniques. This trend has allowed computer-aided molecular modeling of GPCRs. We analyzed the performance of four molecular modeling programs in pose evaluation of re-docked antagonists / inverse agonists to 11 original crystal structures of aminergic GPCRs using an induced fit-docking procedure. AutoDock and Glide were used for docking. AutoDock binding energy function, GlideXP, Prime MM-GB/SA, and YASARA binding function were used for pose scoring. Root mean square deviation (RMSD) of the best pose ranged from 0.09 to 1.58 Å, and median RMSD of the top 60 poses ranged from 1.47 to 3.83 Å. However, RMSD of the top pose ranged from 0.13 to 7.33 Å and ranking of the best pose ranged from the 1st to 60th out of 60 poses. Moreover, analysis of ligand-receptor interactions of top poses revealed substantial differences from interactions found in crystallographic structures. Bad ranking of top poses and discrepancies between top docked poses and crystal structures render current simple docking methods unsuitable for predictive modeling of receptor-ligand interactions. Prime MM-GB/SA optimized for 3NY9 by multiple linear regression did not work well at 3NY8 and 3NYA, structures of the same receptor with different ligands. However, 9 of 11 trajectories of molecular dynamics simulations by Desmond of top poses converged with trajectories of crystal structures. Key interactions were properly detected for all structures. This procedure also worked well for cross-docking of tested β2-adrenergic antagonists. Thus, this procedure represents a possible way to predict interactions of antagonists with aminergic GPCRs.

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A desirability function-based scoring scheme for selecting fragment-like class A aminergic GPCR ligands

Cited by 8 publications

References 12 publications

Structure-Based Consensus Scoring Scheme for Selecting Class A Aminergic GPCR Fragments

Structure-Based Consensus Scoring Scheme for Selecting Class A Aminergic GPCR Fragments

Spiro[pyrrolidine-3,3′-oxindoles] and Their Indoline Analogues as New 5-HT6 Receptor Chemotypes

Towards predictive docking at aminergic G-protein coupled receptors

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