Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases

Nuclear receptors (NRs) constitute an important class of therapeutic targets. We evaluated the performance of 3D structure-based and ligand-based pharmacophore models in predicting the pharmacological profile of NRs ligands using the NRLiSt BDB database. We could generate selective pharmacophores for agonist and antagonist ligands and we found that the best performances were obtained by combining the structure-based and the ligand-based approaches. The combination of pharmacophores that were generated allowed to cover most of the chemical space of the NRLiSt BDB datasets. By screening the whole NRLiSt BDB on our 3D pharmacophores, we demonstrated their selectivity towards their dedicated NRs ligands. The 3D pharmacophores herein presented can thus be used as a predictor of the pharmacological activity of NRs ligands.Graphical AbstractUsing a combination of structure-based and ligand-based pharmacophores, agonist and antagonist ligands of the Nuclear Receptors included in the NRLiSt BDB database could be separatedElectronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0154-2) contains supplementary material, which is available to authorized users.

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“…The identified pharmacophore features represent chemical features directly involved in the ligand-binding site interactions [40]. …”

Section: Discussionmentioning

confidence: 99%

Discriminating agonist and antagonist ligands of the nuclear receptors using 3D-pharmacophores

et al. 2016

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“…Thus, redocking was performed with oseltamivir as co-crystal ligand on NA active site [7]. The parameters observed were rootmean-square deviation (RMSD) of co-crystal ligand at the selected binding site [14].…”

Section: Validation Of Docking Processmentioning

confidence: 99%

“…The only ligand with good affinity could be further optimized until fit enough to be developed as NA inhibitor [6]. Optimization of the selected ligand can be done in several ways, one of them by pharmacophore modification from test ligand [7]. The discovery of the main pharmacophore with the greatest influence on the affinity of a ligand is performed by deletion of each pharmacophore from a ligand one by one [8].…”

Section: Introductionmentioning

confidence: 99%

Akar Kuning (Arcangelisia Flava) As Neuraminidase Inhibitor: Molecular Docking And Pharmacophore Optimization Approach

Pratama¹

2017

Proceedings of the 2nd Sari Mulia International Conference on Health and Sciences 2017 (SMICHS 2017)

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Objectives: This study aims to find a relationship between secondary metabolites of akar kuning and neuraminidase (NA) with molecular docking study and also to determine the most potent NA inhibitor from metabolites of akar kuning. Methods: All ligands were sketched and optimized using Gaussian 03W with Hartree-Fock method basis sets 6-311G. Molecular docking was performed using AutoDock 4.2.6 toward NA in complexes with oseltamivir, a known NA inhibitor as a co-crystal ligand. The main parameter used was the free energy of binding (ΔG) and dissociation constant (K i ) as affinity marker. Pharmacophore optimization was conducted on metabolite with the highest affinity to assess the main pharmacophore with the highest influence. Results: Fibleucin provided the most negative ΔG and the lowest K i toward NA with -8.12 kcal/mol and 1.11 µM, respectively. Further pharmacophore optimization of fibleucin reveals that ether group at position number 25 had the highest influence toward fibleucin affinity and might be considered as the main pharmacophore of fibleucin as NA inhibitor. Conclusion: in silico molecular docking and pharmacophore optimization results indicated that fibleucin could be considered as NA inhibitor and should be potential to be developed as antiinfluenza particularly to H5N1 with oseltamivir resistance.

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“…Moreover, it has been possible execute some studies of prediction to highlight and identify their biosynthetic pathways through PathPred webserver and to choose only the "lead compounds" necessary to obtain in silico Pharmacokinetics and Pharmacodynamics models by means of ADMET/toxicity predictor server applying the LipinskiVeber filter to calculate some parameters related to absorption, distribution, metabolism, elimination, toxicity. Calculated these parameters, only molecules with good bioavailability, good predicted activity and good ADMET properties can be considered as hits compounds, and these compounds can be used to direct the design of next experimental assays [8].…”

Section: Study Of Physical-chemical Properties and Prediction Of Theimentioning

confidence: 99%

Pharmacophore modeling, virtual computational screening and biological evaluation studies

Dotolo

Facchiano

2017

Preprint

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Drug discovery process plays an important role in identifying new investigational drug-likes and developing new potential inhibitors related to a determinate target, in biopharmaceutical field [1]. An alternative promising and efficient used to identify new active substances is Pharmacophore modeling method.We defined a new computational strategy protocol characterized by the use of bioinformatics online tools and by the application of locally installed tools, for lead candidates generation-optimization able to reduce the cycle time and cost of this process and to promote the next steps of study [2].Hence, we have tried to apply this new computational procedure, in a more detailed screening, of small bioactive molecules, searching and identifying new candidates as "lead compounds", potentially able to inhibit biological target AKT1 human protein and its related molecular mechanisms [3].The workflow executed in our work has been characterized by a multi-step design, which concerns different topics: search in PDB database of a model structure for AKT1, pharmacophore modeling and virtual computational screening, biological evaluation divided in two parts (molecular validation of selected compounds and study of physical-chemical properties related to pharmacokinetic/pharmacodynamics prediction models). All these step have been performed through PHARMIT (http://pharmit.csb.pitt.edu) and Discovery Studio 4.5 platform.We selected the PDB structure 3O96 as the reference complex (protein-ligand), and we analyzed it by means of PHARMIT and Discovery Studio, to generate four different "pharmacophore models" with four different list of natural compounds.It is performed a thorough screening of compounds applying several filters, to find some good candidates as possible natural AKT1 allosteric inhibitors.The compounds that match a well-defined pharmacophore have been analyzed through direct molecular docking, for selecting only the best candidates and studying the protein-ligand interactions. Selected compounds have been investigated in more details, to trace their origin, by their chemical-physical properties.This information can help us to predict some plausible enzyme-catalyzed reaction pathways, through PathPred web-server and KEGG compound database, in order to highlight the most important reactions for biosynthesis of compounds and obtain PharmacoKinetics/PharmacoDynamics (PK/PD) models, to investigate the ADMET properties of these lead compounds and to study their behavior in some biological systems, for the next experimental assays.This new computational strategy has been very efficient in showing what could be good "lead compounds" and potential natural inhibitors of AKT1 and PI3K/AKT1 signaling cascade. Therefore, the next steps could be the experimental analysis of pharmacokinetics-pharmacodynamics and toxicity properties "in vitro/in vivo", in order to evaluate the results obtained "in silico".PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.2721v1 | CC BY 4.0 Open Access |

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Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases

Cited by 150 publications

References 158 publications

Discriminating agonist and antagonist ligands of the nuclear receptors using 3D-pharmacophores

Discriminating agonist and antagonist ligands of the nuclear receptors using 3D-pharmacophores

Akar Kuning (Arcangelisia Flava) As Neuraminidase Inhibitor: Molecular Docking And Pharmacophore Optimization Approach

Pharmacophore modeling, virtual computational screening and biological evaluation studies

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