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  • Review Article
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Pseudoreceptor models in drug design: bridging ligand- and receptor-based virtual screening

Nature Reviews Drug Discovery volume 7, pages 667–677 (2008)Cite this article

Key Points

  • During the early phase of drug discovery, in silico receptor-based and ligand-based strategies are used to find novel hits. Pseudoreceptor models link the concepts of both strategies for utilization in virtual screening and quantitative structure–activity relationship modelling.

  • Essentially, pseudoreceptor models can provide an entry point for receptor-based approaches for cases in which high-resolution structures of targets are lacking.

  • The methodology of pseudoreceptor model generation involves three fundamental tasks. First, the presumed key interaction sites (anchor points) of the ligand–receptor complex are defined; second, the core pseudoreceptor model is assembled around these hypotheses; and last, model coordinates are optimized to gain more accurate calculated binding energies in validation studies.

  • Molecular alignment of selected known actives represents the foundation of pseudoreceptor generation. Various approaches can be used to ensure that an optimized model is generated, and these include the use of reference compounds for which their binding mode is experimentally supported; use of key interacting groups; and use of ligands with high-affinity.

  • Models can be generated and refined using various methods, and six main categories are discussed: grid-based, isosurface-based, partition-based, atom-based, peptide-based and fragment-based. Various case studies of pseudoreceptor modelling for hit and lead finding are described, including a pseudoreceptor model of sweet-tasting molecules, de novo design of non-steroid oestrogen receptor antagonists and investigation of ligand binding to a cocaine receptor.

  • Although pseudoreceptor models can be very useful, it is important to realize their limitations. Most importantly, they do not represent real macromolecular binding sites, but only a model that is generated from known ligands. Nevertheless, as long as these limitations are kept in mind, they are an important tool in the drug discovery process.

Abstract

Rational drug design is based on explicit or implicit structure–activity relationship models. Typically, receptor-based or ligand-based strategies are pursued, depending on the information available about known ligands and the receptor structure. Pseudoreceptor models combine the advantages of these two strategies and represent a unifying concept for both receptor mapping and ligand matching. They can provide an entry point for structure-based modelling in drug discovery projects that lack a high-resolution structure of the target. Here, we review the field of pseudoreceptor modelling techniques along with recent hit and lead finding applications, and critically discuss prerequisites, advantages and limitations of the various approaches.

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Figure 1: Aim of pseudoreceptor modelling.
Figure 2: Workflow of pseudoreceptor model generation.
Figure 3: Pseudoreceptor construction routes.
Figure 4: Pseudoreceptor optimization with a genetic algorithm.
Figure 5: Shape complementarity of ligands and their receptor binding pockets.
Figure 6: Two case studies comparing ligand binding to a pseudoreceptor and their true biological receptor.
Figure 7: Differing ligand alignments.

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Acknowledgements

N. Todoroff is thanked for technical support. This work was supported by the Beilstein Institute for the Advancement of Chemical Sciences (Beilstein Institut zur Förderung der Chemischen Wissenschaften), Frankfurt am Main, Germany.

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  1. g.schneider@chemie.uni-frankfurt.de

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  1. Institute of Organic Chemistry and Chemical Biology, Johann Wolfgang Goethe-University Frankfurt, Siesmayerstrasse 70, Frankfurt, D-60323, Germany

    Yusuf Tanrikulu & Gisbert Schneider

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  1. Yusuf Tanrikulu
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DATABASES

RSCB Protein Data Bank

1eqg

1eqh

2hyy

2p54

2rtf

FURTHER INFORMATION

Laboratory of Gisbert Schneider

Glossary

Fragment-based lead discovery

The identification of bioactive substances by assembling small-molecule fragments. Often, the fragments themselves are building blocks of known drugs.

Molecular similarity searching

These are techniques for searching compound databases with the aim of retrieving molecules that have a similar chemical structure to a query compound. Typically, similarity is expressed on the basis of molecular descriptors; for example substructure fingerprints or pharmacophoric features.

Pharmacophore

The spatial arrangement of atoms or groups in a molecule known or predicted to be responsible for specific biological activity.

Scaffold-hopping

The identification of isofunctional but structurally different chemotypes.

Repurposing

The use of known leads and approved drugs for new indications. This includes the development of known drugs to preferably interact with so-called off-targets.

Quantitative structure–activity relationship

(QSAR). Mathematical relationships that link chemical structure and pharmacological activity in a quantitative manner for a series of compounds. Methods that can be used in QSAR include various regression and pattern-recognition techniques.

Atomistic

A molecular design concept or molecular representation that considers single atoms.

Energy minimization

The optimization of molecule conformation towards idealized geometries by help of an energy function.

Bounded functions

Mathematical functions with a range that has an upper bound and a lower bound.

Voronoi patches

A set of polygons (polytopes) that ascertain an optimal tessellation (tiling) of the encapsulated space.

Rotamer library

A collection of residue conformations, each representing an optimum according to an energy function.

Monte Carlo conformational search

A technique for random sampling of molecular conformations, which is often combined with the Metropolis criterion for whether or not to accept a conformation.

Leave-one-out cross-validation

A statistical method to evaluate the performance of prediction tools. A single observation from the original sample is used for validation, and all remaining observations are used as training data. This is repeated such that each observation in the sample is used once in the validation step.

Y-scrambling

A statistical test of prediction tools, in which models are fitted for randomly reordered property/activity values and compared with the model obtained for the actual property/activity values.

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Tanrikulu, Y., Schneider, G. Pseudoreceptor models in drug design: bridging ligand- and receptor-based virtual screening. Nat Rev Drug Discov 7, 667–677 (2008). https://doi.org/10.1038/nrd2615

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