Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity

Jaiteh, Mariama; Rodríguez‐Espigares, Ismael; Selent, Jana; Carlsson, Jens

doi:10.1371/journal.pcbi.1007680

Cited by 37 publications

(32 citation statements)

References 93 publications

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“…Considering that our approach is largely automated, the effort to generate and inspect additional complexes as part of lead optimization process is relatively small. Prediction accuracy could likely be further improved by using more advanced methods for selecting representative snapshots from the MD simulations [47,48] and incorporation of targetspecific knowledge regarding ligand-residue contacts, mutagenesis data, and structure-activity relationships for known ligands [49][50][51][52].…”

Section: Discussionmentioning

confidence: 99%

Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

et al. 2021

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The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the transmembrane helix region of the models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode and the second extracellular loop region. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.

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Section: Discussionmentioning

confidence: 99%

Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

et al. 2021

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“…The best benchmarked modeling pair choices, as well as pairs which did not perform well, were considered for the analysis. The performance of the templates was ranked as good or bad, in published studies, on the basis of good ligand enrichment in VLS ( Perry et al, 2015 ; Loo et al, 2018 ; Jaiteh et al, 2020 ), local and global (RMSD) from crystal structures ( Castleman et al, 2019 ), and both ligand enrichment and RMSD from the crystal structure ( Shahaf et al, 2016 ). Researchers have compared varied parameters in these studies among the target-template pairs, including global sequence identity, TM-wise sequence identity, local sequence identity (identity within the binding pocket), model refinement through molecular dynamics and/or induced-fit docking, and the ligand binding site plasticity.…”

Section: Resultsmentioning

confidence: 99%

“…Models based on local similarity measures have produced better results in virtual screening experiments ( Castleman et al, 2019 ; Szwabowski et al, 2020 ). Multiple studies have shown that sequence identity above 30% could result in good GPCR homology models (within 3 Å) ( Shahaf et al, 2016 ; Loo et al, 2018 ; Jaiteh et al, 2020 ). But most of the GPCRs share low sequence identity with available templates.…”

Section: Introductionmentioning

confidence: 99%

BIO-GATS: A Tool for Automated GPCR Template Selection Through a Biophysical Approach for Homology Modeling

Jabeen

Vijayram

Ranganathan

2021

Front. Mol. Biosci.

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G protein-coupled receptors (GPCRs) are the largest family of membrane proteins with more than 800 members. GPCRs are involved in numerous physiological functions within the human body and are the target of more than 30% of the United States Food and Drug Administration (FDA) approved drugs. At present, over 400 experimental GPCR structures are available in the Protein Data Bank (PDB) representing 76 unique receptors. The absence of an experimental structure for the majority of GPCRs demand homology models for structure-based drug discovery workflows. The generation of good homology models requires appropriate templates. The commonly used methods for template selection are based on sequence identity. However, there exists low sequence identity among the GPCRs. Sequences with similar patterns of hydrophobic residues are often structural homologs, even with low sequence identity. Extending this, we propose a biophysical approach for template selection based principally on hydrophobicity correspondence between the target and the template. Our approach takes into consideration other relevant parameters, including resolution, similarity within the orthosteric binding pocket of GPCRs, and structure completeness, for template selection. The proposed method was implemented in the form of a free tool called Bio-GATS, to provide the user with easy selection of the appropriate template for a query GPCR sequence. Bio-GATS was successfully validated with recent published benchmarking datasets. An application to an olfactory receptor to select an appropriate template has also been provided as a case study.

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“…However, whether this is really an advantage for GPCR drug discovery is a current debate. In a recent publication on the performance of virtual screening against GPCR homology models 12 , in which binding site plasticity was considered by including ensembles of structures, it was shown that MD refinement resulted in moderate improvements of structural accuracy and the virtual screening performance of snapshots was either comparable to or worse than that of the raw homology models 12 . However, and from a different perspective, the methodological approach herein shown is focused on the statistical significance of structural features reflecting receptor activation.…”

Section: Resultsmentioning

confidence: 99%

Statistics for the analysis of molecular dynamics simulations: providing P values for agonist-dependent GPCR activation

Bruzzese

Dalton

Giraldo

2020

Sci Rep

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Molecular dynamics (MD) is the common computational technique for assessing efficacy of GPCR-bound ligands. Agonist efficacy measures the capability of the ligand-bound receptor of reaching the active state in comparison with the free receptor. In this respect, agonists, neutral antagonists and inverse agonists can be considered. A collection of MD simulations of both the ligand-bound and the free receptor are needed to provide reliable conclusions. Variability in the trajectories needs quantification and proper statistical tools for meaningful and non-subjective conclusions. Multiple-factor (time, ligand, lipid) ANOVA with repeated measurements on the time factor is proposed as a suitable statistical method for the analysis of agonist-dependent GPCR activation MD simulations. Inclusion of time factor in the ANOVA model is consistent with the time-dependent nature of MD. Ligand and lipid factors measure agonist and lipid influence on receptor activation. Previously reported MD simulations of adenosine A2a receptor (A2aR) are reanalyzed with this statistical method. TM6–TM3 and TM7–TM3 distances are selected as dependent variables in the ANOVA model. The ligand factor includes the presence or absence of adenosine whereas the lipid factor considers DOPC or DOPG lipids. Statistical analysis of MD simulations shows the efficacy of adenosine and the effect of the membrane lipid composition. Subsequent application of the statistical methodology to NECA A2aR agonist, with resulting P values in consistency with its pharmacological profile, suggests that the method is useful for ligand comparison and potentially for dynamic structure-based virtual screening.

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Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity

Cited by 37 publications

References 93 publications

Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

BIO-GATS: A Tool for Automated GPCR Template Selection Through a Biophysical Approach for Homology Modeling

Statistics for the analysis of molecular dynamics simulations: providing P values for agonist-dependent GPCR activation

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