Computational Medicinal Chemistry to Target GPCRs

Kiss, Dániel; Pándy-Szekeres, Gáspár; Keserü, György M.

doi:10.1016/b978-0-12-820472-6.00208-5

Cited by 4 publications

(3 citation statements)

References 277 publications

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“…Based on the described correlation of ligand pharmacophores with binding pocket (receptor)-derived pharmacophores that are actually targeted, , we envisioned a strategy for the design and synthesis of a fragment library concentrating on pharmacophore fingerprint diversity. This would consequently provide a comprehensive representation of the relevant “bioactivity space”.…”

Section: Resultsmentioning

confidence: 99%

Fragtory: Pharmacophore-Focused Design, Synthesis, and Evaluation of an sp³-Enriched Fragment Library

et al. 2023

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Fragment-based drug discovery has played an important role in medicinal chemistry and pharmaceutical research. Despite numerous demonstrated successes, the limited diversity and overrepresentation of planar, sp 2 -rich structures in commercial libraries often hamper the full potential of this approach. Hence, the thorough design of screening libraries inevitably determines the probability for meaningful hits and subsequent structural elaboration. Against this background, we present the generation of an exclusive fragment library based on iterative entry nomination by a specifically designed computational workflow: "FRAGTORY". Following a pharmacophore diversity-driven approach, we used FRAGTORY in an interdisciplinary academic setting to guide both tailored synthesis efforts and the implementation of in-house compounds to build a curated 288-member library of sp 3enriched fragments. Subsequent NMR screens against a model protein and hit validation by protein crystallography led to the identification of structurally novel ligands that were further characterized by isothermal titration calorimetry, demonstrating the applicability of our experimental approach.

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Section: Resultsmentioning

confidence: 99%

Fragtory: Pharmacophore-Focused Design, Synthesis, and Evaluation of an sp³-Enriched Fragment Library

et al. 2023

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“…Structure-based drug design (SBDD) against G protein-coupled receptors (GPCRs) has benefited from the rapid accumulation of experimentally resolved structures of ligand–GPCR complexes. Moreover, ligand-based drug design (LBDD) has been developed with the evolution of database and artificial intelligence technology. − The experimental structures of GPCRs in combination with the application of molecular dynamics (MD) simulations − and enhanced sampling methods , enable protein flexibility and help toward understanding their conformational heterogeneity and ligand binding. , Examples of SBDD and LBDD and application of MD simulations to understand ligand binding against GPCRs are cited in ref .…”

Section: Introductionmentioning

confidence: 99%

Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A₃ Receptor

Stampelou,

Ladds,

Kolocouris

2024

J. Phys. Chem. B

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A structure-based drug design pipeline that considers both thermodynamic and kinetic binding data of ligands against a receptor will enable the computational design of improved drug molecules. For unresolved GPCR-ligand complexes, a workflow that can apply both thermodynamic and kinetic binding data in combination with alpha-fold (AF)-derived or other homology models and experimentally resolved binding modes of relevant ligands in GPCR-homologs needs to be tested. Here, as test case, we studied a congeneric set of ligands that bind to a structurally unresolved G protein-coupled receptor (GPCR), the inactive human adenosine A 3 receptor (hA 3 R). We tested three available homology models from which two have been generated from experimental structures of hA 1 R or hA 2 AR and one model was a multistate alphafold 2 (AF2)-derived model. We applied alchemical calculations with thermodynamic integration coupled with molecular dynamics (TI/MD) simulations to calculate the experimental relative binding free energies and residence time (τ)-random accelerated MD (τ-RAMD) simulations to calculate the relative residence times (RTs) for antagonists. While the TI/MD calculations produced, for the three homology models, good Pearson correlation coefficients, correspondingly, r = 0.74, 0.62, and 0.67 and mean unsigned error (mue) values of 0.94, 1.31, and 0.81 kcal mol −1 , the τ-RAMD method showed r = 0.92 and 0.52 for the first two models but failed to produce accurate results for the multistate AF2-derived model. With subsequent optimization of the AF2-derived model by reorientation of the side chain of R173 5.34 located in the extracellular loop 2 (EL2) that blocked ligand's unbinding, the computational model showed r = 0.84 for kinetic data and improved performance for thermodynamic data (r = 0.81, mue = 0.56 kcal mol −1 ). Overall, after refining the multistate AF2 model with physics-based tools, we were able to show a strong correlation between predicted and experimental ligand relative residence times and affinities, achieving a level of accuracy comparable to an experimental structure. The computational workflow used can be applied to other receptors, helping to rank candidate drugs in a congeneric series and enabling the prioritization of leads with stronger binding affinities and longer residence times.

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“…Moreover, ligandbased drug design (LBDD) has been developed with the evolution of database and artificial intelligence technology. [1][2][3][4][5][6] The experimental structures of GPCRs in combination with the application of molecular dynamics (MD) simulations [1][2][3][4][5][6] and enhanced sampling methods, 4,7 that enables protein flexibility, helps towards understanding their conformational heterogeneity 8 and ligand binding. 4,7 Examples of SBDD and LBDD and application of MD simulations to understand ligands binding against GPCRs are cited in ref.…”

Section: Introductionmentioning

confidence: 99%

A Computational Workflow for Refining AF2 Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive human Adenosine A3 Receptor

Kolocouris,

Stampelou,

Ladds

2023

Preprint

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A drug design pipeline that considers both thermodynamic and kinetic binding data of ligands against a receptor will enable the computational design of improved drug molecules. Here we studied a congeneric set of ligands that bind to structurally unresolved G protein coupled receptor (GPCR), the inactive human adenosine A3 receptor (hA3R). We tested three available homology models from which two have been generated from experimental structures of hA1R or hA2AR and one model was a multi-state alpha-fold2 (AF2)-derived model. We then applied alchemical calculations with thermodynamic integration coupled to MD simulations (TI/MD) to calculate the experimental relative binding free energies, and residence time (τ)-random accelerated MD simulations (τ-RAMD) to calculate the relative residence times (RTs) for antagonists. While the TI/MD calculations produce for the three homology models good Pearson correlation coefficient, correspondingly, r = 0.74, 0.62, 0.67 and mean unsigned error (mue) = 0.94, 1.31, 0.81 kcal mol-1 the τ-RAMD method showed r = 0.92, 0.52 for the first two models but failed to produce accurate results for the multi-state AF2-derived model. Subsequent optimization of the AF2-derived model by re-orientation of side chain of R1735.34 located in the extracellular loop 2 (EL2), that blocks ligand’s unbinding, the computational model showed r = 0.84 for kinetic data and improved performance for thermodynamic data (r = 0.81, mue = 0.56 kcal mol-1). Overall, after refining the multi state-AF2 model with physics-based tools, we were able to show strong correlation between predicted and experimental ligand relative residence times and affinities, achieving a level of accuracy comparable to an experimental structure. The computational workflow used can be applied to other receptor so helping to rank candidate drugs in a congeneric series, enabling prioritization of leads with stronger binding affinities and longer residence times.

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Computational Medicinal Chemistry to Target GPCRs

Cited by 4 publications

References 277 publications

Fragtory: Pharmacophore-Focused Design, Synthesis, and Evaluation of an sp³-Enriched Fragment Library

Fragtory: Pharmacophore-Focused Design, Synthesis, and Evaluation of an sp³-Enriched Fragment Library

Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A₃ Receptor

A Computational Workflow for Refining AF2 Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive human Adenosine A3 Receptor

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Computational Medicinal Chemistry to Target GPCRs

Cited by 4 publications

References 277 publications

Fragtory: Pharmacophore-Focused Design, Synthesis, and Evaluation of an sp3-Enriched Fragment Library

Fragtory: Pharmacophore-Focused Design, Synthesis, and Evaluation of an sp3-Enriched Fragment Library

Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A3 Receptor

A Computational Workflow for Refining AF2 Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive human Adenosine A3 Receptor

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Product

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Fragtory: Pharmacophore-Focused Design, Synthesis, and Evaluation of an sp³-Enriched Fragment Library

Fragtory: Pharmacophore-Focused Design, Synthesis, and Evaluation of an sp³-Enriched Fragment Library

Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A₃ Receptor