Application of computational methods for class A GPCR Ligand discovery

Szwabowski, Gregory L.; Baker, Daniel L.; Parrill, Abby L.

doi:10.1016/j.jmgm.2023.108434

Cited by 5 publications

(5 citation statements)

References 150 publications

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“…The ionization state of the compounds at pH 7.5 was checked using the Epik program implemented in Schrödinger suite (Prime Version 3.2, Schrödinger, LLC, New York, NY, 2015). We monoprotonated L8 and L9 in the amino group with sp nitrogen of the substituent at the 5-position.…”

Section: Methodsmentioning

confidence: 99%

“…Sodium and chloride ions were added randomly in the water phase to neutralize the systems and reach the experimental salt concentration of 0.150 M NaCl. The total number of atoms of the complex was approximately 100,000 and the simulation box dimensions was (86 × 83 × 141 Å). We used the Desmond Viparr tool to assign the amber99sb , force field (ff99sb) parameters for the calculation of the protein, lipids, and intermolecular interactions, and the Generalized Amber Force Field (GAFF) parameters for the ligands.…”

Section: Methodsmentioning

confidence: 99%

“…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%

See 2 more Smart Citations

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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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Then, generating, and refining hit lists via database searches and ligand docking are integral steps in discovering ligands. For instance, since GPCR are extensively involved in cell signaling pathways, applying these computational methods in finding new GPCR ligands, opens the door for drug discovery [161]. Additionally, the prediction of membrane protein oligomerization has benefited from computational methods.…”

Section: Methodsmentioning

confidence: 99%

Exploring the World of Membrane Proteins: Techniques and Methods for Understanding Structure, Function, and Dynamics

Boulos,

Jabbour,

Khoury

et al. 2023

Molecules

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In eukaryotic cells, membrane proteins play a crucial role. They fall into three categories: intrinsic proteins, extrinsic proteins, and proteins that are essential to the human genome (30% of which is devoted to encoding them). Hydrophobic interactions inside the membrane serve to stabilize integral proteins, which span the lipid bilayer. This review investigates a number of computational and experimental methods used to study membrane proteins. It encompasses a variety of technologies, including electrophoresis, X-ray crystallography, cryogenic electron microscopy (cryo-EM), nuclear magnetic resonance spectroscopy (NMR), biophysical methods, computational methods, and artificial intelligence. The link between structure and function of membrane proteins has been better understood thanks to these approaches, which also hold great promise for future study in the field. The significance of fusing artificial intelligence with experimental data to improve our comprehension of membrane protein biology is also covered in this paper. This effort aims to shed light on the complexity of membrane protein biology by investigating a variety of experimental and computational methods. Overall, the goal of this review is to emphasize how crucial it is to understand the functions of membrane proteins in eukaryotic cells. It gives a general review of the numerous methods used to look into these crucial elements and highlights the demand for multidisciplinary approaches to advance our understanding.

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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.

show abstract

Application of computational methods for class A GPCR Ligand discovery

Cited by 5 publications

References 150 publications

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

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

Exploring the World of Membrane Proteins: Techniques and Methods for Understanding Structure, Function, and Dynamics

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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Application of computational methods for class A GPCR Ligand discovery

Cited by 5 publications

References 150 publications

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

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

Exploring the World of Membrane Proteins: Techniques and Methods for Understanding Structure, Function, and Dynamics

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

Contact Info

Product

Resources

About

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

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