Understanding G Protein Selectivity of Muscarinic Acetylcholine Receptors Using Computational Methods
Abstract
:1. Introduction
2. Results and Discussion
2.1. Structural Modeling and Generation of Receptor–G Protein Conformational Ensembles
2.2. Comparison of MD Structures with cryo-EM Structures
2.3. Structural Comparison of Cognate and Non-Cognate Complexes
2.4. Basis for the G Protein Selectivity of the M1 and M2 Receptors
2.4.1. Thermodynamic Analysis of Cognate and Non-Cognate Complexes
2.4.2. Receptor–G Protein Contacts in Cognate and Non-Cognate Complexes
2.4.3. Intra-Receptor Contacts in Cognate and Non-Cognate Complexes
3. Materials and Methods
3.1. Receptor–G Protein Complex Structure Preparation
3.2. Molecular Dynamics
- The lipids and waters were energy minimized for 2000 steps using a sequence of 1000 steepest descent steps and 1000 conjugate gradient steps while keeping the protein and ligand fixed. This removed any protein–solvent steric clashes and prepared the solvent for the next step.
- The lipids and waters were then relaxed using NPT equilibration MD for a simulation time of 250 ps while keeping the protein and ligand fixed. This allowed for any artificial air bubbles at the protein–solvent interface to be filled in by the solvent.
- The whole molecular system was then energy minimized for 2000 steps (like in Step 1) to allow the protein atoms to adjust to the relaxed solvent.
- The whole molecular system was then slowly heated from 0 K to 310 K at 1 atm pressure over a simulation time of 100 ps.
- The whole system was then relaxed using NPT equilibration at 310 K and 1 atm until the system density was stabilized, which typically occurred within 250 ps.
- Finally, the whole system was equilibrated for 0.5 µs. Simulation snapshots were saved every 10 ps for subsequent structural and thermodynamic analysis.
3.3. Trajectory Analysis
3.4. Binding Free Energy Analysis
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
GPCR | G-protein-coupled receptor |
MD | Molecular dynamics |
MMPBSA | Molecular mechanics/Poisson–Boltzmann surface area |
VMD | Visual molecular dynamics |
RMSD | Root-mean-squared deviation |
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R:G Binding Free Energy (kcal/mol) | M1:Gαi | M1:Gαq | M2:Gαi | M2:Gαq |
---|---|---|---|---|
MD1 | −318 | −233 | −259 | −272 |
MD2 | −252 | −353 | −285 | −184 |
MD3 | −221 | −327 | −325 | −281 |
MD4 | −228 | −363 | −253 | −253 |
Average | −270 | −319 | −280 | −248 |
StdDev | 42 | 59 | 33 | 44 |
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Santiago, L.J.; Abrol, R. Understanding G Protein Selectivity of Muscarinic Acetylcholine Receptors Using Computational Methods. Int. J. Mol. Sci. 2019, 20, 5290. https://doi.org/10.3390/ijms20215290
Santiago LJ, Abrol R. Understanding G Protein Selectivity of Muscarinic Acetylcholine Receptors Using Computational Methods. International Journal of Molecular Sciences. 2019; 20(21):5290. https://doi.org/10.3390/ijms20215290
Chicago/Turabian StyleSantiago, Luis Jaimes, and Ravinder Abrol. 2019. "Understanding G Protein Selectivity of Muscarinic Acetylcholine Receptors Using Computational Methods" International Journal of Molecular Sciences 20, no. 21: 5290. https://doi.org/10.3390/ijms20215290
APA StyleSantiago, L. J., & Abrol, R. (2019). Understanding G Protein Selectivity of Muscarinic Acetylcholine Receptors Using Computational Methods. International Journal of Molecular Sciences, 20(21), 5290. https://doi.org/10.3390/ijms20215290