Molecular mechanism of biased signaling in a prototypical G protein–coupled receptor

Suomivuori, Carl‐Mikael; Latorraca, Naomi R.; Wingler, Laura M.; Eismann, Stephan; King, Matthew C.; Kleinhenz, Alissa L. W.; Skiba, Meredith A.; Staus, Dean P.; Kruse, Andrew C.; Lefkowitz, Robert J.; Dror, Ron O.

doi:10.1126/science.aaz0326

Cited by 187 publications

(208 citation statements)

References 73 publications

Supporting

Mentioning

174

Contrasting

Order By: Relevance

“…While these models are computationally costly to simulate, they can be quite accurate and have recently been used successfully to fold small proteins and even now begin to allow study of the dynamics of larger systems. [2,3] The great amount of detail in the all-atom representation often leads us to forget that all-atom models today still make physical assumptions like the additivity of the intermolecular forces, which may not be fully accurate in all situations. Averaging over the solvent degrees of freedom yields tremendous computational cost savings.…”

Section: Introductionmentioning

confidence: 99%

OpenAWSEM with Open3SPN2: a fast, flexible, and accessible framework for large-scale coarse-grained biomolecular simulations

Bueno

Schafer

et al. 2020

Preprint

View full text Add to dashboard Cite

We present OpenAWSEM and Open3SPN2, new cross-compatible implementations of coarse-grained models for protein (AWSEM) and DNA (3SPN2) molecular dynamics simulations within the OpenMM framework. These new implementations retain the chemical accuracy and intrinsic efficiency of the original models while adding GPU acceleration and the ease of forcefield modification provided by OpenMM’s Custom Forces software framework. By utilizing GPUs, we achieve more than a 100-fold speedup in protein and protein-DNA simulations over the existing LAMMPS-based implementations running on a CPU. We showcase the benefits of OpenMM’s Custom Forces framework by devising and implementing two new potentials that allow us to address important aspects of protein folding and structure prediction and by testing the ability of the combined OpenAWSEM and Open3SPN2 to model protein-DNA binding. The first potential is used to describe the changes in effective interactions that occur as a protein becomes partially buried in a membrane. We also introduced an interaction to describe proteins with multiple disulfide bonds. Using simple pairwise disulfide bonding terms results in unphysical clustering of cysteine residues, posing a problem when simulating the folding of proteins with many cysteines. We now can computationally reproduce Anfinsen’s early Nobel prize winning experiments [1] by using OpenMM’s Custom Forces framework to introduce a multi-body disulfide bonding term that prevents unphysical clustering. Our protein-DNA simulations show that the binding landscape is funneled towards structures that are quite similar to those found using experiments. In summary, this paper provides a simulation tool for the molecular biophysics community that is both easy to use and sufficiently efficient to simulate large proteins and large protein-DNA systems that are central to many cellular processes. These codes should facilitate the interplay between molecular simulations and cellular studies, which have been hampered by the large mismatch between the time and length scales accessible to molecular simulations and those relevant to cell biology.

show abstract

Section: Introductionmentioning

confidence: 99%

OpenAWSEM with Open3SPN2: a fast, flexible, and accessible framework for large-scale coarse-grained biomolecular simulations

Bueno

Schafer

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The biased agonism hypothesis has two key tenets: (a) biased agonists stabilize receptor conformations distinct from full agonists and the result is differential coupling to receptor interacting proteins; (b) the response to GPCR activation arises from many cellular pathways and differential coupling to G proteins, GPCR kinases (GRKs), and β‐arrestins can preferentially activate one pathway over another 12,18,19 . Recent biophysical, structural, and cell biological studies have provided important support to this model 20–25 . Thus, a G protein biased agonist will have greater relative efficacy in activation of the heterotrimeric G protein and subsequent signaling cascades 26 .…”

Section: Classical Pharmacology and An Introduction To Biasmentioning

confidence: 99%

A cellular perspective of bias at G protein‐coupled receptors

2020

View full text Add to dashboard Cite

G protein-coupled receptors (GPCRs) modulate cell function over short-and long-term timescales. GPCR signaling depends on biochemical parameters that define the what, when, and where of receptor function: what proteins mediate and regulate receptor signaling, where within the cell these interactions occur,and how long these interactions persist. These parameters can vary significantly depending on the activating ligand. Collectivity, differential agonist activity at a GPCR is called bias or functional selectivity. Here we review agonist bias at GPCRs with a focus on ligands that show dramatically different cellular responses from their unbiased counterparts.

show abstract

“…In contrast, arrestin-biased ligands did not induce TM3 rotation and were structurally more defined. Thus, the rotational freedom of the terminal F 8 side chain seems to orchestrate intracellular conformations, which was confirmed by molecular dynamics simulations [ 252 ].…”

Section: A Receptor’s Perspectivementioning

confidence: 76%

Capturing Peptide–GPCR Interactions and Their Dynamics

Kaiser

Coin

2020

Molecules

View full text Add to dashboard Cite

Many biological functions of peptides are mediated through G protein-coupled receptors (GPCRs). Upon ligand binding, GPCRs undergo conformational changes that facilitate the binding and activation of multiple effectors. GPCRs regulate nearly all physiological processes and are a favorite pharmacological target. In particular, drugs are sought after that elicit the recruitment of selected effectors only (biased ligands). Understanding how ligands bind to GPCRs and which conformational changes they induce is a fundamental step toward the development of more efficient and specific drugs. Moreover, it is emerging that the dynamic of the ligand–receptor interaction contributes to the specificity of both ligand recognition and effector recruitment, an aspect that is missing in structural snapshots from crystallography. We describe here biochemical and biophysical techniques to address ligand–receptor interactions in their structural and dynamic aspects, which include mutagenesis, crosslinking, spectroscopic techniques, and mass-spectrometry profiling. With a main focus on peptide receptors, we present methods to unveil the ligand–receptor contact interface and methods that address conformational changes both in the ligand and the GPCR. The presented studies highlight a wide structural heterogeneity among peptide receptors, reveal distinct structural changes occurring during ligand binding and a surprisingly high dynamics of the ligand–GPCR complexes.

show abstract

Molecular mechanism of biased signaling in a prototypical G protein–coupled receptor

Cited by 187 publications

References 73 publications

OpenAWSEM with Open3SPN2: a fast, flexible, and accessible framework for large-scale coarse-grained biomolecular simulations

OpenAWSEM with Open3SPN2: a fast, flexible, and accessible framework for large-scale coarse-grained biomolecular simulations

A cellular perspective of bias at G protein‐coupled receptors

Capturing Peptide–GPCR Interactions and Their Dynamics

Contact Info

Product

Resources

About