Recent advances in computational studies of GPCR-G protein interactions

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.Protein-protein interactions are key in cellular signaling. G protein-coupled receptors (GPCRs), the largest superfamily of human membrane proteins, are able to transduce extracellular signals (e.g., hormones and neurotransmitters) to intracellular proteins, in particular the G proteins. Since GPCRs serve as primary targets of ~ 1/3 of currently marketed drugs, it is important to understand mechanisms of GPCR signaling in order to design selective and potent drug molecules. This chapter focuses on recent advances in computational studies of the GPCR-G protein interactions using bioinformatics, protein-protein docking and molecular dynamics simulation approaches

Mechanistic Insights into Specific G Protein Interactions with Adenosine Receptors

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Physical Chemistry B, copyright © 2019 American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.jpcb.9b04867.Coupling between G-protein-coupled receptors (GPCRs) and the G proteins is a key step in cellular signaling. Despite extensive experimental and computational studies, the mechanism of specific GPCR–G protein coupling remains poorly understood. This has greatly hindered effective drug design of GPCRs that are primary targets of ∼1/3 of currently marketed drugs. Here, we have employed all-atom simulations using a robust Gaussian accelerated molecular dynamics (GaMD) method to decipher the mechanism of the GPCR–G protein interactions. Adenosine receptors (ARs) were used as model systems based on very recently determined cryo-EM structures of the A1AR and A2AAR coupled with the Gi and Gs proteins, respectively. Changing the Gi protein to the Gs led to increased fluctuations in the A1AR and agonist adenosine (ADO), while agonist 5′-N-ethylcarboxamidoadenosine (NECA) binding in the A2AAR could be still stabilized upon changing the Gs protein to the Gi. Free energy calculations identified one stable low-energy conformation for each of the A1AR-Gi and A2AAR-Gs complexes as in the cryo-EM structures, similarly for the A2AAR-Gi complex. In contrast, the ADO agonist and Gs protein sampled multiple conformations in the A1AR-Gs system. GaMD simulations thus indicated that the A1AR preferred to couple with the Gi protein to the Gs, while the A2AAR could couple with both the Gs and Gi proteins, being highly consistent with experimental findings of the ARs. More importantly, detailed analysis of the atomic simulations showed that the specific AR-G protein coupling resulted from remarkably complementary residue interactions at the protein interface, involving mainly the receptor transmembrane 6 helix and the Gα α5 helix and α4-β6 loop. In summary, the GaMD simulations have provided unprecedented insights into the dynamic mechanism of specific GPCR–G protein interactions at an atomistic level

G‐Protein‐Coupled Receptor–Membrane Interactions Depend on the Receptor Activation State

This is the peer reviewed version of the following article: Bhattarai, A., Wang, J., & Miao, Y. (2020). G-Protein-Coupled Receptor-Membrane Interactions Depend on the Receptor Activation State. Journal of computational chemistry, 41(5), 460–471. https://doi.org/10.1002/jcc.26082, which has been published in final form at https://doi.org/10.1002/jcc.26082. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.G‐protein‐coupled receptors (GPCRs) are the largest family of human membrane proteins and serve as primary targets of approximately one‐third of currently marketed drugs. In particular, adenosine A1 receptor (A1AR) is an important therapeutic target for treating cardiac ischemia–reperfusion injuries, neuropathic pain, and renal diseases. As a prototypical GPCR, the A1AR is located within a phospholipid membrane bilayer and transmits cellular signals by changing between different conformational states. It is important to elucidate the lipid–protein interactions in order to understand the functional mechanism of GPCRs. Here, all‐atom simulations using a robust Gaussian accelerated molecular dynamics (GaMD) method were performed on both the inactive (antagonist bound) and active (agonist and G‐protein bound) A1AR, which was embedded in a 1‐palmitoyl‐2‐oleoyl‐glycero‐3‐phosphocholine (POPC) lipid bilayer. In the GaMD simulations, the membrane lipids played a key role in stabilizing different conformational states of the A1AR. Our simulations further identified important regions of the receptor that interacted distinctly with the lipids in highly correlated manner. Activation of the A1AR led to differential dynamics in the upper and lower leaflets of the lipid bilayer. In summary, GaMD enhanced simulations have revealed strongly coupled dynamics of the GPCR and lipids that depend on the receptor activation state

Improved Modeling of Peptide-Protein Binding Through Global Docking and Accelerated Molecular Dynamics Simulations

This work is licensed under a Creative Commons Attribution 4.0 International License.Peptides mediate up to 40% of known protein-protein interactions in higher eukaryotes and play a key role in cellular signaling, protein trafficking, immunology, and oncology. However, it is challenging to predict peptide-protein binding with conventional computational modeling approaches, due to slow dynamics and high peptide flexibility. Here, we present a prototype of the approach which combines global peptide docking using ClusPro PeptiDock and all-atom enhanced simulations using Gaussian accelerated molecular dynamics (GaMD). For three distinct model peptides, the lowest backbone root-mean-square deviations (RMSDs) of their bound conformations relative to X-ray structures obtained from PeptiDock were 3.3–4.8 Å, being medium quality predictions according to the Critical Assessment of PRediction of Interactions (CAPRI) criteria. GaMD simulations refined the peptide-protein complex structures with significantly reduced peptide backbone RMSDs of 0.6–2.7 Å, yielding two high quality (sub-angstrom) and one medium quality models. Furthermore, the GaMD simulations identified important low-energy conformational states and revealed the mechanism of peptide binding to the target proteins. Therefore, PeptiDock+GaMD is a promising approach for exploring peptide-protein interactions

Challenges and frontiers of computational modelling of biomolecular recognition

Biomolecular recognition including binding of small molecules, peptides and proteins to their target receptors plays a key role in cellular function and has been targeted for therapeutic drug design. However, the high flexibility of biomolecules and slow binding and dissociation processes have presented challenges for computational modelling. Here, we review the challenges and computational approaches developed to characterize biomolecular binding, including molecular docking, molecular dynamics simulations (especially enhanced sampling) and machine learning. Further improvements are still needed in order to accurately and efficiently characterise binding structures, mechanisms, thermodynamics and kinetics of biomolecules in the future

GLOW: A Workflow Integrating Gaussian-Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Chemical Theory and Computation, Copyright © 2022 American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.jctc.1c01055.We introduce a Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and free energy profiling workflow (GLOW) to predict molecular determinants and map free energy landscapes of biomolecules. All-atom GaMD-enhanced sampling simulations are first performed on biomolecules of interest. Structural contact maps are then calculated from GaMD simulation frames and transformed into images for building DL models using a convolutional neural network. Important structural contacts are further determined from DL models of attention maps of the structural contact gradients, which allow us to identify the system reaction coordinates. Finally, free energy profiles are calculated for the selected reaction coordinates through energetic reweighting of the GaMD simulations. We have also successfully demonstrated GLOW for the characterization of activation and allosteric modulation of a G protein-coupled receptor, using the adenosine A1 receptor (A1AR) as a model system. GLOW findings are highly consistent with previous experimental and computational studies of the A1AR, while also providing further mechanistic insights into the receptor function. In summary, GLOW provides a systematic approach to mapping free energy landscapes of biomolecules. The GLOW workflow and its user manual can be downloaded at http://miaolab.org/GLOW