Recognition of the ligand-induced spatiotemporal residue pair pattern of β2-adrenergic receptors using 3-D residual networks trained by the time series of protein distance maps

Han, Man Jung; Lee, Seungju; Ha, Yuna; Lee, Jee Young

doi:10.1016/j.csbj.2022.10.036

Cited by 2 publications

(1 citation statement)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, simulation results show the role of critical residues and structural motifs responsible for transmitting allosteric signals from the agonist to the transducer-binding regions; TM3 and TM7 residues act as allosteric communication hubs in GRK/β-arrestin selective signaling, whereas TM5 residues favor G protein signaling. The residues and allosteric functional regions in β 2 AR, identified in the present study, can be targeted to design novel biased drugs, as information about functional hotspots in β-adrenergic receptors has been demonstrated to assist in delineating the mechanism of agonist-induced activation and biased signaling. ,,,,− …”

Section: Resultsmentioning

confidence: 92%

Biased Signaling in Mutated Variants of β₂-Adrenergic Receptor: Insights from Molecular Dynamics Simulations

Madhu,

Shewani,

Murarka

2024

J. Chem. Inf. Model.

View full text Add to dashboard Cite

The molecular basis of receptor bias in G proteincoupled receptors (GPCRs) caused by mutations that preferentially activate specific intracellular transducers over others remains poorly understood. Two experimentally identified biased variants of β 2 -adrenergic receptors (β 2 AR), a prototypical GPCR, are a triple mutant (T68F, Y132A, and Y219A) and a single mutant (Y219A); the former bias the receptor toward the β-arrestin pathway by disfavoring G protein engagement, while the latter induces G protein signaling explicitly due to selection against GPCR kinases (GRKs) that phosphorylate the receptor as a prerequisite of β-arrestin binding. Though rigorous characterizations have revealed functional implications of these mutations, the atomistic origin of the observed transducer selectivity is not clear. In this study, we investigated the allosteric mechanism of receptor bias in β 2 AR using microseconds of all-atom Gaussian accelerated molecular dynamics (GaMD) simulations. Our observations reveal distinct rearrangements in transmembrane helices, intracellular loop 3, and critical residues R131 3.50 and Y326 7.53 in the conserved motifs D(E)RY and NPxxY for the mutant receptors, leading to their specific transducer interactions. Moreover, partial dissociation of G protein from the receptor core is observed in the simulations of the triple mutant in contrast to the single mutant and wild-type receptor. The reorganization of allosteric communications from the extracellular agonist BI-167107 to the intracellular receptor−transducer interfaces drives the conformational rearrangements responsible for receptor bias in the single and triple mutants. The molecular insights into receptor bias of β 2 AR presented here could improve the understanding of biased signaling in GPCRs, potentially opening new avenues for designing novel therapeutics with fewer side-effects and superior efficacy.

show abstract

Section: Resultsmentioning

confidence: 92%

Biased Signaling in Mutated Variants of β₂-Adrenergic Receptor: Insights from Molecular Dynamics Simulations

Madhu,

Shewani,

Murarka

2024

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

Deep learning for discriminating non-trivial conformational changes in molecular dynamics simulations of SARS-CoV-2 spike-ACE2

Moraes dos Santos,

Gutembergue de Mendonça,

Jerônimo Gomes Lobo

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Molecular dynamics (MD) simulations produce a substantial volume of high-dimensional data, and traditional methods for analyzing these data pose significant computational demands. Advances in MD simulation analysis combined with deep learning-based approaches have led to the understanding of specific structural changes observed in MD trajectories, including those induced by mutations. In this study, we model the trajectories resulting from MD simulations of the SARS-CoV-2 spike protein-ACE2, specifically the receptor-binding domain (RBD), as interresidue distance maps, and use deep convolutional neural networks to predict the functional impact of point mutations, related to the virus’s infectivity and immunogenicity. Our model was successful in predicting mutant types that increase the affinity of the S protein for human receptors and reduce its immunogenicity, both based on MD trajectories (precision = 0.718; recall = 0.800; $$\hbox {F}_1$$ = 0.757; MCC = 0.488; AUC = 0.800) and their centroids. In an additional analysis, we also obtained a strong positive Pearson’s correlation coefficient equal to 0.776, indicating a significant relationship between the average sigmoid probability for the MD trajectories and binding free energy (BFE) changes. Furthermore, we obtained a coefficient of determination of 0.602. Our 2D-RMSD analysis also corroborated predictions for more infectious and immune-evading mutants and revealed fluctuating regions within the receptor-binding motif (RBM), especially in the $$\beta _{1}^{\prime }/\beta _{2}^{\prime }-C$$ loop. This region presented a significant standard deviation for mutations that enable SARS-CoV-2 to evade the immune response, with RMSD values of 5Å in the simulation. This methodology offers an efficient alternative to identify potential strains of SARS-CoV-2, which may be potentially linked to more infectious and immune-evading mutations. Using clustering and deep learning techniques, our approach leverages information from the ensemble of MD trajectories to recognize a broad spectrum of multiple conformational patterns characteristic of mutant types. This represents a strategic advantage in identifying emerging variants, bypassing the need for long MD simulations. Furthermore, the present work tends to contribute substantially to the field of computational biology and virology, particularly to accelerate the design and optimization of new therapeutic agents and vaccines, offering a proactive stance against the constantly evolving threat of COVID-19 and potential future pandemics.

show abstract

Recognition of the ligand-induced spatiotemporal residue pair pattern of β2-adrenergic receptors using 3-D residual networks trained by the time series of protein distance maps

Cited by 2 publications

References 56 publications

Biased Signaling in Mutated Variants of β₂-Adrenergic Receptor: Insights from Molecular Dynamics Simulations

Biased Signaling in Mutated Variants of β₂-Adrenergic Receptor: Insights from Molecular Dynamics Simulations

Deep learning for discriminating non-trivial conformational changes in molecular dynamics simulations of SARS-CoV-2 spike-ACE2

Contact Info

Product

Resources

About

Recognition of the ligand-induced spatiotemporal residue pair pattern of β2-adrenergic receptors using 3-D residual networks trained by the time series of protein distance maps

Cited by 2 publications

References 56 publications

Biased Signaling in Mutated Variants of β2-Adrenergic Receptor: Insights from Molecular Dynamics Simulations

Biased Signaling in Mutated Variants of β2-Adrenergic Receptor: Insights from Molecular Dynamics Simulations

Deep learning for discriminating non-trivial conformational changes in molecular dynamics simulations of SARS-CoV-2 spike-ACE2

Contact Info

Product

Resources

About

Biased Signaling in Mutated Variants of β₂-Adrenergic Receptor: Insights from Molecular Dynamics Simulations

Biased Signaling in Mutated Variants of β₂-Adrenergic Receptor: Insights from Molecular Dynamics Simulations