Journal: Nature

Article Title: Ligand efficacy modulates conformational dynamics of the µ-opioid receptor

doi: 10.1038/s41586-024-07295-2

Figure Lengend Snippet: a , Distance distributions of spin-labelled µOR under different ligand conditions. b , Distance distributions in the presence of ligand and G i . c , Distance distributions of phosphorylated µOR (µORp) in the presence of ligand and pre-activated β-arrestin-1 (βarr1). a – c , Shaded areas along the line indicate 95% confidence interval. d , Gaussian populations centred around 26 Å, 33 Å, 39 Å and 43 Å. Data represent median population ± 95% confidence interval derived from bootstrapping analysis using n = 1,000 iterations. Populations marked with asterisks have non-overlapping confidence intervals in the presence and absence of transducer.

Article Snippet: DEER data were processed via Gaussian mixture models (GMM) implemented in Matlab (v.2019b) using the DEERlab toolbox (v.0.9.2) .

Techniques: Derivative Assay

Journal: Nature

Article Title: Ligand efficacy modulates conformational dynamics of the µ-opioid receptor

doi: 10.1038/s41586-024-07295-2

Figure Lengend Snippet: Populations from 6 Gaussian peaks of 30 DEER datasets are shown as scatter plot. Each blue dot represents one of the 30 samples. Red lines are the results of a linear fit. Numbers in each subpanel are corresponding correlation coefficients, which are labeled by a star (*) and red color if p < 0.05.

Article Snippet: DEER data were processed via Gaussian mixture models (GMM) implemented in Matlab (v.2019b) using the DEERlab toolbox (v.0.9.2) .

Techniques: Labeling, IF-P

Journal: Nature

Article Title: Ligand efficacy modulates conformational dynamics of the µ-opioid receptor

doi: 10.1038/s41586-024-07295-2

Figure Lengend Snippet: a , Schematic of single-molecule FRET experiment. Labelled µOR was tethered to a cover slip via its Flag tag, biotinylated M1 Fab, streptavidin (SA) and biotinylated PEG. TIRFM, total internal reflection fluorescence microscopy. b , c , SmFRET distributions of µOR–Cy3/Cy5 ( b ) and µOR–Cy3/Cy7 ( c ) in the presence of different ligands. Gaussian peaks were fitted to FRET states (red and blue) and background noise (black). Green lines represent the cumulative fitted distributions. Dashed lines in blue and red represent peak centres of naloxone- and DAMGO-bound samples, respectively ( n represents the number of fluorescence traces used to calculate the corresponding histograms). Data are mean ± s.d. from three repeats.

Article Snippet: DEER data were processed via Gaussian mixture models (GMM) implemented in Matlab (v.2019b) using the DEERlab toolbox (v.0.9.2) .

Techniques: FLAG-tag, Fluorescence, Microscopy

Journal: Nature

Article Title: Ligand efficacy modulates conformational dynamics of the µ-opioid receptor

doi: 10.1038/s41586-024-07295-2

Figure Lengend Snippet: a , b , µOR∆7-R182C/R273C is labeled with Cy3/Cy5. c – e , µOR∆7-R182C/R276C is labeled with Cy3/Cy5. f – h , µOR∆7-R182C/R276C is labeled with Cy3/Cy7. FRET distributions of µOR∆7-R182C/R276C labeled with Cy3/Cy5 (c) and Cy3/Cy7 (f). Error bars in c and f indicate s.d. from 3 repeats. FRET peak centers of µOR∆7-R182/R273 + Cy3/Cy5 (a, related to Fig. ), µOR∆7-R182/R276 + Cy3/Cy5 (d), and µOR∆7-R182/R276 + Cy3/Cy7 (g). The numbers on each bar are the peak centers extracted from the Gaussian fitting. Error bars indicate standard errors of the fitting. FRET values of each frame of µOR samples in Fig. (b), Extended Data Fig. 9c (e), and Extended Data Fig. 9f (h) are plotted as box-and-whisker plots. IQR, inter qaurtile range. The number of traces of each condition is indicated in the corresponding histograms. FRET efficiencies between 0.6 and 1.2 (b and e) and between 0 and 1.2 (h) were used for one-way ANOVA Tukey’s test. ***, p < 0.001. n.s., not significant.

Article Snippet: DEER data were processed via Gaussian mixture models (GMM) implemented in Matlab (v.2019b) using the DEERlab toolbox (v.0.9.2) .

Techniques: Labeling, Whisker Assay

Journal: Nature Computational Science

Article Title: Identifying patterns in amyotrophic lateral sclerosis progression from sparse longitudinal data

doi: 10.1038/s43588-022-00299-w

Figure Lengend Snippet: Input, training, and optimization of the Mixture of Gaussian Processes model.

Article Snippet: Input, training, and optimization of the Mixture of Gaussian Processes model. Extended Data Fig. 2 Clusters spanning 90% of all individuals in PROACT.

Techniques:

Journal: Earth's Future

Article Title: The Nonlinear and Distinct Responses of Ocean Heat Content and Anthropogenic Carbon to Ice Sheet Freshwater Discharge in a Warming Climate

doi: 10.1029/2024EF004475

Figure Lengend Snippet: (a) Normalized Root Mean Square Error (nRMSE) values between the Gaussian Predictor Response (GPR) predicted OHC ANTH and actual OHC ANTH generated by withholding one predictor at a time for the Control (gray), AIS (blue), GrIS (pink), and AGrIS (black) simulations. (b) Same as for panel (a) but for C ANTH .

Article Snippet: Here, we identify the different driving factors in the FW linear and nonlinear OHC ANTH and C ANTH responses using a predictive, Gaussian Process Regression (GPR) model in MATLAB's Regression Learner toolbox.

Techniques: Generated, Control