Negative-stain EM

For negative-stain EM, 5 μl of sample (protein concentration ranging from ~4 to 12 μg/ml) was incubated on glow-discharged carbon-coated copper grids (homemade carbon layer) for 1 min at room temperature and then washed twice in 100-μl drops of H₂O. Excess H₂O was removed by touching the rim of the grids with blotting paper. The grids were then stained with 5 μl of 2% uranyl acetate solution for 1 min at room temperature, and excess uranyl acetate solution was removed by touching the rim of the grids with blotting paper. Datasets were collected using a Titan Halo (FEI) equipped with a Falcon 2 detector in nanoprobe mode at an operating voltage of 300 kV. Micrographs were recorded with EPU software (Thermo Fisher Scientific) at ×58,000 magnification (1.847 Å per pixel) with varying defoci from −1.5 to −2.5 μm in 0.5-μm steps at an exposure of 4 s with 10 frames per micrograph and a total dose of 105 e/Ų. Recorded, gain-normalized frames were aligned, motion-corrected, and dose-weighted using MotionCor2 software (58). Defocus values were assessed using either Gctf (59) or CTFFIND4 software (60). Using RELION3 (46), ~300 particles were manually picked and boxed out with a box size of 240 pixels. 2D alignment and classification were then performed. The resulting five initial 2D class averages were used as templates for automated picking with Gautomatch software (www2.mrc-lmb.cam.ac.uk/research/locally-developed-software/zhang-software/). For all quantifications, 20,000 particles (with a box size of 340 pixels) were aligned and 2D classified in RELION3. After the first round of 2D classification, ~10,000 particles with clear signals were selected for the second round, which was used for quantification of the particles into the bent, intermediate, and extended conformational states (related to Fig. 7C). For the representative 2D classes displayed in fig. S1C, the whole dataset of resting ND-α₅β₁ (81,249 particles in total) were boxed out (with a box size of 240 pixels) and submitted to alignment and 2D classification in RELION3.