Capsid Expansion Mechanism Of Bacteriophage T7 Revealed By Multi-State Atomic Models Derived From Cryo-EM Reconstructions
ELECTRON MICROSCOPY
Sample
Bacteriophage T7 MLD capsid II
Specimen Preparation
Sample Aggregation State
PARTICLE
Vitrification Instrument
FEI VITROBOT MARK I
Cryogen Name
ETHANE
Sample Vitrification Details
Blot for 2 seconds twice with 2 mm offset before plunging into liquid ethane (FEI VITROBOT MARK I).
3D Reconstruction
Reconstruction Method
SINGLE PARTICLE
Number of Particles
43417
Reported Resolution (Å)
3.5
Resolution Method
FSC 0.143 CUT-OFF
Other Details
Particles were selected from scanned micrograph images, first automatically by the ethan method and then by manual screening with the boxer program in ...
Particles were selected from scanned micrograph images, first automatically by the ethan method and then by manual screening with the boxer program in EMAN. The TEM instrument contrast transfer function parameters were determined automatically using fitctf2.py and were then visually validated using the EMAN ctfit program. The datasets were then divided into two subsets (even and odd) and processed completely independently, including both initial models and refinements. For 3D reconstructions, the whole datasets were divided into even-odd halves and the initial de novo models and subsequent iterative refinements were all independently performed for each half dataset. The images were first binned 4x to obtain initial models and particle parameters assuming icosahedral symmetry. De novo initial models were built using the random model approach. Random subsets of particles were assigned random initial orientations and iteratively refined until convergence. Consistent icosahedral capsid structures (other than occasional differences in handedness) were obtained by repeating the random model process. Particles with inconsistent/unstable view parameters in the initial refinements were excluded in further image processing. The orientation and center parameters were then transferred to the un-binned images for high-resolution refinements which included Simplex method-based orientation/center optimization and grid search-based refinement of defocus, astigmatism, and magnification of the images. All image refinement and reconstructions were performed with in-house developed programs jspr.py (for overall work-flow), jalign (for 2D alignment) and j3dr (for 3D reconstruction), which use EMAN and EMAN2 library functions.
