EXPECTATION-MAXIMIZATION FOR STRUCTURE DETERMINATION DIRECTLY FROM CRYO-EM MICROGRAPHS

Area of Science:

• Structural Biology
• Biophysics
• Computational Biology

Background:

• Single-particle cryo-electron microscopy (cryo-EM) generates 3-D molecular structures from 2-D projections.
• Existing cryo-EM pipelines struggle with low signal-to-noise ratio (SNR) data, especially for small molecules, due to difficulties in detecting projection images.

Purpose of the Study:

• To develop a novel computational method for reconstructing 3-D molecular structures from cryo-EM data with low SNR.
• To overcome the limitations of current algorithms in accurately detecting projection images from noisy micrographs of small molecules.

Main Methods:

• An approximate expectation-maximization algorithm was devised to directly estimate the 3-D structure from cryo-EM micrographs.
• The proposed method bypasses the conventional step of locating and extracting individual projection images.

Main Results:

• The computational scheme was validated through numerical experiments.
• Successful recovery of molecular structures from simulated noisy measurements was demonstrated, particularly in low-SNR regimes.

Conclusions:

• The developed algorithm enables direct 3-D structure estimation from cryo-EM micrographs, effectively addressing challenges posed by low SNR.
• This approach offers a promising solution for determining the structures of small molecules that are difficult to analyze with existing cryo-EM techniques.