The Laboratory of biological electron microscopy and structural biology

(dr. fei sun’s lab)



A local-optimization refinement algorithm in single particle analysis for macromolecular complex with multiple rigid modules

Single particle analysis, which can be regarded as an average of signals from thousands or even millions of particle projections, is an efficient method to study the three-dimensional structures of biological macromolecules. An intrinsic assumption in single particle analysis is that all the analyzed particles must have identical composition and conformation. Thus specimen heterogeneity in either composition or conformation has raised great challenges for high-resolution analysis. For particles with multiple conformations, inaccurate alignments and orientation parameters will yield an averaged map with diminished resolution and smeared density. Besides extensive classification approaches, here based on the assumption that the macromolecular complex is made up of multiple rigid modules whose relative orientations and positions are in slight fluctuation around equilibriums, we propose a new method called as local optimization refinement to address this conformational heterogeneity for an improved resolution. The key idea is to optimize the orientation and shift parameters of each rigid module and then reconstruct their three-dimensional structures individually. Using simulated data of 80S/70S ribosomes with relative fluctuations between the large (60S/50S) and the small (40S/30S) subunits, we tested this algorithm and found that the resolutions of both subunits are significantly improved. Our method provides a proof-of-principle solution for high-resolution single particle analysis of macromolecular complexes with dynamic conformations.


Shan H., Wang Z., Zhang F., Xiong Y., Yin C.C.* and Sun F.* (2016), A local-optimization refinement algorithm in single particle analysis for macromolecular complex with multiple rigid modules. Proten Cell 7(1): 1-13. doi:10.1007/s13238-015-0229-2.

(In collaboration with Prof. Chang-cheng Yin, Peking University and Prof. Fa Zhang, Institute of Computation Technology, CAS)

The improvement by the LO-refinement procedure for the simulated dataset with SNR=0.11. (A) and (D) are the  comparisons of reconstructions for small subunits (A) and large subunits (D) of ribosome. (B) and (E) are the corresponding FSC curves respectively. (C) and (F) are the local resolution analyzes of the reconstructed density maps.