Abstract:
The cryo-electron microscopy (cryo-EM) technique captures 2D projections of biological structures, revealing their inherent Heterogeneity arising from structural flexibility, conformational changes, and distinct functional states. Factors such as ligand binding, conformational rearrangements, and variations in subunit composition can influence this variability. Accurate identification and understanding of this Heterogeneity play a pivotal role in unraveling the intricate structure-function relationships that govern biological processes and facilitating the development of targeted therapeutic interventions. Researchers have developed numerous computational approaches to address the heterogeneity challenge in cryo-EM. These methods aim to extract distinct conformations from heterogeneous datasets, enabling the resolution of underlying structural variability. These approaches unveil hidden details and provide deeper mechanistic insights by generating high-resolution 3-D reconstructions of individual states within a heterogeneous sample. In this research project, our objective was to identify the flexible and fixed regions of the LDL protein. We employed principal component analysis based on the singular value decomposition (SVD) algorithm, which enhances the resolution of the 3-D volume and facilitates the classification of 2-D images using EMAN2. Our study successfully obtained different 3-D volumes with improved resolution through multiple refinement iterations. Further analysis identified two highly variable principal components, allowing us to distinguish between the fixed and variable parts of the protein structure.
