LCTC

LCTC quantifies structural conformational distributions in heterogeneous cryogenic electron microscopy (cryo-EM) datasets by assigning experimental 2D images to template 3D structures derived from Multi-body Re-refinement of RELION.

Key Features:

• Template assignment: Assigns experimental 2D cryo-EM images to template 3D structures derived through Multi-body Re-refinement of RELION.
• Two-stage matching algorithm: Performs an initial rapid assignment using auto-correlation functions of image contours to filter images, followed by precise pixel-pixel matching between remaining 2D images and template images.
• Quantification of conformational populations: Computes distributions of structural conformations, including clamp-opening states of RNA polymerase.
• Validation and benchmarking: Reproduced known distributions in simulated Thermus aquaticus RNAP datasets and showed improved performance relative to clustering-based Manifold Embedding and Maximum Likelihood-based Multi-body Re-refinement algorithms.
• Application to experimental data: Applied to an Escherichia coli RNAP cryo-EM dataset to reveal populations of clamp-opening conformations.

Scientific Applications:

• Distribution quantification: Quantifying structural conformational distributions in heterogeneous cryo-EM datasets.
• Conformational ensemble analysis: Analyzing clamp-opening conformational ensembles of Thermus aquaticus and Escherichia coli RNA polymerase (RNAP).
• Algorithm benchmarking: Benchmarking and comparing performance against Manifold Embedding and Maximum Likelihood-based Multi-body Re-refinement approaches.

Methodology:

Assigns 2D images to template 3D structures from Multi-body Re-refinement of RELION using a two-stage matching algorithm: (1) rapid auto-correlation-based contour matching to filter images and (2) pixel-pixel matching for precise assignment.