CLEAPA: a framework for exploring the conformational landscape of cryo-EM using energy-aware pathfinding algorithm.

Area of Science:

• Structural Biology
• Computational Biology
• Biophysics

Background:

• Cryo-electron microscopy (cryo-EM) enables studying macromolecular conformational changes.
• Representing complex biomolecular dynamics in 2D energy landscapes is challenging.
• High-dimensional latent spaces from cryo-EM offer richer conformational information.

Purpose of the Study:

• To develop a novel framework for identifying preferred transition pathways in high-dimensional conformational landscapes.
• To address the challenge of searching for kinetically preferred sequences in complex biomolecular systems.

Main Methods:

• Introduced a graph-based approach for minimum energy path searching.
• Utilized local density estimation for determining edge weights in the conformational landscape graph.
• Applied the method to both synthetic and real-world cryo-EM datasets.

Main Results:

• Successfully identified accurate transition states in continuous conformational changes.
• Demonstrated the framework's effectiveness on complex datasets.
• The method facilitates exploration of high-dimensional conformational dynamics.

Conclusions:

• The developed framework provides an effective means to navigate and analyze high-dimensional conformational landscapes.
• This approach enhances the utility of cryo-EM data for understanding molecular mechanisms.
• The CLEAPA package is available for broader application.