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

## TL;DR

This paper introduces CLEAPA, a new method for finding the most likely paths between different shapes of molecules seen in cryo-EM images.

## Contribution

The novel contribution is a framework using energy-aware pathfinding to explore high-dimensional conformational landscapes in cryo-EM.

## Key findings

- CLEAPA successfully identifies accurate transition states in synthetic and real datasets.
- The method uses local density to estimate energy and find minimum energy paths in graphs.
- It improves the exploration of conformational changes in complex biomolecules.

## Abstract

Cryo-electron microscopy (cryo-EM) is a powerful technique for studying macromolecules and holds the potential for identifying kinetically preferred transition sequences between conformational states. Typically, these sequences are explored within two-dimensional energy landscapes. However, due to the complexity of biomolecules, representing conformational changes in two dimensions can be challenging. Recent advancements in reconstruction models have successfully extracted structural heterogeneity from cryo-EM images using higher-dimension latent space. Nonetheless, creating high-dimensional conformational landscapes in the latent space and then searching for preferred paths continues to be a formidable task.

This study introduces an innovative framework for identifying preferred trajectories within high-dimensional conformational landscapes. Our method encompasses the search for the minimum energy path in the graph, where edge weights are determined based on the energy estimation at each node using local density. The effectiveness of this approach is demonstrated by identifying accurate transition states in both synthetic and real-world datasets featuring continuous conformational changes.

The CLEAPA package is available at https://github.com/tengyulin/energy_aware_pathfinding/.