Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0

Area of Science:

• Computational biophysics
• Structural biology
• Bioinformatics

Background:

• Cryo-electron microscopy (cryoEM) enables structural determination of large, dynamic molecular complexes.
• Analyzing the dynamics of these complexes is challenging due to size, low resolution, and computational expense.
• There is a growing need for efficient computational methods to analyze structural dynamics.

Purpose of the Study:

• To review advancements in computational pipelines for analyzing global dynamics of supramolecular complexes.
• To highlight the role of coarse-grained models, elastic network models, and normal mode analysis (NMA).
• To focus on the ProDy Python API and its contributions to protein dynamics analysis.

Main Methods:

• Review of elastic network models (ENM) and normal mode analysis (NMA).
• Discussion of ensemble analyses like principal component analysis (PCA).
• Focus on ProDy, SignDy, and CryoDy pipelines for analyzing cryoEM data.

Main Results:

• Ensemble NMA, with the SignDy pipeline, facilitates comparison of dynamics in homologous structures.
• Pseudoatom fitting, within the CryoDy pipeline, improves global dynamics analysis for large, low-resolution cryoEM assemblies.
• ProDy provides a robust platform for these advanced computational dynamics analyses.

Conclusions:

• Renewal and extension of established models and methods in new pipelines are crucial for advancing cryoEM data analysis.
• These computational strategies are essential for interpreting the dynamics of complex biological systems.
• The discussed pipelines offer efficient solutions for the next era of cryoEM research.