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Optical Tweezers to Study RNA-Protein Interactions in Translation Regulation

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Predicting Protein-Nucleic Acid Flexibility Using Persistent Sheaf Laplacians.

Nicole Hayes, Ekaterina Merkurjev, Guo-Wei Wei

|November 24, 2025

Summary

This summary is machine-generated.

The Persistent Sheaf Laplacian (PSL) framework accurately predicts protein-nucleic acid complex flexibility using atomic B-factors. PSL outperforms traditional models like GNM and ENM, offering improved insights into biomolecular dynamics.

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Area of Science:

Structural Biology
Computational Biology
Biophysics

Background:

Protein-nucleic acid complex flexibility, measured by B-factors, is crucial for understanding structure, dynamics, and function.
Traditional models like Gaussian Network Models (GNM) and Elastic Network Models (ENM) struggle with multiscale interactions in large biomolecular systems.

Purpose of the Study:

To apply the Persistent Sheaf Laplacian (PSL) framework for enhanced B-factor prediction in protein-nucleic acid complexes.
To evaluate PSL's performance against established methods for modeling biomolecular flexibility.

Main Methods:

Utilized the Persistent Sheaf Laplacian (PSL) framework, integrating multiscale analysis, algebraic topology, and sheaf theory.
Applied PSL to diverse datasets including protein-RNA and nucleic-acid-only structures.
Benchmarked PSL against Gaussian Network Models (GNM) and multiscale Flexibility/Rigidity Index (mFRI).

Main Results:

PSL demonstrated superior B-factor prediction accuracy compared to GNM and mFRI.
Achieved up to a 21% improvement in Pearson correlation coefficient for B-factor prediction.
PSL effectively captures topological invariants and homotopic shape evolution in biomolecular data.

Conclusions:

The Persistent Sheaf Laplacian (PSL) framework offers a robust and adaptable method for modeling complex biomolecular interactions.
PSL shows significant potential for applications in mutation impact analysis and drug design.
PSL provides more accurate B-factor predictions, enhancing the understanding of protein-nucleic acid complex dynamics.