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Predicting protein-nucleic acid flexibility using persistent sheaf Laplacians.

Nicole Hayes1, Ekaterina Merkurjev1,2, Guo-Wei Wei1,3,4

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We introduce the Persistent Sheaf Laplacian (PSL) for predicting atomic B-factors in protein-nucleic acid complexes. PSL offers superior accuracy over traditional models, enhancing our understanding of biomolecular dynamics and function.

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Area of Science:

  • Biophysics
  • Computational Biology
  • Structural Biology

Background:

  • Protein-nucleic acid complex flexibility, measured by atomic 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 accurate B-factor prediction in protein-nucleic acid complexes.
  • To evaluate PSL's performance against established methods on diverse biological datasets.

Main Methods:

  • Utilized the Persistent Sheaf Laplacian (PSL) framework, integrating multiscale analysis, algebraic topology, combinatorial Laplacians, and sheaf theory.
  • Benchmarked PSL against GNM and multiscale FRI (mFRI) on protein-RNA and nucleic-acid-only structures.

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 PSL framework provides a robust and adaptable method for modeling complex biomolecular interactions.
  • PSL's accuracy in B-factor prediction highlights its potential for applications in mutation impact analysis and drug design.