Clustering DNA and RNA molecular dynamics ensembles via secondary structure

Area of Science:

• Structural Biology
• Biophysics
• Computational Biology

Background:

• Macromolecular structure is fundamental to biological function.
• Many biomolecules, including nucleic acids, possess intrinsically disordered regions (IDRs) that lack a stable fold.
• Entropic disorder in nucleic acids arises from incomplete base pairing and molecular length exceeding persistence length.

Purpose of the Study:

• To develop a novel method for classifying and clustering secondary structures of intrinsically disordered nucleic acid regions.
• To provide a quantitative approach for analyzing the conformational ensembles of DNA and RNA.
• To better understand the role of entropic disorder in nucleic acid function.

Main Methods:

• Developed a clustering method based on secondary structure analysis for DNA and RNA.
• Utilized the number of base pairs to reorganize as a distance metric for structures with the same topology.
• Applied k-means, hierarchical, and density-based clustering algorithms to analyze structural ensembles.

Main Results:

• Demonstrated the broad distribution of secondary structures in a M13 bacteriophage DNA fragment.
• Revealed hidden order within an RNA Holliday junction structure.
• Showcased the method's ability to recognize structures differing only by internal reorientation.

Conclusions:

• The developed clustering approach effectively classifies secondary structures of intrinsically disordered nucleic acids.
• This method connects structural clustering to hybridization energy barriers, organizing the free-energy landscape.
• Provides a powerful tool for analyzing the complex conformational dynamics of DNA and RNA.