DSSR

DSSR identifies and annotates features of RNA tertiary structures, including canonical and noncanonical base pairs (including modified nucleotides in any tautomeric or protonation state), multiplets, stacks, stems, helices, loops, and pseudoknots to characterize RNA three-dimensional architecture.

Key Features:

• Base-pair identification: Automated identification of canonical and noncanonical base pairs, accommodating modified nucleotides in any tautomeric or protonation state.
• Multiplet detection: Detection of higher-order coplanar base associations (multiplets).
• Stacking arrays classification: Identification and classification of arrays of stacked pairs by base-pair identity and backbone connectivity.
• Stem versus helix distinction: Differentiation between stems (covalently connected canonical pairs) and helices (stacked pairs of arbitrary types or linkages).
• Coaxial stacking recognition: Recognition of coaxial stacking among multiple stems within a single helix.
• Isolated pair cataloging: Cataloging of isolated canonical pairs that are not part of any stem.
• Loop characterization: Characterization of closed loop types including hairpin, bulge, internal, and junction loops.
• Pseudoknot handling: Detection and characterization of pseudoknots of arbitrary complexity.
• Junction loop definition: Definition of junction loops using isolated pairs and stem ends regardless of their involvement in pseudoknots.
• Validation on structural databases: Robustness demonstrated by extensive testing on all nucleic acid structures in the Protein Data Bank.

Scientific Applications:

• RNA folding analysis: Analysis of complex RNA folding patterns and higher-order interactions.
• Structure-function studies: Structural annotation to support investigations of RNA structure–function relationships.
• Modified nucleotide assessment: Examination of the roles and geometries of modified nucleotides in tertiary structure.
• Benchmarking and curation: Validation and benchmarking of RNA structural datasets derived from the Protein Data Bank.

Methodology:

Automated computational identification and annotation of base pairs, multiplets, stacked arrays, stems, helices, loops, and pseudoknots, with junction loops defined from isolated pairs and stem ends.