PKNOTS

PKNOTS predicts RNA secondary structures including pseudoknots using a dynamic programming algorithm to compute minimum free-energy configurations for studies of RNA folding and structure–function relationships.

Key Features:

• Dynamic programming algorithm: Employs a dynamic programming approach to predict optimal RNA secondary structures with respect to minimum energy using established RNA thermodynamic models.
• Computational complexity: Operates with worst-case time complexity O(N^6) and storage complexity O(N^4).
• Graphical representation: Utilizes Feynman diagrams to represent and visualize the combinatorial structure of pseudoknot-containing foldings.
• Thermodynamic parameters: Integrates standard RNA folding thermodynamics with additional parameters specifically tailored to pseudoknot stability.
• Implementation and validation: Generates optimal minimum-energy structures for individual RNA sequences and has been demonstrated on several small RNAs with and without pseudoknots.

Scientific Applications:

• RNA structure-function studies: Enables modeling of pseudoknotted and non-pseudoknotted secondary structures to support investigations of RNA folding, function, and implications in biological processes and disease mechanisms.

Methodology:

Applies a dynamic programming algorithm to compute minimum-energy structures using standard and pseudoknot-specific thermodynamic parameters and represents combinatorial contributions via Feynman diagrams.