A fast and robust iterative algorithm for prediction of RNA pseudoknotted secondary structures

Area of Science:

• Computational biology
• Bioinformatics
• Molecular biology

Background:

• Predicting RNA secondary structures with pseudoknots presents accuracy and efficiency challenges.
• Existing free energy minimization methods are slow and limited in pseudoknot prediction.
• Few methods incorporate known structural information, and their robustness with partial data is poorly understood.

Purpose of the Study:

• To develop a novel computational method for accurate and efficient prediction of pseudoknotted RNA secondary structures.
• To assess the robustness of the new method when provided with partial structural information.
• To compare the performance of the new method against existing state-of-the-art tools.

Main Methods:

• Introduced Iterative HFold, a new method for pseudoknotted RNA secondary structure prediction.
• Iterative HFold integrates the speed of HFold with the energy parameters of HotKnots V2.0.
• The method accepts pseudoknot-free structures as input to generate low-energy pseudoknotted structures.

Main Results:

• Iterative HFold demonstrates robustness with partial input information, with accuracy increasing from 54% to 79% as input increases to 40%.
• The method is significantly faster than HotKnots V2.0 with comparable accuracy.
• Iterative HFold shows superior accuracy compared to IPknot on benchmark datasets.

Conclusions:

• Iterative HFold offers a robust and efficient approach for pseudoknotted RNA secondary structure prediction.
• Accuracy surpasses IPknot with >5% input information and rivals HotKnots V2.0 with ~35% input, while being substantially faster.
• The Iterative HFold method and associated data are publicly available for research use.