RNA Secondary Structure Prediction with Pseudoknots Using Chemical Reaction Optimization Algorithm.

Area of Science:

• Molecular Biology
• Computational Biology
• Bioinformatics

Background:

• RNA molecules are crucial for cellular functions, with pseudoknots playing a significant role.
• Predicting RNA secondary structures, particularly those with pseudoknots, is computationally complex (NP-hard).
• Minimum free energy (MFE) is a common approach for RNA structure prediction.

Purpose of the Study:

• To develop an efficient and accurate method for predicting RNA secondary structures with pseudoknots.
• To address the computational challenges associated with RNA pseudoknotted structure prediction.

Main Methods:

• A metaheuristic algorithm, Chemical Reaction Optimization (CRO), was adapted for RNA pseudoknotted structure prediction.
• Redesigned CRO reaction operators and introduced a 'Repair' operator to enhance accuracy.
• Evaluated performance using four energy models and datasets from RNA STRAND and Pseudobase++ databases.

Main Results:

• The proposed CRO-based approach demonstrated improved accuracy in predicting true positive base pairs.
• The algorithm effectively reduced false positive and false negative base pairs.
• Comparative analysis showed the CRO model outperforms existing algorithms in both accuracy and speed.

Conclusions:

• The Chemical Reaction Optimization (CRO) based approach offers a superior method for RNA pseudoknotted structure prediction.
• The enhanced CRO algorithm with the Repair operator provides a significant advancement in accuracy and efficiency.