Challenges and approaches to predicting RNA with multiple functional structures.

Area of Science:

• Computational Biology
• Structural Biology
• Genomics

Background:

• Advancements in sequencing technology necessitate novel computational tools for genetic code interpretation.
• In vivo chemical probing techniques are evolving, presenting new challenges for RNA folding prediction.
• The paradigm is shifting from predicting a single minimum free energy RNA structure to exploring structural ensembles.

Purpose of the Study:

• To review efficient methods for predicting RNA structures with multiple functional folds.
• To discuss approaches for analyzing thermodynamic ensembles of RNA structures.
• To highlight examples of viral RNA regions exhibiting conformational diversity.

Main Methods:

• Description of an efficient combinatorially complete method for RNA structure prediction.
• Overview of three free energy minimization approaches for predicting multi-ple functional RNA folds.
• Presentation of two methods for analyzing thermodynamics-based Boltzmann ensembles of RNA structures.

Main Results:

• Viral RNA 3'-untranslated regions (3'-UTR) can fold into multiple conformations.
• Experimental characterization using single-molecule fluorescence resonance energy transfer (smFRET) and NMR spectroscopy validates multi-conformation predictions.
• The review provides examples illustrating challenges in predicting structure-function relationships for RNAs with diverse biological roles.

Conclusions:

• Predicting RNA structural ensembles is crucial for understanding function.
• Further development of well-defined examples and metrics is needed to improve RNA structure and function prediction from sequence.
• This work guides future improvements in computational RNA structure and function prediction.