Progress toward SHAPE Constrained Computational Prediction of Tertiary Interactions in RNA Structure.

Area of Science:

• Biochemistry
• Computational Biology
• Molecular Biology

Background:

• Accurate RNA structure modeling is essential for understanding genetic regulation, but challenges remain due to RNA flexibility and complex interactions like pseudoknots.
• Current thermodynamic models and computational methods struggle to reliably predict large RNA structures, especially those involving non-canonical base pairs.

Purpose of the Study:

• To investigate the utility of chemical probing, specifically SHAPE reactivity, in identifying nucleotides involved in pseudoknots and non-canonical interactions within large RNA molecules.
• To explore how varying experimental conditions (reagents, Mg2+ presence, temperature) and molecular dynamics simulations can enhance the accuracy of RNA structure prediction.

Main Methods:

• Scrutinized SHAPE (Selective 2'-hydroxyl acylation analyzed by primer extension) reactivity of each nucleotide in an 188 nt lariat-capping ribozyme under multiple conditions.
• Analyzed reactivity data in conjunction with X-ray structures and performed molecular dynamics simulations to rationalize nucleotide behavior and identify complex interactions.
• Investigated probing at increasing temperatures and with different reagents, including the presence or absence of Mg2+.

Main Results:

• SHAPE reactivities accurately reported nucleotide status when analyzed with X-ray structures, with paradoxical reactivities explained by molecular dynamics simulations.
• Valuable information on intricate RNA interactions was obtained by probing with different reagents and varying Mg2+ concentrations.
• Probing at increasing temperatures proved highly effective in identifying non-canonical interactions and pseudoknot pairings.

Conclusions:

• Chemical probing, particularly SHAPE, combined with molecular dynamics, provides crucial insights into complex RNA structures, including pseudoknots and non-canonical interactions.
• Optimizing probing strategies, such as varying temperature and reagent conditions, significantly enhances the ability to deduce intricate RNA structural features.
• These findings offer a pathway to improve computational RNA structure modeling software by providing more accurate input data.