Mechanistic insights into ASO-RNA complexation: Advancing antisense oligonucleotide design strategies

Area of Science:

• Biochemistry
• Molecular Biology
• Drug Discovery

Background:

• Oligonucleotide drugs, including antisense oligonucleotides (ASOs), are an emerging class of therapeutics targeting previously undruggable biomacromolecules.
• Current ASO design primarily focuses on Watson-Crick base pairing, potentially overlooking the impact of messenger RNA (mRNA) three-dimensional structures on drug efficacy.
• The U.S. Food and Drug Administration has approved only 18 oligonucleotide drugs to date, highlighting the need for improved design strategies.

Purpose of the Study:

• To investigate the impact of incorporating mRNA structural properties, specifically triple base pairs and hairpin motifs, into ASO design.
• To explore how ASO length and hairpin loop mutations affect ASO-mRNA complex stability.
• To provide mechanistic insights into ASO-mRNA complexation for improved ASO lead discovery.

Main Methods:

• Utilized atomistic molecular dynamics simulations, guided by experimental data, to model ASO-RNA interactions.
• Designed and analyzed ASO-hairpin complexes, including a modified RNA pseudoknot.
• Investigated the influence of ASO length, triple base pair incorporation, and hairpin loop mutations on complex stability.

Main Results:

• Incorporating common triple base pairs into ASO design targeting RNA hairpin motifs enhances ASO efficacy.
• ASO-mRNA complex stability is significantly influenced by ASO length, the number of triple base pairs, and the dynamic accessibility of hairpin loop bases.
• RNA pseudoknots are valuable for creating training datasets for machine learning models in ASO design.

Conclusions:

• Integrating mRNA's three-dimensional structural features, beyond simple base pairing, is crucial for effective ASO design.
• The study provides novel mechanistic understanding of ASO-mRNA complexation, paving the way for more efficient drug discovery.
• The findings support the use of pseudoknots in machine learning-based ASO design strategies.