Construction of a Tm-value prediction model and molecular dynamics study of AmNA-containing gapmer antisense oligonucleotide

• Published in: Molecular therapy. Nucleic acids 2024-09, Vol.35 (3), p.102272, Article 102272
• Main Authors: Kuroda, Masataka, Kasahara, Yuuya, Hirose, Masako, Yamaguma, Harumi, Oda, Masayuki, Nagao, Chioko, Mizuguchi, Kenji
• Format: Article
• Language: English
• Subjects: amido-bridged nucleic acid, AmNA; machine learning; molecular dynamics, MD; MT: Bioinformatics; Original; RNase H-dependent antisense oligonucleotide, gapmer ASO; Tm value prediction
• ISSN: 2162-2531; 2162-2531
• DOI: 10.1016/j.omtn.2024.102272

RNase H-dependent antisense oligonucleotides (gapmer ASOs) represent a class of nucleic acid therapeutics that bind to target RNA to facilitate RNase H-mediated RNA cleavage, thereby regulating the expression of disease-associated proteins. Integrating artificial nucleic acids into gapmer ASOs enhances their therapeutic efficacy. Among these, amido-bridged nucleic acid (AmNA) stands out for its potential to confer high affinity and stability to ASOs. However, a significant challenge in the design of gapmer ASOs incorporating artificial nucleic acids, such as AmNA, is the accurate prediction of their melting temperature (Tm) values. The Tm is a critical parameter for designing effective gapmer ASOs to ensure proper functioning. However, predicting accurate Tm values for oligonucleotides containing artificial nucleic acids remains problematic. We developed a Tm prediction model using a library of AmNA-containing ASOs to address this issue. We measured the Tm values of 157 oligonucleotides through differential scanning calorimetry, enabling the construction of an accurate prediction model. Additionally, molecular dynamics simulations were used to elucidate the molecular mechanisms by which AmNA modifications elevate Tm, thereby informing the design strategies of gapmer ASOs. [Display omitted] Mizuguchi and colleagues present a groundbreaking Tm prediction model for AmNA-modified gapmer ASOs, enhancing RNA-targeted therapeutics. Using DSC and MD simulations, the study reveals the model’s precision and the molecular basis of AmNA’s stabilizing effect, offering significant insights for ASO design.