Biology subjects

Saon, M. S.

Publications and source records attributed to Saon, M. S..

3 recordsLinked to original sources

bioRxiv2025

Prevalence of dual-donating amines in key regions of functional RNAs

RNA performs many critical functions nearly all of which are enabled by complex hydrogen bonded structures. Nucleotides possess far fewer hydrogen bond donors than acceptors, and only the exocyclic amine can donate two H-bonds, suggesting a specialized role. To assess the prevalence and structural contexts of dual-donating amines within structured RNAs, we created a computational workflow that mines and analyzes experimental RNA-containing structures. We evaluated H-bonding in over 250,000 amines from more than 1,800 structures. Dual-donating amines were found most frequently in Gs where they regularly interacted with diverse pairs of acceptors. In contrast, the dual-donating amines of As and Cs were less frequent and they interacted with a more select set of acceptors. For all three nucleobases, amines that were dual- donating had both reduced solvent accessibility and higher atom density relative to amines that were non-donating, indicating a tendency of dual donors to be more buried and help compact the RNA. Moreover, analysis of RNA pseudo-torsion angles revealed that dual-donating amines are enriched in two non A-form conformations, both of which are present in S-motifs found in the sarcin-ricin loop of rRNA. We find that dual-donating amines populate additional structural motifs including the GNRA tetraloop receptor, the kink-turn, and the WC/H A-minor motif, which are present in the self-splicing group I intron, the SAM riboswitch, and the poly(A)-bound ENE. We suggest that dual-donating amines may enhance interactions by reducing conformational entropy loss as well as strengthening nearby H-bonds.

bioinformatics↗

bioRxiv2025

Identification and characterization of shifted GU wobble pairs resulting from alternative protonation of RNA

RNA can serve as an enzyme, small molecule sensor, and vaccine, and it may have been a conduit for the origin of life. Despite these profound functions, RNA is thought to have quite limited molecular diversity. A pressing question, therefore, is whether RNA can adopt novel molecular states that enhance its function. Covalent modifications of RNA have been demonstrated to augment biological function, but much less is known about non-covalent alterations such as novel protonated or tautomeric forms. Conventionally, a G*U wobble has the U shifted into the major groove. We used a cheminformatic approach to identify four structural families of shifted G*U wobbles in which the G instead resides in the major groove of RNA, which requires alternative tautomeric states of either base, or an anionic state of the U. We provide experimental support for these shifted G*U wobbles via the potent, and unconventional, in vivo reactivity of the U with dimethylsulfate (DMS) in three organisms. These shifted wobbles may play important functional roles and could serve as drug targets. Our cheminformatics approach is general and can be applied to identify alternative protonation states in other RNA motifs, as well as in DNA and proteins. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=59 SRC="FIGDIR/small/630957v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1921d65org.highwire.dtl.DTLVardef@1c8124borg.highwire.dtl.DTLVardef@28fbddorg.highwire.dtl.DTLVardef@af8d20_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

Exploring the Efficiency of Deep Graph Neural Networks for RNA Secondary Structure Prediction

Ribonucleic acid (RNA) plays a vital role in various biological processes and forms intricate secondary and tertiary structures associated with its functions. Predicting RNA secondary structures is essential for understanding the functional and regulatory roles of RNA molecules in biological processes. Traditional free-energy-based methods for predicting these structures often fail to capture complex interactions and long-range dependencies within RNA sequences. Recent advancements in machine learning, particularly with graph neural networks (GNNs), have shown promise in enhancing the ability to model the relationships between molecular sequences and their structures. This work specifically explores the efficacy of various GNN architectures in modeling RNA secondary structure. Through benchmarking the GNN methods against traditional energy-based models on standard datasets, our analysis demonstrates that GNN models improves traditional methods, offering a robust framework for accurate RNA structure prediction.

bioinformatics↗