RNA Design Using incaRNAfbinv Demonstrated with the Identification of Functional RNA Motifs in Hepatitis Delta Virus

Area of Science:

• Computational biology
• Molecular biology
• Bioinformatics

Background:

• Computational RNA design evolved from early inverse folding solvers like RNAinverse.
• Modern techniques offer enhanced efficiency and control over RNA sequence design.
• incaRNAfbinv builds upon RNAinverse, incorporating fragment-based approaches for RNA secondary structure design.

Purpose of the Study:

• To introduce incaRNAfbinv 2.0, a free web-server for advanced computational RNA design.
• To demonstrate the utility of fragment-based inverse RNA folding for both synthetic RNA design and functional motif discovery.
• To showcase the application of incaRNAfbinv 2.0 in identifying RNA motifs across different genomic contexts.

Main Methods:

• Decomposition of RNA secondary structures into functional motifs.
• Utilizing a fragment-based inverse RNA folding approach.
• Implementing flexible constraint insertion and motif selection within the incaRNAfbinv 2.0 web-server.

Main Results:

• The incaRNAfbinv 2.0 web-server provides precise control over RNA secondary structure motif design.
• Designed RNA sequences are ranked based on their similarity to known RNA structures.
• The tool successfully identified a functional RNA motif in hepatitis delta virus (HDV) across multiple genotypes.

Conclusions:

• Computational RNA design using inverse folding is effective for creating synthetic RNAs.
• This approach is also a valuable strategy for discovering functional RNA motifs within genomic data.
• incaRNAfbinv 2.0 enhances RNA design capabilities by integrating fragment-based methods and flexible constraints.