Generative Tertiary Structure-based RNA Design. (arXiv:2301.10774v1 [q-bio.BM])

Learning from 3D biological macromolecules with artificial intelligence
technologies has been an emerging area. Computational protein design, known as
the inverse of protein structure prediction, aims to generate protein sequences
that will fold into the defined structure. Analogous to protein design, RNA
design is also an important topic in synthetic biology, which aims to generate
RNA sequences by given structures. However, existing RNA design methods mainly
focus on the secondary structure, ignoring the informative tertiary structure,
which is commonly used in protein design. To explore the complex coupling
between RNA sequence and 3D structure, we introduce an RNA tertiary structure
modeling method to efficiently capture useful information from the 3D structure
of RNA. For a fair comparison, we collect abundant RNA data and split the data
according to tertiary structures. With the standard dataset, we conduct a
benchmark by employing structure-based protein design approaches with our RNA
tertiary structure modeling method. We believe our work will stimulate the
future development of tertiary structure-based RNA design and bridge the gap
between the RNA 3D structures and sequences.