To knot or not to knot: Multiple conformations of the SARS-CoV-2 frameshifting RNA element

The SARS-CoV-2 Programmed -1 Ribosomal Frameshifting Element Crystal Structure Solved to 2.09 Å Using Chaperone-Assisted RNA Crystallography

The programmed -1 ribosomal frameshifting element (PFSE) of SARS-CoV-2 is a well conserved structured RNA found in all coronaviruses' genomes. By adopting a pseudoknot structure in the presence of the ribosome, the PFSE promotes a ribosomal frameshifting event near the stop codon of the first open reading frame Orf1a during translation of the polyprotein pp1a. Frameshifting results in continuation of pp1a via a new open reading frame, Orf1b, that produces the longer pp1ab polyprotein. Polyproteins pp1a and pp1ab produce nonstructural proteins NSPs 1-10 and NSPs 1-16, respectively, which contribute vital functions during the viral life cycle and must be present in the proper stoichiometry. Both drugs and sequence alterations that affect the stability of the -1 programmed ribosomal frameshifting element disrupt the stoichiometry of the NSPs produced, which compromise viral replication. For this reason, the -1 programmed frameshifting element is considered a promising drug target. Using chaperone assisted RNA crystallography, we successfully crystallized and solved the three-dimensional structure of the PFSE. We observe a three-stem H-type pseudoknot structure with the three stems stacked in a vertical orientation stabilized by two triple base pairs at the stem 1/stem 2 and stem 1/stem 3 junctions. This structure provides a new conformation of PFSE distinct from the bent conformations inferred from midresolution cryo-EM models and provides a high-resolution framework for mechanistic investigations and structure-based drug design.

A Fiedler Vector Scoring Approach for Novel RNA Motif Selection

Novel RNA motif design is of great practical importance for technology and medicine. Increasingly, computational design plays an important role in such efforts. Our coarse-grained RAG (RNA-As-Graphs) framework offers strategies for enumerating the universe of RNA 2D folds, selecting "RNA-like" candidates for design, and determining sequences that fold onto these candidates. In RAG, RNA secondary structures are represented as tree or dual graphs. Graphs with known RNA structures are called "existing", and the others are labeled "hypothetical". By using simplified features for RNA graphs, we have clustered the hypothetical graphs into "RNA-like" and "non-RNA-like" groups and proposed RNA-like graphs as candidates for design. Here, we propose a new way of designing graph features by using Fiedler vectors. The new features reflect graph shapes better, and they lead to a more clustered organization of existing graphs. We show significant increases in K-means clustering accuracy by using the new features (e.g., up to 95% and 98% accuracy for tree and dual graphs, respectively). In addition, we propose a scoring model for top graph candidate selection. This scoring model allows users to set a threshold for candidates, and it incorporates weighing of existing graphs based on their corresponding number of known RNAs. We include a list of top scored RNA-like candidates, which we hope will stimulate future novel RNA design.