Tying the knot: Unraveling the intricacies of the coronavirus frameshift pseudoknot

SARS-CoV-2 point mutations are over-represented in terminal loops of RNA stem-loop structures that can be resolved by Nsp13 helicase in a unique manner with respect to nucleotide dependence

To improve health outcomes for COVID-19 (coronavirus disease 2019) patients, the factors that influence coronavirus genome variation need to be ascertained. The SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) genome is rich in predicted RNA secondary structures, particularly stem-loops (SLs) formed by intramolecular base pairing within palindromic sequences. We analyzed the NCBI Virus collection of SARS-CoV-2 genome sequences from COVID-19 individuals to map variants relative to SL structural elements. Point mutations in the SARS-CoV-2 genome, with a C-to-U transition bias, were over-represented in unpaired nucleotides and, more specifically, within the terminal loops of RNA SL structures. As the sole helicase encoded by SARS-CoV-2, Nsp13 may operate in the timely resolution of secondary RNA structures to facilitate SARS-CoV-2 RNA copying or processing. We characterized Nsp13 to resolve SARS-CoV-2 sequence-derived unimolecular RNA SL substrates and determined that it does so in a functionally cooperative manner. In addition to ATP, Nsp13 resolves the unimolecular RNA SL structure in the absence of nucleotide, in contrast to the strict ATP requirement for a bimolecular RNA forked duplex. We suggest a model in which a series of binary and ternary complex interactions of Nsp13 with nucleotide and/or RNA SL pose mechanistic implications for RNA SL resolution.

CParty: hierarchically constrained partition function of RNA pseudoknots

MOTIVATION

Biologically relevant RNA secondary structures are routinely predicted by efficient dynamic programming algorithms that minimize their free energy. Starting from such algorithms, one can devise partition function algorithms, which enable stochastic perspectives on RNA structure ensembles. As the most prominent example, McCaskill's partition function algorithm is derived from pseudoknot-free energy minimization. While this algorithm became hugely successful for the analysis of pseudoknot-free RNA structure ensembles, as of yet there exists only one pseudoknotted partition function implementation, which covers only simple pseudoknots and comes with a borderline-prohibitive complexity of O(n5) in the RNA length n.

RESULTS

Here, we develop a partition function algorithm corresponding to the hierarchical pseudoknot prediction of HFold, which performs exact optimization in a realistic pseudoknot energy model. In consequence, our algorithm CParty carries over HFold's advantages over classical pseudoknot prediction in characterizing the Boltzmann ensemble at equilibrium. Given an RNA sequence S and a pseudoknot-free structure G, CParty computes the partition function over all possibly pseudoknotted density-2 structures G∪G' of S that extend the fixed G by a disjoint pseudoknot-free structure G'. Thus, CParty follows the common hypothesis of hierarchical pseudoknot formation, where pseudoknots form as tertiary contacts only after a first pseudoknot-free "core" G and we call the computed partition function hierarchically constrained (by G). Like HFold, the dynamic programming algorithm CParty is very efficient, achieving the low complexity of the pseudoknot-free algorithm, i.e. cubic time and quadratic space. Finally, by computing pseudoknotted ensemble energies, we unveil kinetics features of a therapeutic target in SARS-CoV-2.

AVAILABILITY AND IMPLEMENTATION

CParty is available at https://github.com/HosnaJabbari/CParty.