ScanFold 2.0: a rapid approach for identifying potential structured RNA targets in genomes and transcriptomes

Abstract

A major limiting factor in target discovery for both basic research and therapeutic intervention is the identification of structural and/or functional RNA elements in genomes and transcriptomes. This was the impetus for the original ScanFold algorithm, which provides maps of local RNA structural stability, evidence of sequence-ordered (potentially evolved) structure, and unique model structures comprised of recurring base pairs with the greatest structural bias. A key step in quantifying this propensity for ordered structure is the prediction of secondary structural stability for randomized sequences which, in the original implementation of ScanFold, is explicitly evaluated. This slow process has limited the rapid identification of ordered structures in large genomes/transcriptomes, which we seek to overcome in this current work introducing ScanFold 2.0. In this revised version of ScanFold, we no longer explicitly evaluate randomized sequence folding energy, but rather estimate it using a machine learning approach. For high randomization numbers, this can increase prediction speeds over 100-fold compared to ScanFold 1.0, allowing for the analysis of large sequences, as well as the use of additional folding algorithms that may be computationally expensive. In the testing of ScanFold 2.0, we re-evaluate the Zika, HIV, and SARS-CoV-2 genomes and compare both the consistency of results and the time of each run to ScanFold 1.0. We also re-evaluate the SARS-CoV-2 genome to assess the quality of ScanFold 2.0 predictions vs several biochemical structure probing datasets and compare the results to those of the original ScanFold program.

Structure of the SARS-CoV-2 Frameshift Stimulatory Element with an Upstream Multibranch Loop

“…We derived the SARS-CoV-2 FSE structure from our previous secondary structure analysis of the reference sequence genome (NC_045512.2) . The only modification was a GC-base pair swap (C13426G G13475C) to improve later T7 polymerase efficiency; refolding of this swapped sequence with ScanFold 2.0 showed no change in predicted base pairing. A similar modification was used to crystallize a PK-only structure, although only the PK structure was crystallized, as the AH and HSS were seen as too flexible to provide sufficient crystallographic efficiency .…”

“…Despite emerging in only 2019, the SARS-CoV-2 FSE has already been studied in several important contexts, to name a few: bioinformatic and computational techniques, − ,,,,− chemical probing, ,,− ,, dual luciferase assays, ,,,,, SAXS, optical tweezers, genome-wide knockout screens, , crystallography, , and cryo-EM. , No work, however, has yet attempted to model the tertiary interactions of the SARS-CoV-2 FSE with an intact AH. Our previous in silico work located the AH and HSS within a multibranch loop (Figure ).…”

Speciation, Natural Selection, and Networks: Three Historians Versus Theoretical Population Geneticists

“…This approach has advantages over the modeling of various potential equilibrium structures calculated from sequence ensembles by determining probabilities of individual bases being paired or unpaired (Zhang et al 2021). Technical disputes in this area are approaching resolution (Andrews et al 2022).…”

RNA–Small-Molecule Interaction: Challenging the “Undruggable” Tag

“…Computational tools like ScanFold can also locate functional regions within the genome. 268 Although RNA lags behind proteins in terms of already existing predicted structures or approved drugs, it is slowly making its way. Several available databases focus solely on this macromolecule, including RNAbase, RNAstrand, RNA structurome, RNA bricks, and RNA central.…”

“…The functionality of a segment can be verified by observing the effect of mutations or ASO-mediated blockage of that domain on the phenotype. Computational tools like ScanFold can also locate functional regions within the genome …”