In silico prediction of COVID-19 test efficiency with DinoKnot

Abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus spreading across the world causing the disease COVID-19. COVID-19 is diagnosed by quantitative reverse-transcription polymer chain reaction (qRT-PCR) testing which utilizes different primer-probe sets depending on the assay used. Using in silico analysis we aimed to determine how the secondary structure of the SARS-CoV-2 RNA genome affects the interaction between the reverse primer during qRT-PCR and how it relates to the experimental primer-probe test efficiencies. We introduce the program DinoKnot (Duplex Interaction of Nucleic acids with pseudoKnots) that follows the hierarchical folding hypothesis to predict the secondary structure of two interacting nucleic acid strands (DNA/RNA). DinoKnot is the first program that utilizes stable stems in both strands as a guide to predict their interaction structure. Using DinoKnot we predicted the interaction of the reverse primers used in four common COVID-19 qRT-PCR tests with the SARS-CoV-2 RNA genome. In addition, we predicted how 12 mutations in the primer/probe binding region may affect the primer/probe ability and subsequent SARS-CoV-2 detection. We identified three mutations that may prevent primer binding, reducing the ability for SARS-CoV-2 detection. Furthermore, we investigated the effect of mutations in two variants of concern (UK and South Africa) on the efficacy of the existing primer-probe sets. Despite mutations, we did not detect deviation in primer binding when compared to the reference target strand. We believe our contributions can aid in the design of more sensitive SARS-CoV-2 diagnosis tests.