In silico thermodynamic evaluation of the effectiveness of RT-LAMP primers to SARS-CoV-2 variants detection

Abstract

Viral mutations are the primary cause of mismatches in primer-target hybridisation, affecting the sensibility of molecular techniques, potentially leading to detection dropouts. Despite its importance, little is known about the quantitative effect of mismatches in primer-target hybridisation. We use up-to-date and highly detailed thermodynamic model parameters of DNA mismatches to evaluate the sensibility to variants of SARS-CoV-2 RT-LAMP primers. We aligned 18 RT-LAMP primer sets, which were underwent clinical validation, to the genomes of Wuhan strain (ws), 7 variants and 4 subvariants, and calculated hybridisation temperatures allowing up to three consecutive mismatches. We calculate the coverage when the mismatched melting temperature falls by more than 5{degrees}C in comparison to the matched alignments. If no mismatches are considered, the average coverage found would be 94% for ws, falling the lowest value for Omicron: 84%. However, considering mismatches the coverage is much higher: 97% (ws) to 88% (Omicron). Stabilizing mismatches (higher melting temperatures), account for roughly 1/3 of this increase. The number of primer dropouts increases for new each variant, however the effect is much less severe if mismatches are considered. We suggest using melting temperature calculations to continuously assess the trend of primer dropouts.