Sub-Picomolar Detection of SARS-CoV-2 RBD via Computationally-Optimized Peptide Beacons

Abstract

The novel coronavirus SARS-CoV-2 continues to pose a significant global health threat. Along with vaccines and targeted therapeutics, there is a critical need for rapid diagnostic solutions. In this work, we employ deep learning-based protein design to engineer molecular beacons that function as conformational switches for high sensitivity detection of the SARS-CoV-2 spike protein receptor binding domain (S-RBD). The beacons contain two peptides, together forming a heterodimer, and a binding ligand between them to detect the presence of S-RBD. In the absence of S-RBD (OFF), the peptide beacons adopt a closed conformation that opens when bound to the S-RBD and produces a fluorescence signal (ON), utilizing a fluorophore-quencher pair at the two ends of the heterodimer stems. Two candidate beacons, C17LC21 and C21LC21, can detect the S-RBD with limits of detection (LoD) in the sub-picomolar range. We envision that these beacons can be easily integrated with on-chip optical sensors to construct a point-of-care diagnostic platform for SARS-CoV-2.

In Silico Protein Folding Prediction of COVID-19 Mutations and Variants

“…This specific approach of using trRosetta algorithm was intriguing to us because it was an available web-based platform and presented a scalable, efficient method for future researchers to conduct preliminary analysis into new mutations and variants that may not yet exist under natural conditions. Additionally, several studies have been reported on protein/peptide prediction of/against novel coronavirus SARS-COV-2 using trRosetta modeling suite [ 23 , 46 , 47 , 48 ]. Instead of time-consuming laboratory work to alter the genetic sequences of pathogens, large-scale computer generation of proteins could provide an avenue to narrow down mutations to study, discern patterns that can only be seen with large quantities of data and identify new therapeutics.…”