Sub-Picomolar Detection of SARS-CoV-2 RBD via Computationally-Optimized Peptide Beacons
Name
Tripathy-soumyatr-MS-MAS-2021-thesis.pdf
Description
Thesis PDF
Size
4.36 MB
Format
Adobe PDF
Checksum (MD5)
c6d20460303f8d12b1d199b1f159f157
Author(s)
Tripathy, Soumya Pratap
Advisor(s)
Jacobson, Joseph M.
Date Issued
September 2021
Publisher
Massachusetts Institute of Technology
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 a 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 (SRBD). 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.
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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