Sub-Picomolar Detection of SARS-CoV-2 RBD via Computationally-Optimized Peptide Beacons
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 c...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/142839 https://orcid.org/0000-0001-5778-4430 |
| _version_ | 1811095647005179904 |
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| author | Tripathy, Soumya Pratap |
| author2 | Jacobson, Joseph M. |
| author_facet | Jacobson, Joseph M. Tripathy, Soumya Pratap |
| author_sort | Tripathy, Soumya Pratap |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T16:22:40Z |
| format | Thesis |
| id | mit-1721.1/142839 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T16:22:40Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1428392022-06-01T03:02:17Z Sub-Picomolar Detection of SARS-CoV-2 RBD via Computationally-Optimized Peptide Beacons Tripathy, Soumya Pratap Jacobson, Joseph M. Program in Media Arts and Sciences (Massachusetts Institute of Technology) 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. S.M. 2022-05-31T13:31:58Z 2022-05-31T13:31:58Z 2021-09 2022-05-25T15:55:52.438Z Thesis https://hdl.handle.net/1721.1/142839 https://orcid.org/0000-0001-5778-4430 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Tripathy, Soumya Pratap Sub-Picomolar Detection of SARS-CoV-2 RBD via Computationally-Optimized Peptide Beacons |
| title | Sub-Picomolar Detection of SARS-CoV-2 RBD via Computationally-Optimized Peptide Beacons |
| title_full | Sub-Picomolar Detection of SARS-CoV-2 RBD via Computationally-Optimized Peptide Beacons |
| title_fullStr | Sub-Picomolar Detection of SARS-CoV-2 RBD via Computationally-Optimized Peptide Beacons |
| title_full_unstemmed | Sub-Picomolar Detection of SARS-CoV-2 RBD via Computationally-Optimized Peptide Beacons |
| title_short | Sub-Picomolar Detection of SARS-CoV-2 RBD via Computationally-Optimized Peptide Beacons |
| title_sort | sub picomolar detection of sars cov 2 rbd via computationally optimized peptide beacons |
| url | https://hdl.handle.net/1721.1/142839 https://orcid.org/0000-0001-5778-4430 |
| work_keys_str_mv | AT tripathysoumyapratap subpicomolardetectionofsarscov2rbdviacomputationallyoptimizedpeptidebeacons |