SBIR/STTR Research Award
In Silico-Designed Nanosensors for Biothreat Detection
Award amount
$999,999Program and phase
SBIR · Phase IIAward year
2017Award details
- Awarding agency
- Department of Defense
- Branch
- Army
- Recipient company
- PARABON NANOLABS, INC.
- Proposal award date
- 2017-02-15
- Contract end date
- 2019-02-14
- UEI
- MXWGP7E3TFM8
- Solicitation number
- 2013.3
- Topic code
- OSD13-HS3
Research abstract
In this subsequent SBIR Phase II project, Parabon NanoLabs will advance an in silico design (ISD) methodology and supporting design software for the development of DNA origami nanosensors able to detect biothreat agents.In the initial Phase II project, Parabon developed methods for (i) computing the binding interfaces of a target molecule, (ii) determining the optimal binding ligands for these locations, and (iii) designing origami to position these ligands appropriately.The approach was validated by designing and creating two novel nanosensors for detecting streptavidin (SA) and horseradish peroxidase (HRP), respectively.This subsequent Phase II effort will extend the ISD approach to include a larger and more varied set of binding ligands with the goal of increasing nanosensor potential binding affinity for arbitrary molecular targets.Enhanced ISD will be used to design a highly specific nanosensor for detection of an anthrax toxin surrogate, which will be demonstrated in a prototype lateral flow assay (LFA).
Technology keywords
Not reported
Other awards by this recipient
Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.
- A System for Engineering Nanocarriers Able to Transport Cargo Across the Blood-Brain Barrier
- Kin-X: A Software System for Rapid and Distant Kinship Inference and Automated Pedigree Reconstruction
- A nanoengineering platform for programmable gene editing therapies against rare diseases
- A System for Engineering Nanocarriers Able to Transport Cargo Across the Blood-Brain Barrier
- Novel whole-genome analysis methods for Alzheimer's risk prediction