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Technology Overview
OHSU # 3108 — De novo peptide sequencer
Summary
De novo protein-sequencing approaches are poised to revolutionize nearly all fields of biological research and medicine; however current approaches are low-throughput and limited in their sensitivity. The current technology is a novel method that could be implemented with existing reagents and equipment to improve de novo peptide sequencing capabilities.
Technology Overview
Currently, untargeted proteomics primarily relies on digesting intact proteins into small peptides and reading their sequence using liquid chromatography-mass spectroscopy (LC-MS). However, LC-MS and other current protein sequencing approaches are unable to completely sequence proteins de novo, suffer from low throughput, and do not have the sensitivity to interpret post-translational modifications with high confidence. The current technology is a novel de novo peptide sequencing method, which can be implemented with readily available reagents and equipment. Features of this peptide sequencing method include:
- Fluorescence lifetime imaging microscopy (FLIM) based approach.
- Utilization of standard fluorophores for all the measurements, increasing compatibility with existing reagents and equipment.
- No requirement for amino acid-specific binders, which can be difficult to design and validate, thereby overcoming a critical limitation in competing peptide sequencing approaches.
- Flexibility in workflow to potentially allow for:
- Multiple read-outs per amino acid, which enables high confidence, machine learning-based sequence predictions.
- Ability to remove one amino acid at a time via common techniques such as Edman degradation to sequence the peptide.
- Use of different imaging oligonucleotide and fluorescent dye designs to increase identification of post-translational modifications.
Licensing Opportunity
This technology is available for licensing.
Inventors:
- Fehmi Civitci
- Eugene Tu
- Kevin Schilling
- Mingchong Dai
- Nicholas Groves
- Justin Plaut
- Srivathsan Ranganathan
- Andy Mendoza
- Riccardo Carloni
- Yu-Jui (Roger) Chiu
- Emine (Sila) Ozdemir
- Bruce Branchaud
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