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Software for sequencing human antibody proteins from polyclonal immune responses

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R44GM140607-01
Agency Tracking Number: R44GM140607
Amount: $1,551,492.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: 400
Solicitation Number: PA19-272
Solicitation Year: 2019
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-03-01
Award End Date (Contract End Date): 2023-02-28
Small Business Information
San Diego, CA 92109-5754
United States
DUNS: 031092123
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 (619) 752-4821
Business Contact
Phone: (888) 416-9305
Research Institution

The natural immune response to foreign pathogens involves a complex coordination of
cells, including an adaptive response to select and secrete antibodies into circulation.
Individuals who have recovered from a pathogenic infection retain immune memory and
continue to circulate pathogen-specific antibodies. For many infectious diseases like respiratory
syncytial virus, Ebola, and poxviruses, antibodies with neutralizing activity to multiple viral
strains have been discovered from human survivors. The discovered antibodies are highly
valuable as potential biologic therapeutics for the broader population, as the antibodies have
been naturally optimized to defend against human pathogens.Efforts to discover antibodies from humans recovering from coronavirus infection are
underway, SARS-CoV-2 in particular, but are hampered by the limitations of existing antibody
discovery platforms. Current approaches require screening for live B cells actively producing
pathogen-specific antibodies, which are sensitive to cell death and rarely found in blood. In
contrast, antibody protein is stable and pathogen-reactive antibodies are abundant in serum.
While protein is the ideal material to start with, characterization of polyclonal antibody (pAb)
protein presents new challenges.Recent advances in mass spectrometry and analysis have shown individual antibody
candidates can be derived from affinity-purified pAb proteins when a sufficiently matched B-cell
genetic antibody repertoire is provided. We aim to develop algorithms to supplant the need of a
genetic antibody repertoire, and de novo identify antibody candidates from limited complexity
pAb samples. This is achieved by improvements to de novo peptide sequencing using machine
learning, and targeted assembly of specificity determining regions (CDR3s) and antibody
frameworks using de Bruijn graphs. The proposed software will provide estimates of clonal
diversity and candidate sequences that can be synthesized and tested for reactivity. In addition
to addressing needs for infectious diseases, as demonstrated with an urgent unmet need to
stop the COVID-19 pandemic, the software also applies to clinical and biomedical research
needs in autoimmune disease, and commercial interests in replacing polyclonal antibody
reagents with highly reproducible monoclonal antibody equivalents.

* Information listed above is at the time of submission. *

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