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Computational algorithm to predict interacting MHC alleles from TCR sequences

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R43GM143955-01A1
Agency Tracking Number: R43GM143955
Amount: $256,581.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: 400
Solicitation Number: PA20-260
Timeline
Solicitation Year: 2020
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-02-10
Award End Date (Contract End Date): 2023-02-09
Small Business Information
1546 SAN ANTONIO AVE
Menlo Park, CA 94025-3129
United States
DUNS: 117591376
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 BINBIN CHEN
 (706) 594-5091
 binbineow@gmail.com
Business Contact
 BINBIN CHEN
Phone: (706) 594-5091
Email: binbin@vcreate.io
Research Institution
N/A
Abstract

Abstract
Major histocompatibility complexes (MHC) guide immune response by presenting antigen
fragments on a cell’s surface and interacting with T-cell receptors (TCRs). In recent years, many
T-cell therapies have successfully engineered T-cells to target MHC-antigen complexes
associated with cancers and other diseases. However, most T-cell therapies require identifying
a TCR that interacts with an MHC-antigen complex of interest, a slow and expensive search
process. Our proposal aims to speed up this search process through a computational algorithm
that will predict whether a TCR will interact with an MHC allele of interest. Current screening
assays for low frequency TCRs have high false positive rates. Researchers can use our tool to
computationally filter TCR candidates for interaction with a specific MHC allele before running
expensive validation experiments. In this proposal, we will first validate our approach through a
prototype algorithm that we will train on public TCR-MHC interaction data. We will then conduct
new tetramer staining experiments that address two major challenges for developing an
algorithm across multiple MHC alleles: the lack of interaction data for alleles other than A*02,
and the limited antigen diversity in existing public data. These experiments will provide
TCR-MHC data across 800 antigens for four common MHC alleles: A*01:01, A*02:01, A*11:01,
and B*07:02. Finally, we will construct and validate computational algorithms for each MHC
allele and evaluate the importance of various TCR components (e.g., alpha or beta chan,
CDR3) in predicting TCR-MHC interaction. Our work will result in the first computational tool to
help T-cell therapy developers filter TCR candidates based on MHC specificity. Beyond cell
therapies, this tool will also help researchers track T-cells in diseases where MHC alleles play a
major role.

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

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