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Cited by 0Monoclonal and Polyclonal Antibodies Researchvaccines and immunoinformatics approachesProtein Degradation and Inhibitors

The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.

Computational Design of a TCR-Based Bispecific Engager Targeting Cancerous KRAS G12V Mutations

Open access
Shah, Nisarg, Sharma, Gargi, Patel, Vihaan, Wu, Melinda, Mummadi, Anishreddy, Taneja, Anya, Shah, Moksh, Le, Gia Bao, Krishna, Madhav, Brah, Harman·2026·ACS Omega
DOI 10.1021/acsomega.5c10229PMC PMC13019212

Published version of a preprint

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Abstract

Bispecific T-cell engager (BiTE) therapy is a type of immunotherapy that redirects cytotoxic T cells to target tumor cells. Traditionally, a BiTE structure dual binds to the CD3 receptor on the T-cell surface and to the mutated antigen presented on the surface of the cancer cell as an extracellular protein. An example of this is blinatumomab, an FDA-approved BiTE structure for the treatment of B-cell acute lymphoblastic leukemia (B-ALL). However, traditional BiTE complexes are at risk of off-tumor effects due to their targeting of cell surface antigens, which are not specific to certain cancer variants. In this study, we utilized a computational approach, an area that remains underdeveloped in current immunotherapy design, to design the initial structures of two TCR-BiTEs that consist of a single-chain variable fragment (scFv) in conjunction with a T-cell receptor (TCR) that binds specifically to the KRAS G12V neoantigen, which currently does not have any FDA-approved targeted therapy treatments. Ultimately, we present two promising TCR-BiTEs that are specific to two different HLAs (HLA:03:01 and HLA:11:01) targeting KRAS G12V, serving as valuable starting points for further evaluation and design in vitro. We validated the design of our TCR-BiTE structures through AlphaFold tools, free energy estimation methods, and molecular dynamics analysis, thereby also providing a potential computational pipeline that can be applied in the design of TCR-BiTE structures targeting other mutations in addition to KRAS G12V.

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Crossref · 10.1021/acsomega.5c10229

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Figures

1 Diagram of the structure for traditional BiTES vs that of our proposed TCR-BiTE structure. For each depiction, the TCR-BiTE is depicted on the left, and the TCR-BiTE simulated during a sample immune response is depicted on the right.
1 Diagram of the structure for traditional BiTES vs that of our proposed TCR-BiTE structure. For each depiction, the TCR-BiTE is depicted on the left, and the TCR-BiTE simulated during a sample immune response is depicted on the right.CC BY-NC-ND · PubMed Central
2 Residue-level confidence traces (pLDDT scores) for the five TCR-BiTE constructs, including (a) 8i5c, (b) 8i5d, (c) 8rro, (d) 8wte, and (e) 8wul predicted by AlphaFold2. Each plot overlays the pLDDT scores for the top five ranked structural models per construct. The predicted Local Distance Difference Test (pLDDT) score is a per-residue metric ranging from 0 to 100, estimating AlphaFold’s confidence in the atomic position of each amino acid. Scores above 90 indicate highly reliable predictions (typically structured domains such as TCR or scFv regions), while lower scores (<70) reflect flexible or uncertain regions, often corresponding to synthetic linker regions or interdomain junctions.
2 Residue-level confidence traces (pLDDT scores) for the five TCR-BiTE constructs, including (a) 8i5c, (b) 8i5d, (c) 8rro, (d) 8wte, and (e) 8wul predicted by AlphaFold2. Each plot overlays the pLDDT scores for the top five ranked structural models per construct. The predicted Local Distance Difference Test (pLDDT) score is a per-residue metric ranging from 0 to 100, estimating AlphaFold’s confidence in the atomic position of each amino acid. Scores above 90 indicate highly reliable predictions (typically structured domains such as TCR or scFv regions), while lower scores (<70) reflect flexible or uncertain regions, often corresponding to synthetic linker regions or interdomain junctions.CC BY-NC-ND · PubMed Central
3 Predicted alignment error (PAE) matrices for the five TCR-BITEs, (a) 8i5c, (b) 8i5d, (c) 8rro, (d) 8wte, and (e) 8wul, as calculated by AlphaFold2. Each heatmap visualizes the predicted pairwise positional error between residues (in Ångströms), with blue indicating high-confidence relationships and red indicating uncertainty. Diagonal blocks represent well-folded intradomain regions, while off-diagonal regions reflect domain–domain confidence. Modular designs like TCR-BiTEs often exhibit strong intradomain structure (blue blocks) and weaker interdomain anchoring (red zones), particularly at flexible linker regions.
3 Predicted alignment error (PAE) matrices for the five TCR-BITEs, (a) 8i5c, (b) 8i5d, (c) 8rro, (d) 8wte, and (e) 8wul, as calculated by AlphaFold2. Each heatmap visualizes the predicted pairwise positional error between residues (in Ångströms), with blue indicating high-confidence relationships and red indicating uncertainty. Diagonal blocks represent well-folded intradomain regions, while off-diagonal regions reflect domain–domain confidence. Modular designs like TCR-BiTEs often exhibit strong intradomain structure (blue blocks) and weaker interdomain anchoring (red zones), particularly at flexible linker regions.CC BY-NC-ND · PubMed Central
4 Multiple sequence alignment (MSA) coverage plots for (a) 8i5c, (b) 8i5d, (c) 8rro, (d) 8wte, and (e) 8wul. The red lines represent homologous sequences aligned to the query, while the black trace indicates the per-position alignment depth. High coverage in scFv and TCR domains suggests confident evolutionary constraints, whereas sparse coverage near linker junctions and novel fusions reflects limited homologous supportpotentially impacting AlphaFold2’s confidence and structural accuracy in those regions.
4 Multiple sequence alignment (MSA) coverage plots for (a) 8i5c, (b) 8i5d, (c) 8rro, (d) 8wte, and (e) 8wul. The red lines represent homologous sequences aligned to the query, while the black trace indicates the per-position alignment depth. High coverage in scFv and TCR domains suggests confident evolutionary constraints, whereas sparse coverage near linker junctions and novel fusions reflects limited homologous supportpotentially impacting AlphaFold2’s confidence and structural accuracy in those regions.CC BY-NC-ND · PubMed Central
5 AlphaFold-predicted tertiary structures for the five TCR-BiTE constructs, including (a) 8i5c, (b) 8i5d, (c) 8rro, (d) 8wte, and (e) 8wul. Color-coding highlights the modular components: anti-CD3 scFv (blue), TCR chains (green), and flexible GGGGS linkers (red). These structures validate the modular assembly of each construct.
5 AlphaFold-predicted tertiary structures for the five TCR-BiTE constructs, including (a) 8i5c, (b) 8i5d, (c) 8rro, (d) 8wte, and (e) 8wul. Color-coding highlights the modular components: anti-CD3 scFv (blue), TCR chains (green), and flexible GGGGS linkers (red). These structures validate the modular assembly of each construct.CC BY-NC-ND · PubMed Central
6 RMSD value of structure backbone through 100 ns viewed with XMGrace. RMSD values fluctuate from 0 to 1 nm throughout the simulation, indicating significant but not erratic structural change.
6 RMSD value of structure backbone through 100 ns viewed with XMGrace. RMSD values fluctuate from 0 to 1 nm throughout the simulation, indicating significant but not erratic structural change.CC BY-NC-ND · PubMed Central
7 Radius of gyration of the structure backbone viewed with XMGrace.
7 Radius of gyration of the structure backbone viewed with XMGrace.CC BY-NC-ND · PubMed Central
8 Radius of the gyration of each of the three docked structures. (a) CD3 Rg, (b) 8wte TCR-BiTE Rg, and (c) HLA-bound neoantigen Rg.
8 Radius of the gyration of each of the three docked structures. (a) CD3 Rg, (b) 8wte TCR-BiTE Rg, and (c) HLA-bound neoantigen Rg.CC BY-NC-ND · PubMed Central
9 Solvent-accessible surface area of the CDR loops of the anti-CD3 and 8wte TCR sections of the BiTE. (a) SASA of the VH chain of anti-CD3, in both the docked and undocked forms. (b) SASA of the VL chain. (c) SASA of the 8wte α chain. (d) SASA of the 8wte beta chain.
9 Solvent-accessible surface area of the CDR loops of the anti-CD3 and 8wte TCR sections of the BiTE. (a) SASA of the VH chain of anti-CD3, in both the docked and undocked forms. (b) SASA of the VL chain. (c) SASA of the 8wte α chain. (d) SASA of the 8wte beta chain.CC BY-NC-ND · PubMed Central
10 Estimated CD8+ T-cell population across the four MHC-1 complexes. For each condition, the left panel depicts the total TC (cytotoxic T-cell) population and memory TC fraction over time, while the right panel shows the TC population at four different stages (Active – purple, Duplicating – green, resting – blue, and anergic (y2) – orange). (a) MHC-1 peptide complex with KRAS G12V and HLA:A*03:01. (b) MHC-1 peptide complex with KRAS WT and HLA:A*03:01. (c) MHC-1 peptide complex with KRAS WT and HLA:A*11:01. (d) MHC-1 peptide complex with KRAS G12V and HLA:A*11:01.
10 Estimated CD8+ T-cell population across the four MHC-1 complexes. For each condition, the left panel depicts the total TC (cytotoxic T-cell) population and memory TC fraction over time, while the right panel shows the TC population at four different stages (Active – purple, Duplicating – green, resting – blue, and anergic (y2) – orange). (a) MHC-1 peptide complex with KRAS G12V and HLA:A*03:01. (b) MHC-1 peptide complex with KRAS WT and HLA:A*03:01. (c) MHC-1 peptide complex with KRAS WT and HLA:A*11:01. (d) MHC-1 peptide complex with KRAS G12V and HLA:A*11:01.CC BY-NC-ND · PubMed Central
11 Estimated concentrations for cytokines and interleukins. The main panel depicts the change in concentrations of multiple cytokines and interleukins over time. The inner plot modeled the danger signal together with the leukocyte growth factor IL-2 (orange). (a) MHC-1 peptide complex with KRAS G12V and HLA:A*03:01. (b) MHC-1 peptide complex with KRAS WT and HLA:A*03:01. (c) MHC-1 peptide complex with KRAS WT and HLA:A*11:01. (d) MHC-1 peptide complex with KRAS G12V and HLA:A*11:01.
11 Estimated concentrations for cytokines and interleukins. The main panel depicts the change in concentrations of multiple cytokines and interleukins over time. The inner plot modeled the danger signal together with the leukocyte growth factor IL-2 (orange). (a) MHC-1 peptide complex with KRAS G12V and HLA:A*03:01. (b) MHC-1 peptide complex with KRAS WT and HLA:A*03:01. (c) MHC-1 peptide complex with KRAS WT and HLA:A*11:01. (d) MHC-1 peptide complex with KRAS G12V and HLA:A*11:01.CC BY-NC-ND · PubMed Central

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Citations

Cites

Research works printed but not linked: 6

  1. Baeuerle P. A., 2009, Curr. Opin. Mol. Ther., 11, 22
  2. 10.1038/s41592-022-01488-1 · DOI · Paper page
  3. 10.3389/fimmu.2019.02064 · DOI
  4. 10.1093/bioinformatics/btv552 · DOI
  5. Zhou J., 2017, Cell. Mol. Immunol., 14, 826
  6. Paggi J. M., 2023, Nucleic Acids Res., 53, W306

Metadata

Authors
Shah, N., Sharma, G., Patel, V., Wu, M., Mummadi, A., Taneja, A., Shah, M., Le, G. B., Krishna, M., & Brah, H.
Affiliations
Aspiring Scholars Directed Research Program (US)
Corresponding author
Brah, Harman
ORCID
Venue
ACS Omega
Publisher
American Chemical Society (ACS)
Published
2026-03-09
Volume
11
Issue
11
Pages
17366-17380
ISSN
2470-1343 (ISSN-L)
DOI
10.1021/acsomega.5c10229
PMC
PMC13019212
OpenAlex
W7134948581
Cited by
0
References
42
FWCI
0.00
Type
journal article
Language
en
Open access
Licence
Locations
  • ACS Omega (publishedVersion, cc-by-nc-nd)
  • ChemRxiv (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
Topics
  • Life Sciences › Immunology and Microbiology › Immunology › Monoclonal and Polyclonal Antibodies Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
Keywords
bispecific T-cell engagers, KRAS G12V mutation, neoantigens, T cell receptor engineering, scFv, HLA class I binding, cancer immunotherapy, computational protein design, on-target off-tumor toxicity, AlphaFold, free energy calculation, molecular dynamics

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