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| Attention, Transformers, and LLMs: a hands-on introduction in Pytorch | Learning | aideep-learningmachine-learning +2 more tags | Intermediate |
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Engagements
Re-engineering Lilly’s KisunlaTM into a novel antibody targeting IL13RA2 against GBM using AI-driven macromolecular modeling
Atrium Health Levine Cancer
- Summary and objectives of the proposed experiments:
- An initial research-based Ab (scFv47, discovered by our collaborator Dr. Balyasnikova) model, modeling Ab-Ag (IL13RA2 against GBM) protein complex, and identifying the binding sites (epitopes) using ROSETTA and AlphaFold2 multimer tools.
- Graft the CDRs of scFv (single-chain variable fragment) of antibody or Bispecific T cell engagers (BTEs) onto the template Ab, the framework of Lilly's Kisunla™ Ab drug.
- Modify, improve, and optimize the overall or full antibody protein structures using AI-driven macromolecule modeling (AlphaFold3).
- Explore single nucleotide polymorphism (SNP), pathogenic genetic variants and N-glycosylation of IL13RA2 (target) protein domain interacting with the Ab candidates among the patient population using ROSETTA software packages.
Status: In Progress
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Stanley Nwoji
Harrisburg University of Science and Technology
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