Excel · study · 2026
AI-enabled discovery and biochemical optimization of minibinders targeting cancer cell-surface proteins
Listed in NCBI GEO
AI-based protein design can rapidly generate novel protein binders, but experimental validation and functional optimization remain bottlenecks.
Description
We present a scalable workflow to develop of AI-designed minibinders against cancer-associated surface proteins. Screening thousands of designs using mammalian cell-surface display identifies several high-affinity PD-L1 minibinders but far fewer for CD276 (B7-H3) and VTCN1 (B7-H4), highlighting substantial target dependence.
Interface predicted template modeling (ipTM) scores generated by Chai-1 with ESM embeddings correlate with binding success and capture deleterious effects of interface mutations. Fluorophore-labeled AI-minibinders enable flow-cytometric staining comparable to conventional antibodies. However, as chimeric antigen receptors (CARs), some show poor cell-surface trafficking and limited CAR-T cell functionality.
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Redesign through a genetic algorithm-based diversification strategy that preserves the binding interface while changing non-binding surfaces reveals experimentally a defined pI window that improves CAR expression and enhances target-selective tumor cell killing while reducing off-target cytotoxicity. Our findings establish biochemical optimization beyond the binding interface as critical requirement for translating AI-minibinders into functional applications.
Links
Get the data
- GEO FTP directory ftp.ncbi.nlm.nih.gov/geo/series/GSE338nnn/GSE338543 ↗
download · from NCBI GEO
Where it is published
- GEO accession page ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE338543 ↗
landing page · from NCBI GEO
Documentation and papers
- PRJNA1494948 ncbi.nlm.nih.gov/bioproject/PRJNA1494948 ↗
project · from NCBI GEO
Topics
- Stated by source
- Other
- From keywords
- Life Sciences
- Inferred from text
- Cancer 75%
Provenance · 1 source records, 7 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| NCBI GEO | GSE338543 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · NCBI GEO | connector:ncbi_geo@1.0.0 | |
| concepts[disease].local:disease:cancer | enrichment · NCBI GEO | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[field].local:field:life-sciences | mapping · NCBI GEO | connector:ncbi_geo@1.0.0 | |
| concepts[method].geo_series_type:other | source · NCBI GEO | connector:ncbi_geo@1.0.0 | /gdstype |
| description | source · NCBI GEO | connector:ncbi_geo@1.0.0 | /summary |
| publication_date | source · NCBI GEO | connector:ncbi_geo@1.0.0 | |
| title | source · NCBI GEO | connector:ncbi_geo@1.0.0 | /title |