Biology subjects

Fandrey, C. I.

Publications and source records attributed to Fandrey, C. I..

3 recordsLinked to original sources

bioRxiv2025

Development of AI-designed protein binders for detection and targeting of cancer cell surface proteins

Artificial intelligence (AI)-based protein design opens new avenues for the rapid generation of new research tools and therapeutics, but experimental validation lags behind the computational design throughput. Here, we present a scalable workflow for the discovery and validation of AI-designed protein binders. Leveraging the RFdiffusion protein design pipeline with a custom filter for stable alpha-helical bundle folds, we construct libraries of thousands of AI-binders against cancer-associated surface proteins. Mammalian cell-surface and phage display screening yield multiple high-affinity PD-L1 binders but fewer hits for CD276 (B7-H3) and VTCN1 (B7-H4), reflecting the target-dependent efficiency of RFdiffusion in generating high-quality designs. Using our experimentally validated AI-designed binder libraries, we benchmark freely available structure prediction models. We find that interface predicted template modelling (ipTM) scores by Chai-1 with ESM embedding correlate well with experimental success and even predict deleterious effects of binding interface mutations. To demonstrate the versatility of AI-binders as research tools, we deploy them in CAR-T cells and also assemble them with fluorophore-labeled streptavidin into tetravalent quattrobinders, which achieve antibody-comparable staining of endogenous PD-L1 by flow cytometry. With high production yields and accessible structural models, AI-designed quattrobinders are versatile and cost-effective research tools amenable to community-driven validation and optimization.

bioengineering↗

bioRxiv2025

Transposon-Display of AI-designed binders enables manipulation of the proteome in human cells

Transposon-Display is a highly scalable screening method that links proteins to their encoding DNA during expression in E. coli via a mutant transposase. Leveraging this system, we identified AI-designed binders targeting four intracellular human proteins, saturation-mutagenized top candidates, and charge-balanced variants to improve compatibility with the intracellular environment while preserving target binding. The resulting neutral binders can be fused to EGFP allowing antibody-free intracellular staining. Expressing neutral binders in human cells as fusions with functional domains can drive small molecule-controlled protein aggregation and trigger proteasomal degradation of endogenous target proteins in living cells. Manipulating the proteome of living cells using libraries of AI-designed binders may provide a new avenue to screen for disease-relevant protein functions and large-scale functional data may help to refine protein design algorithms.

bioengineering↗

bioRxiv2024

Cell Type-Agnostic Optical Perturbation Screening Using Nuclear In-Situ Sequencing (NIS-Seq)

Genome-scale perturbation screening is widely used to identify disease-relevant cellular proteins serving as potential drug targets. However, most biological processes are not compatible with commonly employed perturbation screening methods, which rely on FACS- or growth-based enrichment of cells. Optical pooled screening instead uses fluorescence microscopy to determine the phenotype in single cells, and subsequently to identify individual perturbagens in the same cells. Published methods rely on cytosolic detection of endogenously expressed barcoded transcripts, which limits application to large, transcriptionally active cell types, and often relies on local clusters of clonal cells for unequivocal barcode assignment, thus precluding genome-scale screening for many biological processes. Nuclear In-Situ Sequencing (NIS-Seq) solves these shortcomings by creating bright sequencing signals directly from nuclear genomic DNA, enabling screening any nucleus-containing cell type at high density and high library complexity. We benchmark NIS-Seq by performing three genome-scale optical screens in live cells, identifying key players of inflammation-related cellular pathways.

genomics↗