Back

PhenoDEL as a Novel Screening Strategy Based on Intracellular Protein Degradation Activity

Onda, Y.; Ochi, Y.; Araki, T.; Kageoka, M.; Takeda, S.; Yamada, K.; Ueda, T.; Ohno, K.; Tanaka, M.; Sakai, D.; Hasegawa, M.; Tanaka, Y.

2025-11-27 synthetic biology
10.1101/2025.11.26.690606 bioRxiv
Show abstract

Targeted protein degradation (TPD), including proteolysis targeting chimeras (PROTACs) and molecular glue degraders (MGDs), is a promising therapeutic approach. However, systematic discovery of such small molecules remains a major challenge. Here, we present PhenoDEL, a novel phenotypic DNA-encoded library (DEL) screening platform that integrates one-bead one-compound DEL (OBOC-DEL) with the Beacon(R) optofluidic system for high-throughput, single-cell analysis. By co-culturing individual OBOC-DEL beads and engineered reporter cells in nanoliter-scale chambers, PhenoDEL enables direct observation of compound-induced protein degradation at single-cell resolution. We demonstrate this approach by identifying compounds that induce degradation of FKBP12F36V-EGFP fusion proteins in PC-3 cells. The workflow allows precise linkage between compound identity and cellular phenotype via DNA barcoding and next-generation sequencing. PhenoDEL overcomes limitations of conventional screening methods, offering high sensitivity, spatial control, and scalability. This platform holds significant potential for mechanism-driven drug discovery, including identification of novel PROTACs and MGDs. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/690606v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@fe27bforg.highwire.dtl.DTLVardef@1e38ef5org.highwire.dtl.DTLVardef@be958aorg.highwire.dtl.DTLVardef@4bc48a_HPS_FORMAT_FIGEXP M_FIG C_FIG

Published in ACS Chemical Biology (predicted rank #12) · training set

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.