Perturb2Target
Built with Claude: Life SciencesGenome-scale screens and GWAS both tell you which genes matter in disease — but not what to do about them. A knockdown that reverses pathology is a drug lead; one that worsens it is a warning. Perturb2Target closes that gap by integrating evidence across biological scale, from population genetics to protein structure, to repurpose existing drugs for underserved autoimmune diseases like multiple sclerosis. We built an agentic AI pipeline that reasons over four layers. First, population data: we anchored candidates in GWAS through Open Targets credible-set fine-mapping and CD4⁺ T-cell cis-eQTLs, yielding 239 genetically-supported targets. Second, integrated single-cell atlases: we harmonized 1.9 million CD4⁺ T cells spanning ~20 inflammatory diseases into one batch-corrected manifold, and derived a contamination-filtered MS disease-direction from cross-atlas replication. Third, a CD4⁺ CRISPRi Perturb-seq screen scored, for every knockdown, whether its transcriptional effect reverses the MS direction — supplying the therapeutic sign that genetics alone cannot. Fourth, AlphaFold2 drug–protein interaction modelling assessed structural druggability of the leads. The pipeline is orchestrated by agentic AI, which chains these heterogeneous datasets into a single ranked, directional, genetics-anchored, druggable shortlist. Our headline finding: IL2RA emerges as a promising MS target — genetically supported (L2G 0.74), reversing the disease signature on knockdown, a validated immunotherapy node (the IL-2 receptor axis) and a cognate ligand. By unifying genetics, cell-atlas context, functional perturbation, and structure, Perturb2Target nominates not just which genes, but which way to push them — and whether they can be drugged.
