AI Insight
Researchers developed VirEvo, a computational framework that uses artificial intelligence and genome foundation models to design compact, tissue-specific gene promoters for gene therapy applications. The system successfully compressed a human p16INK4a promoter by 85.9% (from 2,822 bp to 398 bp) while retaining 77% of its original selectivity for senescent cells and reducing unwanted background activity. The framework combines virtual dual-luciferase assays with evolutionary algorithms to optimize promoter sequences without requiring experimental retraining of the AI model.
Why it matters
This technology addresses a critical limitation in gene therapy, where viral vectors have limited space for genetic material. Compact promoters that maintain tissue specificity could enable more efficient and targeted gene therapies, potentially improving treatments for diseases requiring precise cellular targeting while minimizing off-target effects.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Compact tissue-specific promoters are highly desirable for gene therapy because viral vectors possess limited packaging capacity. However, existing promoter engineering strategies rely primarily on rational design or de novo sequence generation and lack efficient approaches for compressing long native promoters while preserving regulatory specificity. Although genome foundation models have substantially improved sequence-to-function prediction, they have not been effectively translated into computational platforms for promoter engineering.
Here, we present VirEvo, a computational promoter engineering framework that integrates a virtual dual-luciferase assay (VirDLA), genome-foundation-model-guided genetic evolution, and an orthogonal Pan-Tissue Consistency Filter (PTCF). VirDLA introduces an internal-reference normalization strategy inspired by dual-luciferase reporter assays, enabling relative comparison of promoter activity across tissues without retraining AlphaGenome. Guided by these normalized activity scores, VirEvo iteratively optimizes promoter selectivity, off-target activity, and sequence length.
Using the human p16INK4a promoter as a proof of concept, VirEvo evolved a compact synthetic promoter, SRP2M, of only 398 bp, representing an 85.9% reduction in sequence length. Experimental validation using dual-luciferase reporter assays in senescent IMR90 fibroblasts demonstrated that SRP2M retained 77% of wild-type senescence selectivity while reducing basal leakage to 52% of the wild-type level.
Together, these results demonstrate the feasibility of genome-foundation-model-guided promoter engineering. VirEvo provides a generalizable framework for designing compact tissue-specific regulatory elements and extends the application of genome foundation models from functional prediction to synthetic regulatory engineering.
Source: Directed evolution of compact synthetic promoters via AlphaGenome and genetic algorithms