AI-driven Design and Discovery of Next-generation Genome Editors for Ultra-high Specificity and Efficiency
محورهای موضوعی : Biotechnological Journal of Environmental Microorganisms
Abdul Razak Mohamed Sikkander
1
,
Hala S. Abuelmakarem
2
,
Joel J. P. C. Rodrigues
3
1 - Department of Chemistry, Velammal Engineering College, Chennai -600066 Tamilnadu INDIA
2 - Department of Biomedical Engineering, College of Engineering, King Faisal University, Al-Ahsa, 31982, Saudi Arabia
3 - National Institute of Telecommunications (Inatel), Santa Rita do Sapuca´ı, MG, Brazil; Instituto de Telecomunica¸c˜oes, Portugal; Federal University of Piau´ı (UFPI), Teresina, PI, Brazil
کلید واژه: Artificial intelligence, Genome editing, CRISPR Cas9, Guide RNA optimization, Off target prediction, Deep learning,
چکیده مقاله :
Genome‑editing technologies such as CRISPR‑Cas9 have transformed biology and medicine by enabling precise modifications of DNA sequences. Yet key challenges remain: accurate guide‑RNA (gRNA) design, minimizing off‑target effects, optimizing editing efficiency, and tailoring editors to specific cell types or organisms. Artificial intelligence (AI) offers powerful methods—machine learning, deep learning, ensemble models—to learn from large volumes of genomic, epigenomic and experimental editing‑outcome data and thereby design and optimize genome‑editing reagents. This paper reviews the development of AI‑driven genome‑edit tools, presents a hypothetical evaluation framework, and reports results from simulated datasets comparing baseline heuristics versus AI‑augmented design. In our simulation, AI models improved predicted on‑target efficiency by ~25 % and reduced predicted off‑target risk by ~30 % relative to standard rule‑based design. Tabulated results illustrate improvements in gRNA ranking, editor variant selection, and delivery‑vector prediction. We discuss methodological steps: feature engineering (sequence context, chromatin accessibility, cleavage kinetics), model architecture (CNNs, transformer models, ensemble learning), training/validation workflows and deployment considerations (interpretability, regulatory constraints, dataset bias). Limitations include biased training data, cell‑type specificity, delivery challenges, and ethical oversight. Future perspectives emphasize foundation models for editing‑protein design, active‑learning from screening experiments, personalized editing prescriptions, and AI‑augmented clinical pipelines. In conclusion, AI‑powered design and optimisation of genome‑editing tools is poised to accelerate therapeutic, agricultural and synthetic‑biology applications—provided that robust datasets, interpretability and ethical frameworks are in place.
Genome‑editing technologies such as CRISPR‑Cas9 have transformed biology and medicine by enabling precise modifications of DNA sequences. Yet key challenges remain: accurate guide‑RNA (gRNA) design, minimizing off‑target effects, optimizing editing efficiency, and tailoring editors to specific cell types or organisms. Artificial intelligence (AI) offers powerful methods—machine learning, deep learning, ensemble models—to learn from large volumes of genomic, epigenomic and experimental editing‑outcome data and thereby design and optimize genome‑editing reagents. This paper reviews the development of AI‑driven genome‑edit tools, presents a hypothetical evaluation framework, and reports results from simulated datasets comparing baseline heuristics versus AI‑augmented design. In our simulation, AI models improved predicted on‑target efficiency by ~25 % and reduced predicted off‑target risk by ~30 % relative to standard rule‑based design. Tabulated results illustrate improvements in gRNA ranking, editor variant selection, and delivery‑vector prediction. We discuss methodological steps: feature engineering (sequence context, chromatin accessibility, cleavage kinetics), model architecture (CNNs, transformer models, ensemble learning), training/validation workflows and deployment considerations (interpretability, regulatory constraints, dataset bias). Limitations include biased training data, cell‑type specificity, delivery challenges, and ethical oversight. Future perspectives emphasize foundation models for editing‑protein design, active‑learning from screening experiments, personalized editing prescriptions, and AI‑augmented clinical pipelines. In conclusion, AI‑powered design and optimisation of genome‑editing tools is poised to accelerate therapeutic, agricultural and synthetic‑biology applications—provided that robust datasets, interpretability and ethical frameworks are in place.
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