Jun 2026· Journal of Genome Biotechnology and Genetics· 0 citations· 135 references
TL;DR
The convergence of standardised experimental data, specialised microbial datasets, and advanced AI architectures is paving the way to transform microbial biotechnology by accelerating metabolic engineering and synthetic biology applications.
Abstract
CRISPR-Cas9 has transformed microbial biotechnology by enabling precise genome modifications; however, achieving high editing efficiency remains a challenge due to multiple determinants, including on-target specificity, off-target events, PAM sequence, sgRNA scaffold composition, and RNA secondary structure. Our review foresees how artificial intelligence (AI) can address those challenges by enabling automated identification as well as highly active guide RNA (gRNA) optimisation. We highlight the influence of a data-driven training strategy that is focused on high-quality, diverse, and accurately labelled microbial datasets—mainly, given the limitations of models derived from mammalian systems that are not directly transferable to microbial organisms. Moreover, we discuss the key role of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and centralised, curated CRISPR-Cas databases as foundational elements for developing robust and predictive frameworks. Emerging directions are also explored, including generative AI approaches capable of supporting automated experimental planning. By considering the potential dual use of such technologies, the review further addresses bioethical considerations and regulatory frameworks necessary to ensure responsible genome engineering as a milestone, as well as the implementation of safeguards against misuse, particularly in pathogenic microorganisms. Furthermore, the convergence of standardised experimental data, specialised microbial datasets, and advanced AI architectures is paving the way to transform microbial biotechnology by accelerating metabolic engineering and synthetic biology applications.
Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins constitute adaptive immune systems in prokaryotes and have transformed life sciences, precision medicine, and synthetic biology as programmable genome-editing tools. Despite their broad utility, naturally occurring DNA-targeting Cas effectors remain constrained by several intrinsic limitations, including large protein size that complicates delivery, stringent protospacer adjacent motif (PAM) requirements that restrict targetable genomic space, and mismatch tolerance that can lead to off-target activity and potential genotoxicity. These challenges have made Cas protein engineering and the discovery of novel CRISPR and CRISPR-like systems from metagenomic resources central to the development of next-generation genome-editing platforms. This Review places recent advances within an integrated synthetic biology engineering continuum that links natural effector discovery, structure-guided hypothesis generation, high-throughput functional screening, machine learning-enabled model construction, and iterative redesign. This Review summarizes progress in the screening, optimization, and functional engineering of DNA-targeting CRISPR and CRISPR-like effectors, with emphasis on structure-guided rational design, directed evolution coupled with high-throughput screening, bioinformatics- and evolution-guided mining of novel systems from large-scale sequence databases, and artificial intelligence-assisted development. By integrating these strategies, we highlight how CRISPR effector engineering is moving toward design-build-test-learn (DBTL)-inspired workflows that expand the functional landscape of genome-editing technologies and advance genome editing toward improved efficiency, safety, and programmability.
Lingwei She, Zeyu Liang, Qin Zou et al.· ACS Synthetic Biology· 1 citation
The engineering of sgRNAs offers a powerful means to systematically enhance the stability, specificity, and reliability of CRISPR-based biosensors, thereby accelerating their practical deployment in clinical diagnostics.
Xiaojing Liu, Tingyi Chen, Xiangjun Li et al.· ACS Sensors· 0 citations
This article synthesizes contemporary advancements in CRISPR-mediated mammalian genome modification, detailing core mechanisms – such as guide RNA and the Cas9 endonuclease – alongside next-generation modalities, including base and prime editing.
Olga Aldoshina, Dmitriy Lazarev, E. Smirnova· Veterinariya, Zootekhniya i...· 0 citations
It is argued that AI and CRISPR are complementary components of an emerging design-build-test-learn framework rather than a mature autonomous breeding platform, and progress will depend on plant-specific benchmark datasets, prospective validation, multi-environment field trials, interoperable data standards, equitable access to transformation and computational infrastructure, and governance focused on the properties and evidence of resulting products.
Anilkumar Lalasing Chavan, Pavan Rathod G. P., Chandana Suresh K. S. et al.· Plant cell biotechnology and...· 0 citations
The current developments in engineering of Cas effector protein, optimizing guide RNA designs, developing advanced reporter probes, and utilizing chemical additives are summarized and their significant contribution to increasing the fidelity of editing and diagnostic sensitivity is highlighted.
Sheng Li, Han Li, Bojie Chen et al.· Interdisciplinary Medicine· 0 citations
Major bioinformatics tools for CRISPR are reviewed, identifying the layered separation as a key bottleneck and highlighting the demand for a unified programming framework to streamline workflows and advance CRISPR algorithm innovation.