CRISPR technology, which is derived from the bacterial adaptive immune system, has transformed traditional genetic engineering techniques, made strain engineering significantly easier, and become a very versatile genome editing system that allows for precise, programmable modifications to a wide range of microbial genomes. The economies of fermentation-based manufacturing are changing because of its quick acceptance in both academic and industry labs. CRISPR processes have been used to modify industrially significant bacteria, including the lactic acid producers, Clostridium spp., Escherichia coli, and Corynebacterium glutamicum, in order to increase the yields of bioethanol, butanol, succinic acid, acetone, and polyhydroxyalkanoate precursors. CRISPR-mediated promoter engineering and single-step multiplex editing have improved inhibitor tolerance, raised ethanol titers, and allowed for the de novo synthesis of terpenoids, flavonoids, and recombinant vaccines in yeasts, especially Saccharomyces cerevisiae and emerging non-conventional species. While enzyme and biopharmaceutical manufacturing use CRISPR for quick strain optimization and glyco-engineering, food and beverage fermentations benefit from starter-culture customization for aroma, texture, and probiotic functionality. Off-target effects, cytotoxicity linked to Cas9, inefficient delivery in specific microorganisms, and regulatory ambiguities in commercial fermentation settings are some of the main challenges. This review provides an industry-specific summary of CRISPR–Cas9 applications in microbial fermentation and highlights technical developments, persisting challenges, and industrial advancements.

Computational and machine learning approaches play a pivotal role in identifying, characterizing, and targeting noncanonical DNA structures, including G-quadruplexes, Z-DNA, hairpins, and triplexes. These configurations play critical roles in maintaining genomic stability, facilitating DNA repair, and regulating chromatin organization. Although the human genome predominantly adopts the B DNA conformation, evidence indicates that non-B DNA forms exert significant influence on gene expression and disease development. This highlights the need for dedicated computational frameworks to systematically investigate these alternative structures. Machine learning model, encompassing supervised and unsupervised algorithms such as K Nearest Neighbors, Support Vector Machines, and deep learning architectures including Convolutional Neural Networks, have shown considerable potential in predicting sequence motifs predisposed to forming non-B DNA conformations. These predictive tools contribute to identifying genomic regions associated with disease susceptibility. Complementary bioinformatics platforms and molecular docking tools, notably Auto Dock, along with chemical libraries like ZINC, facilitate the virtual screening of small molecules targeting specific DNA structures. Stabilizers of G quadruplexes, exemplified by CX 5461, have demonstrated therapeutic promise in BRCA-deficient cancers, highlighting the translational impact of computational methods on drug discovery. Anticipating DNA structural shifts opens new avenues in personalized medicine for complex diseases, with computational chemistry and machine learning deepening our understanding of DNA topology and guiding smarter ligand design. The integrated approach proposed in this review addresses the previous studies performed in this field and highlights the current limitations in structural genomics and advances the development of precision therapeutics aligned with individual genomic profiles.