Background: Limitations of Non-Homologous End Joining and Metabolic Genetic Data Bottlenecks in Fungal-Derived Drug R&D

• Traditional filamentous fungi-based research and development of valuable metabolites have been hampered by the inherent dominance of non-homologous end joining (NHEJ) repair mechanisms, leading to double-strand break (DSB) noise and extremely low homologous recombination (HR) efficiency. In the baseline genomic context of strains such as Aspergillus niger and Aspergillus fumigatus, the induction of single-base substitutions, precise insertions, and deletions has resulted in distortions of transcriptomic and metabolomic data matrices due to random NHEJ errors.
• Specifically, in the large-scale enzyme production and secondary metabolite R&D stages of global biotech companies like Novonesis, the inability to computationally control in silico the changes in target gene group sequence variations on microenvironment flux-based feedback loops has led to failures in maintaining effective active concentrations. Furthermore, cell lysis-induced structural collapse noise and batch effects have delayed the collection of multi-omics data, acting as a critical data bottleneck in metabolic engineering R&D.

Discovery: Activation of the fPE7max Modality and Demonstration of Single-Cell Resolution Omics Independent Variable Tensor Synchronization

• To overcome these data bottlenecks, this study developed the fPE7max modality, a highly efficient prime editor system optimized for filamentous fungi, and introduced an in silico computational activation algorithm that includes DNA-RNA hybrid binding free energy calculations during genomic docking. fPE7max has demonstrated disruptive performance improvements by inducing precise base variations, insertions, and deletions with up to 90% efficiency by forming only single-strand nicks without double-strand breaks. This technology, published in Nature Biotechnology (doi: 10.1038/s41587-026-03202-4) on June 30, 2026, surpasses simple gene editing tools.
• By activating a three-dimensional computational tensor synchronization architecture that combines multi-omics independent variable data at single-cell resolution, the study elucidated the topological variations of transcriptional regulator-induced gradients and metabolic networks within filamentous fungi following fPE7max application. This is an innovative demonstration that unprecedentedly proves the molecular biological integrity of programmable genome engineering by pre-excluding potential false-positive target signals and mathematically eliminating batch effects during experimental design.

Tuning of Secondary Metabolic Biosynthetic Pathways and Establishment of a Reversible Homeostatic Precision Layering Model

• Through the fPE7max platform, a novel precision layering model was established that allows for precise clamping of metabolic homeostasis by tuning the rate constants of rate-limiting steps in the structure of the non-ribosomal peptide synthetase active domain, a specific secondary metabolite synthesis pathway in filamentous fungi. By modifying the precise promoter strength or binding affinity of target genes, a reversible homeostatic regulation backbone architecture was secured, allowing for precise up- or down-regulation of gene transcription to prevent cytotoxicity caused by overexpression.
• This system enables reversible tuning of cell viability and target metabolite synthesis capacity in the physicochemical stress conditions of bioreactors based on protein-protein interaction and substrate concentration gradient omics matrix data. Through this precision layering model, metabolically optimized optimal phenotype populations can be computationally screened and predicted within a strain library, supporting the performance layering of omics matrix-based precision layering.

Prospects: Establishing a Standard for Programmable Computational Systems Biology and Activating Next-Generation IND Digital Governance

• This fPE7max mapping architecture represents a complete reset of the academic paradigm in genome editing and metabolic engineering, shifting from static post-analysis to AI-based multidimensional tensor modeling-based programmable computational systems biology. This will be integrated as a high-throughput screening-stage genetic gradient correction coefficient and batch effect control solution in the bio-pharmaceutical, antimicrobial, and natural product drug screening pipelines of global biotech and multinational pharmaceutical companies, serving as a computational moat that guarantees production processes with zero batch-to-batch variation.
• Furthermore, it meets the standardization requirements as a companion diagnostic biomarker discovery and therapeutic target screening platform, and is expected to be firmly established as a high-value digital governance asset that disruptively shortens the approval timeline for clinical trial applications and cGMP commercial production licenses for next-generation bio-pharmaceuticals.