AI-Driven Crispr Guide Design for Targeted Biochemical Enhancement of Flavonoid Biosynthesis in Tomato

Enhancing flavonoid biosynthesis in Tomato (Solanum lycopersicum) is of tremendous potential for nutritional enhancement, stress tolerance, and overall crop improvement. Although CRISPR-Cas9 technology holds great promise, current methods for boosting flavonoid biosynthesis in crops are constrained by inefficacies in guide RNA (gRNA) design, off-targeting, and prolonged design times that deter optimizing complex metabolic networks such as flavonoid biosynthesis. This study introduces FlavoCRISPR-AI, a global AI-driven pipeline for intelligent design of highly efficient CRISPR guide RNAs (gRNAs) with the particular aim of optimizing the flavonoid metabolic pathway by genome editing. The methodology integrates genomic mining, prediction of gRNA efficiency with the help of deep learning, filtering based on target specificity, and in silico biochemical simulation. Genomic sequences relevant to the work were accessed from SGN and NCBI, while pathway-specific data for flavonoid biosynthesis were accessed from KEGG and MetaCyc databases. Candidate genes such as CHS (Chalcone Synthase), F3H (Flavanone 3-hydroxylase), and DFR (Dihydroflavonol 4-reductase) were identified by looking for PAM motifs within coding and promoter sequences. The valid gRNAs were then filtered based on the limitations of GC content, melting temperature, and off-target similarity based on Hamming distance. The sequences were then one-hot encoded and passed through a Convolutional Neural Network (CNN) developed using Python with TensorFlow, trained on the manually curated experimental CRISPR datasets to predict on-target activity. High-probability gRNAs were also checked with CRISPOR and CasOFFinder to assess off-target effects. To predict the downstream impact of each edit, candidate gRNAs were subjected to ODE-based in silico simulations of the flavonoid biosynthesis pathway, which predicted alterations in metabolite accumulation. Relative performance comparison determined FlavoCRISPR-AI achieves improved performance, with on-target efficiency AUC of 0.98, off-target prediction accuracy of 0.98, and nine validated high-efficiency gRNAs for every ten designed, and reducing average design time to 6.7[Formula: see text]s per gRNA. This robust Python-coded pipeline demonstrates a scalable and efficient approach to precision genome engineering with useful applications in crop metabolic engineering and functional genomics.