Unlocking The Mysteries of DNA Adducts with Artificial Intelligence

Cellular genome is considered a dynamic blueprint of a cell since it encodes genetic information that gets temporally altered due to various endogenous and exogenous insults. Largely, the extent of genomic dynamicity is controlled by the trade-off between DNA repair processes and the genotoxic potential of the causative agent (genotoxins or potential carcinogens). A subset of genotoxins form DNA adducts by covalently binding to the cellular DNA, triggering structural or functional changes that lead to significant alterations in cellular processes via genetic (e. g., mutations) or non-genetic (e. g., epigenome) routes. Identification, quantification, and characterization of DNA adducts are indispensable for their comprehensive understanding and could expedite the ongoing efforts in predicting carcinogenicity and their mode of action. In this review, we elaborate on using Artificial Intelligence (AI)-based modeling in adducts biology and present multiple computational strategies to gain advancements in decoding DNA adducts. The proposed AI-based strategies encompass predictive modeling for adduct formation via metabolic activation, novel adducts' identification, prediction of biochemical routes for adduct formation, adducts' half-life predictions within biological ecosystems, and, establishing methods to predict the link between adducts chemistry and its location within the genomic DNA. In summary, we discuss some futuristic AI-based approaches in DNA adduct biology. While the experimental approaches are evolving, parallel computational efforts are indispensable to decode DNA adducts. This review accentuates AI's potential in various adduct biology aspects, including novel adduct identification, parent genotoxin associations, formation mechanisms, site of metabolism prediction, and genomic positioning.image

Automated summary

The cellular genome encodes genetic information that is dynamically altered due to various insults. The extent of genomic changes is controlled by the balance between DNA repair processes and the genotoxic potential of causative agents. DNA adducts formed by genotoxins can cause significant alterations in cellular processes. Understanding and predicting DNA adducts are important for predicting carcinogenicity. In this review, the use of AI-based modeling in decoding DNA adducts and various computational strategies are discussed.