Integrating Spectroscopy, Molecular Docking, and Machine Learning to Decipher Mycotoxin–Protein-Binding Mechanisms

This study investigates the molecular interactions of two mycotoxins, ochratoxin A (OTA) and citrinin (CIT), with globular proteins, such as human serum albumin (HSA) and lysozyme (LYS), using an integrated spectroscopic, computational, and machine learning (ML) approach. Fluorescence and UV–visible spectroscopy revealed that OTA binds strongly to HSA through static quenching, with binding constants of 1.12 × 10 5 M –1 at 298 K and 1.33 × 10 4 M –1 at 323 K, indicating enthalpy-driven stabilization via hydrogen bonding and van der Waals forces. In contrast, CIT displayed weaker polarity-driven interactions with both proteins. ML models, particularly random forest and Gaussian process regression, achieved near-perfect prediction of quenching behavior ( R 2 ≈ 0.985), surpassing traditional methods. Molecular docking corroborated spectroscopic findings, highlighting stronger OTA–HSA affinity compared to that of OTA–LYS and CIT complexes. Overall, the results demonstrate that combining spectroscopy, ML, and docking offers quantitative and mechanistic insights into mycotoxin–protein binding, with implications for toxicity assessment and detoxification strategies.