Evaluation of the catalytic activity of candidate enzymes. The mycotoxin standards of ZEA or OTA were added into 500 µL reaction supernatants at a final concentration of 1000 ng/mL. One major method to eliminate mycotoxins using enzymes is to fix enzymes with the feed. These enzymes start to perform detoxification when they reach the stomach of animals along with the feed. To simulate the activity of enzymes under practical conditions (e.g., animal intestines and stomach), we set the temperature of the experimental environment to 37 °C. After incubation at 37 °C for 3 h, 500 µL methanol was added to stop the reaction. The mixture was shaken vigorously and then centrifuged at 13,000 g for 10 min. Finally, the supernatant was filtered using a 0.22-μm filter before being subjected to liquid chromatography-mass spectrometry (LC-MS) analysis. Mycotoxin determination and metabolite analyses were achieved following the previously developed LC-MS method 45 . For the degradation assay under food matrices, mycotoxin standards were added to the wheat and maize flour at a final concentration of 1000 ng/g. Then, 500 µL enzyme supernatant was mixed with 500 mg mycotoxin-contaminated flour. After incubation at 37 °C for 3 h, the reaction was stopped by adding 500 µL methanol. Finally, the mycotoxin in the samples was extracted for analysis as described above. The experiments were independently performed three times (n=3 biologically independent samples), and at least two technical replicates were used in each experimental group. Model interpretability evaluation and MD simulations. To interpret which residues on enzymes and which atoms on substrates are most important for the catalysis process, the multi-head selfattention mechanism 31 was employed by assigning attention weights to the residues and atoms. For instance, a higher attention weight for a residue means that the residue is more important for enzyme activity toward the specific substrate, a higher attention weight for an atom means that it is important for binding with enzymes or means it may be present at the reaction sites (i.e., the atoms and bonds changed in the substrate during reactions) 21 . To evaluate the model's recognition ability for the reaction sites on substrates, we compared attention weights with known reaction sites collected from EnzyMine 46 . Atoms less than one bond distance from bonds that changed in the reaction were considered reaction sites. We considered it to be identified correctly if an atom in a reaction site was ranked in the top 2N (N = the number of atoms in reaction sites) of all atoms because atoms with other functions (e.g., providing binding affinity with enzymes) may also be top ranked. We also randomly generated equal numbers of atoms on each substrate. Then, we compared the recognition ratio of atoms in reaction sites by PU-EPP and the recognition ratio of the randomly generated result. To evaluate the correctness of important residues inferred using the attention mechanism, we analyzed the important residues on ZH4 and OH1 enzymes using MD simulations . The first 25 amino acids in OH1 were deleted because they were likely to serve as a signal peptide. Then, the consistency of the results achieved by these two approaches was evaluated. To conduct MD simulations, an initial guess of the small molecule binding site in the enzyme was detected by BiteNet () with the protein structure predicted by AlphaFold 47 as input. Additionally, four extra putative sites (e.g., pockets and grooves) were manually identified. The small molecule was then docked to each putative site via AutoDock Vina using the default scoring function. For docking, a 25 × 25 × 25 Å cubic box was set up to encompass the binding site, and the exhaustiveness value was set as 32. For each putative site, nine binding poses were obtained. Then, we selected the 10 top-ranked binding poses to conduct all-atom MD simulations.