Our novel strategy for the rational design of immobilized derivatives (RDID) is directed to predict the behavior of the protein immobilized derivative before its synthesis, by the usage of mathematic algorithms and bioinformatics tools. However, this approach needs to be validated for each target enzyme. The objective of this work was to validate the RDID strategy for covalent immobilization of the enzyme laccase from Trametes maxima MUCL 44155 on glyoxyl‐ and monoaminoethyl‐N‐aminoethyl (MANA)‐Sepharose CL 4B supports. Protein surface clusters, more probable configurations of the protein–supports systems at immobilization pHs, immobilized enzyme activity, and protein load were predicted by RDID1.0 software. Afterward, immobilization was performed and predictions were experimentally confirmed. As a result, the laccase‐MANA‐Sepharose CL 4B immobilized derivative is better than laccase‐glyoxyl‐Sepharose CL 4B in predicted immobilized derivative activity (63.6% vs. 29.5%). Activity prediction was confirmed by an experimentally expressed enzymatic activity of 68%, using 2,6‐dimethoxyphenol as substrate. Experimental maximum protein load matches the estimated value (11.2 ± 1.3 vs. 12.1 protein mg/support mL). The laccase‐MANA‐Sepharose CL 4B biocatalyst has a high specificity for the acid blue 62 colorant. The results obtained in this work suggest the possibility of using this biocatalyst for wastewater treatment.

Laccase catalyzes oxidation of lignin and aromatic compound with similar structure to this one. Their low substrate specifi city results on degradation of similar phenolic compounds. In this context, Molecular Docking was performed with different ligands suggesting potential biodegradation. Binding active-sites prediction of fungal laccase (access number uniprotkb: A0A166P2X0), from Ganoderma weberianum was performed using machine learning algorithm based on Deep Convolutional Neural Networks (DeepSite-CNNs chemoinformatic tool). Herein, ligands like 2,4-dichlorophenol, benzidine, sulfi soxazole, trimethoprim and tetracycline were analyzed and two additional reference controls which were 2,2-azinobis 3-ethylbenzothiazoline-6-sulfonic acid (ABTS) and 2,6-dimetoxyphenol (2,6 DMP) were used in comparison with the other former mentioned ligands based on high laccase affi nity. The fi ve ligands were carried out because their potential biotechnological interest: the antibiotics sulfi soxazole, trimethoprim and tetracycline, and xenobiotics 2,4-dichlorophenol and benzidine. Molecular docking experiments returned Gibbs free energy of binding (FEB or affi nity) for laccase-ligand complexes. The best docking binding-interaction from each laccase-ligand conformation complexes suggest great ability of these ligands to interact with the laccase active-binding site. Herein, FEB values (kcal/mol) were obtained with higher affi nity values for reference controls like 2,6-dimethoxyphenol with-4.8 Kcal/mol and ABTS with-7.1 Kcal/mol. Furthermore, the FEB values were-4.7,-6.5,-6.8,-5.2 and-6.5 Kcal/mol, for 2,4-dichlorophenol, benzidine, sulfi soxazole, tetracycline and trimethoprim respectively with high prevalence of hydrophobic interaction with functional laccase binding residues. Lastly, this study presents for fi rst time at the bioinformatics fi eld a molecular docking approach for the prediction of potential substrate of laccase from Ganoderma weberianum towards biotechnological application.

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