A structural-chemical explanation of fungal laccase activity

Abstract

Fungal laccases (EC 1.10.3.2) are multi-copper oxidases that oxidize a wide variety of substrates. Despite extensive studies, the molecular basis for their diverse activity is unclear. Notably, there is no current way to rationally predict the activity of a laccase toward a given substrate. Such knowledge would greatly facilitate the rational design of new laccases for technological purposes. We report a study of three datasets of experimental K_m values and activities for Trametes versicolor and Cerrena unicolor laccase, using a range of protein modeling techniques. We identify diverse binding modes of the various substrates and confirm an important role of Asp-206 and His-458 (T. versicolor laccase numbering) in guiding substrate recognition. Importantly, we demonstrate that experimental K_m values correlate with binding affinities computed by MMGBSA. This confirms the common assumption that the protein-substrate affinity is a major contributor to observed K_m. From quantitative structure-activity relations (QSAR) we identify physicochemical properties that correlate with observed K_m and activities. In particular, the ionization potential, shape, and binding affinity of the substrate largely determine the enzyme's K_m for the particular substrate. Our results suggest that K_m is not just a binding constant but also contains features of the enzymatic activity. In addition, we identify QSAR models with only a few descriptors showing that phenolic substrates employ optimal hydrophobic packing to reach the T1 site, but then require additional electronic properties to engage in the subsequent electron transfer. Our results advance our ability to model laccase activity and lend promise to future rational optimization of laccases toward phenolic substrates.

Figure 1

(a) TvL (Protein Data Bank code 1GYC) showing the three domains and the T1, T2 and T3 copper sites. (b) Sequence alignment of TvL and CuL: Amino acid residues in agreement with the consensus are colored based on polarity (positive charge: blue; negative charge: red; polar uncharged: green; apolar uncharged: orange); important structural features are highlighted below or above the sequence: copper-binding sites (brown), substrate-binding residues (orange), and flexible loops close to the substrate-binding site (red).

Figure 2

Binding modes of representative compounds of the TvL datasets A and B. Sinapic acid (a) and syringaldazine (b) are dataset A compounds. Compounds from (c) to (f) are dataset B substrates.

Figure 3

Representative bound conformations of substrates binding to CuL (dataset C).

Figure 4

Plots of the maximum correlation observed for the three datasets (A, B and C) between ∆G_bind(MMGBSA) (kcal/mol) and pK_m or relative reported activity. (a) Dataset A compounds showed a R² value of 0.10 at pH 4.5 and 0 Å flexibility. (b) For dataset B, a maximum R² value of 0.29 was observed. (c) When the outlier compound p-hydroxybenzoic acid was removed from the dataset B, the correlation was reduced to 0.02. (d) The dataset C showed the highest R² value of 0.33 at pH 4.5 with 8 Å protein flexibility.

Figure 5

Scatter plots of the descriptors (computed values without normalization) of the QSAR models showing significant correlation (>95% confidence) with log(activity) or pK_m. (a) QPlogPo/w found in the QSAR of free ligand states of dataset B. (b) ESP max showed a better correlation with log (activity) of dataset B when using the bound state than when using free ligand states. (c) IP (in units of eV) displayed a good correlation with pK_m of dataset C. (d) Vol/SASA of the bound state of dataset C compounds showed a good correlation with pK_m.

Figure 6

Binding modes of the in-house evaluated substrates.

Figure 7

Prediction of the substrate activities using our recommended pK_m models 5 and 6. (a) Predictions on in-house evaluated compounds. For OH-dilignol, a range of predicted pK_m was obtained as shown in Supplementary Table S12. (b) Externally validated trend prediction using models 5 and 6 (please see text for details).

Figure 8

RMSF plots of the residues for the last 20 ns of MD simulations. (a) Plot of the RO state of TvL states. (b) Plot of the 3e⁻ reduced state of TvL states. (c) Plot of the RO state of CuL states. (d) Plot of the 3e⁻ reduced state of CuL states. (e) Loop regions of TvL (magenta colored) and CuL (yellow colored) states showing high fluctuations during MD and change in conformation in the representative MD structure. Residue numbering is according to the TvL structure (1GYC).