TY - JOUR
AU - Westh,, Peter
AB - Abstract The glycoside hydrolase (GH) family 6 is an important group of enzymes that constitute an essential part of industrial enzyme cocktails used to convert lignocellulose into fermentable sugars. In nature, enzymes from this family often have a carbohydrate binding module (CBM) from the CBM family 1. These modules are known to promote adsorption to the cellulose surface and influence enzymatic activity. Here, we have investigated the functional diversity of CBMs found within the GH6 family. This was done by constructing five chimeric enzymes based on the model enzyme, TrCel6A, from the soft-rot fungus Trichoderma reesei. The natural CBM of this enzyme was exchanged with CBMs from other GH6 enzymes originating from different cellulose degrading fungi. The chimeric enzymes were expressed in the same host and investigated in adsorption and quasi-steady-state kinetic experiments. Our results quantified functional differences of these phylogenetically distant binding modules. Thus, the partitioning coefficient for substrate binding varied 4-fold, while the maximal turnover (kcat) showed a 2-fold difference. The wild-type enzyme showed the highest cellulose affinity on all tested substrates and the highest catalytic turnover. The CBM from Serendipita indica strongly promoted the enzyme’s ability to form productive complexes with sites on the substrate surface but showed lower turnover of the complex. We conclude that the CBM plays an important role for the functional differences between GH6 wild-type enzymes. Introduction The glycoside hydrolase (GH) family 6 is an important group of enzymes involved in the breakdown of cellulose in nature and in industrial biomass saccharification processes (Nidetzky and Claeyssens, 1994; Lynd et al., 2002). The enzymes hydrolyze the β-1,4 glycosidic bonds in cellulose by an inverting mechanism and convert the insoluble polysaccharide into soluble sugars, predominantly cellobiose (Claeyssens et al., 1990; Rouvinen et al., 1990; Teeri, 1997). The GH6 enzymes are produced by numerous microorganisms, including fungi that are found in many different habitats (Carlile et al., 2001). More than half of the enzymes in the GH6 family are multi-modular (Varnai et al., 2013) and contain both a catalytic domain (CD) and an N-terminal carbohydrate binding module (CBM) connected through a flexible peptide linker. Fungal cellulolytic enzymes possess almost exclusively CBMs from family 1 (CBM1) (Boraston et al., 2004; Lombard et al., 2014; Bateman et al., 2018), which has been extensively characterized (reviewed in (Varnai et al., 2014)). The function of the modules is mainly to enhance cellulose adsorption, resulting in an increased concentration of enzymes on the cellulose surface (Tomme et al., 1988; Ståhlberg et al., 1991; Reinikainen et al., 1992; Palonen et al., 1999), but CBMs may also be involved in the disruption of crystalline cellulose (Arantes and Saddler, 2010; Bernardes et al., 2019). The CBM1 has a wedge-shaped architecture, consisting of a rough face and a planer face (Kraulis et al., 1989; Mattinen et al., 1998). The latter contains three aromatic amino acids that have been shown to interact with the cellulose surface (Kraulis et al., 1989; Linder et al., 1995b; Boraston et al., 2004). The most well-characterized GH6 enzyme is the cellobiohydrolase (CBH) from the soft-rot fungus Trichoderma reesei, henceforth denoted TrCel6A (reviewed in (Payne et al., 2015)). This enzyme possesses an N-terminal CBM of 39 amino acids, with a tryptophan (W7) and two tyrosine residues (Y33 and Y34) in the binding face. Despite much interest in TrCel6A, the GH6 family is sparsely characterized biochemically, and the functional breadth of the GH6 family remains poorly explored. Recently, we provided comparative data for a group of different fungal GH6 CBHs (Christensen et al., 2019) and found that their functional parameters varied distinctively. This variability was particularly pronounced when comparing single domain enzymes with enzymes that contained a CBM, but we also found distinctive variation in the adsorption and kinetics of GH6 cellobiohydrolases with CBMs. Whether these differences were related to structural variations in the CDs or the CBMs was not investigated. In general, the functional diversity of CBMs connected to GH6 enzymes is sparsely described and numerous questions remain unanswered regarding the importance of the CBM structure for the enzyme function. In the current study, we aim to address this by creating five chimeric enzymes based on the CD and linker from TrCel6A, and different CBMs originating from GH6 enzymes from fungi living in different habitats. The investigated CBMs were selected on the basis of sequence similarity to ensure that they were not closely related. The enzyme variants were investigated with respect to adsorption to three different cellulosic substrates and quasi-steady-state kinetics. Our results showed that the phylogenetically distinct CBMs were also functionally different with respect to substrate affinity and the catalytic turnover, indicating that the CBMs are important for the functional differences between GH6 enzymes from different organisms. Material and Methods Phylogentic analysis A total of 187 fungal amino acid sequences annotated as GH6 enzymes were selected from the (carbohydrate active enzyme) CAZy database (Lombard et al., 2014) and the National Center for Biotechnology Information database (Geer et al., 2010). A multiple sequence alignment was performed using ClustalW (Thompson et al., 1994). The pairwise and multiple alignment gap-opening values were set to 30, and all other parameters were set to default values. Sequences without a CBM were deleted and the remaining (101) sequences were reduced to their CBM sequences (position 1–39 according to the TrCel6A numbering), followed by a re-alignment. Identical CBM sequences were removed manually. The evolutionary history of the remaining 63 sequences was inferred by using the maximum likelihood method based on the Whelan and Goldman model (Whelan and Goldman, 2001). The tree with the highest log likelihood (−2246.1561) is shown. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using a JTT model, and then selecting the topology with a superior log likelihood value. A discrete Gamma distribution was used to model evolutionary rate differences among sites. The rate variation model allowed for some sites to be evolutionarily invariable. Alignments and phylogenetic analysis was performed in MEGA7 (Kumar et al., 2016). The phylogenetic tree was edited in the software FigTree v1.4.4 (http://tree.bio.ed.ac.uk/software/figtree/). Enzymes Five chimeric enzymes were created based on the TrCel6A CD and its linker. For each of these chimeras, the natural CBM was exchanged with the CBM from one of the following characterized or putative GH6 enzymes: Podospora anserina [CDP24957.1], Neocallimastix frontalis [AAQ93324.1], Serendipita indica [CCA68892.1], Coprinopsis cinerea [BAH08702.1], and Volvariella volvacea [AAT64008.1]. Accession numbers (Genbank) are shown in brackets. The exchanged region was residue 1–38 according to the TrCel6A numbering. The alanine on position 1 in N. frontalis [AAQ93324.1] was mutated to a glutamine, so that all CBM sequences started with a glutamine. Henceforth, we will name the chimeras by TrCel6A followed by initial letters of genus and species of the CBM source (Pa, Nf, Si, Cc, and Vv), while the wild-type enzyme (with its native CBM) will henceforth be called TrCel6Awt. The enzymes were heterologously expressed in Aspergillus oryzae and purified as described elsewhere (Borch et al., 2014; Sørensen et al., 2015). The purity of the enzymes was confirmed as a single band on a sodium dodecyl sulfate polyacrylamide electrophoresis gel. The enzyme concentration was determined by absorbance at 280 nm using theoretical extinction coefficients (Gasteiger et al., 2005) of 97 790 M−1 cm−1 (TrCel6Awt), 100 310 M−1 cm−1 (TrCel6APa), 94 810 M−1 cm−1 (TrCel6ANf), 103 165 M−1 cm−1 (TrCel6ASi), 96 175 M−1 cm−1 (TrCel6ACc), and 101 675 M−1 cm−1 (TrCel6AVv). Substrates The cellulose substrate Avicel PH101 (Sigma-Aldrich, St. Louis, MO) was washed four times in MilliQ water and once in 50 mM acetate, pH 5.0 (henceforth called standard buffer), prior to the experiments. Regenerated amorphous cellulose (RAC) was prepared from Avicel as described elsewhere (Zhang et al., 2006; Christensen et al., 2018). Bacterial microcrystalline cellulose (BMCC) was prepared from bacterial cellulose as described elsewhere (Väljamäe et al., 1999; Cruys-Bagger et al., 2013b). Finally, we used so-called washed pre-treated corn stover (PCS) as an example of a lignocellulosic substrate. PCS is made by mild acid pre-treatment of corn stalks as described elsewhere (Selig et al., 2007; Murphy et al., 2010), and suspended in the same acetate buffer as the pure cellulosic substrates. Fig. 1 Open in new tabDownload slide (A) WebLogo of family 1 CBMs from GH6 enzymes. The sequence logo was constructed from an alignment of 63 sequences. The letter size is proportional to the degree of amino acid conservation. The logo was generated using WebLogo version 2.8.2 (Crooks et al., 2004). (B) Alignment of the CBMs investigated in this study. The color intensity indicates the degree of amino acid conservation. The numbering was according to CBMTr, and the gap between the positions 31 and 32 was not included in the numbering. Gray stars indicate positions with cysteine residues. Black stars are aromatic residues in the planar surface involved in cellulose binding. See the online manuscript for a version in colors Fig. 1 Open in new tabDownload slide (A) WebLogo of family 1 CBMs from GH6 enzymes. The sequence logo was constructed from an alignment of 63 sequences. The letter size is proportional to the degree of amino acid conservation. The logo was generated using WebLogo version 2.8.2 (Crooks et al., 2004). (B) Alignment of the CBMs investigated in this study. The color intensity indicates the degree of amino acid conservation. The numbering was according to CBMTr, and the gap between the positions 31 and 32 was not included in the numbering. Gray stars indicate positions with cysteine residues. Black stars are aromatic residues in the planar surface involved in cellulose binding. See the online manuscript for a version in colors Adsorption experiments Enzyme adsorption was investigated by pull-down assays with fixed concentrations of substrate (Avicel; 20 g L−1, RAC; 0.5 g L−1 or BMCC; 2.5 g L−1) and enzyme concentrations ranging from 0.05 to 3 μM. Following incubation for 1 h at 25°C, the samples were centrifugated at 2500g for 3 min. Supernatants (50 μL) were diluted in standard buffer (150 μL). The concentration of free enzyme in the diluted supernatant was determined by the intrinsic fluorescence as described elsewhere (Sørensen et al., 2015) and calculated against standard samples of enzyme in standard buffer. All experiments were carried out in triplicates. Activity measurements We conducted both enzyme saturation (conventional Michaelis–Menten (MM)) and substrate saturation (inverse MM) kinetic experiments as detailed elsewhere (Cruys-Bagger et al., 2013a; Kari et al., 2017). In the conventional MM approach, where the substrate is in excess, 0.02 μM enzyme was incubated with increasing Avicel loads ranging from 4.4 to 100.3 g L−1. For the inverse MM approach, where the enzyme is in excess, samples with 5 g L−1 Avicel were incubated with increasing enzyme concentration ranging from 0.2 to 10 μM. Hydrolysis was done for 1 h at 25°C in a thermomixer operated at 1100 rpm. Experiments were stopped by centrifugation at 2500g for 3 min. Reducing end concentrations in the supernatant were quantified by the para-hydroxybenzoic acid hydrazide (PAHBAH) method (Lever, 1973). Procedures of the assay are described elsewhere (Sørensen et al., 2015). Absorption at 405 nm was determined in a plate reader (Spectra Max 3, Molecular Devices, Sunnyvale, CA) and compared with a cellobiose standard series with concentrations from 0 to 1000 μM. The rate, defined by the increment in the cellobiose concentration after 1 h contact time, was determined from the slope of the cellobiose concentration between t = 0 and 1 h. The activity on PCS was measured by incubating 25 g L−1 PCS with 2.5-μM enzyme at 50°C for 24 h in a thermomixer operated at 1100 rpm. The reactions were stopped by centrifugation at 1500g for 3 min. The supernatants were diluted 1:10, and the reducing end concentrations were quantified by the PAHBAH method as described above. All experiments were carried out in triplicates. Results Sequence analysis To investigate the sequence diversity of CBMs from GH6 enzymes, we created an alignment consisting of 63 homologous CBM sequences. The alignment was condensed to a WebLogo shown in Fig. 1A. Seven amino acids were completely conserved (Q9, C10, G11, G12, Y34, Q36, and C37 according to the numbering of TrCel6A). Positions 7, 15, 33, and 34 were always aromatic amino acids. Two cysteine residues were conserved (C10 and C37) and two other cysteines were only absent in two of the 63 sequences (C21 and C27). Two additional cysteines (C3 and C20) were present in around one-fourth of the sequences. The three cysteine pairs are believed to be involved in disulfide bridges (Kraulis et al., 1989; Hoffren et al., 1995; Mattinen et al., 1998; Carrard and Linder, 1999). In order to elucidate the functional diversity within the group of GH6 associated CBMs, we selected five CBM sequences (Fig. 1B) from the alignment to serve as suitable representatives for this group. In order to select CBMs with high diversity, we chose sequences with different aromatic residues on position 7, 15, and 33, absence of C3/C20, and presence of additional aromatic residues and other uncommon amino acids. The selected CBMs were fused with the CD and linker from TrCel6Awt and expressed in the same expression host (A. oryzae). This is important to ensure that the enzymes are modified by the same glycosylation system, which provides a good fundament for direct functional comparison of the chimeric enzymes. The five purified chimeric enzymes were denoted TrCel6APa, TrCel6AVv, TrCel6ACc, TrCel6ASi, and TrCel6ANf, while the wild-type enzyme is called TrCel6Awt. The amino acid sequences of the different CBMs (henceforth called CBMTr, CBMPa, CBMVv, CBMCc, CBMSi, and CBMNf) are shown in Fig. 1B. The identity between these CBMs was between ~50 and ~75% (Supplementary Fig. S3). The CBMs have some noteworthy differences including very uncommon residues on some positions such as W, H, and Y at position 35 (in respectively CBMVv, CBMCc, and CBMSi). Three of the selected modules have all three cysteine pairs (CBMTr, CBMPa, and CBMNf), while the three remaining (CBMCc, CBMSi, and CBMNf) did not have the C3/C20 pair (Fig. 1B). Fig. 2 Open in new tabDownload slide Phylogenetic tree of 63 CBM1 amino acid sequences, this is naturally connected with fungal GH6 domains. The CBM sequences were aligned according to Fig. 1A. An unrooted maximum likelihood tree was drawn as a radiation model in MEGA7. The scale bar is 0.2 amino acid substitutions per site. The taxonomic rank of the organisms from which the CBMs originate is indicated above the marked clades (indicated with colors in the online manuscript). The bootstrap analysis and CBM sequences with accession numbers are given in Supplementary Fig. S1. CBMs from the current study are indicated by the abbreviations specified in the main text. See the online manuscript for a version in colors Fig. 2 Open in new tabDownload slide Phylogenetic tree of 63 CBM1 amino acid sequences, this is naturally connected with fungal GH6 domains. The CBM sequences were aligned according to Fig. 1A. An unrooted maximum likelihood tree was drawn as a radiation model in MEGA7. The scale bar is 0.2 amino acid substitutions per site. The taxonomic rank of the organisms from which the CBMs originate is indicated above the marked clades (indicated with colors in the online manuscript). The bootstrap analysis and CBM sequences with accession numbers are given in Supplementary Fig. S1. CBMs from the current study are indicated by the abbreviations specified in the main text. See the online manuscript for a version in colors The 3D structure has not been solved for any CBM that is naturally connected to a GH6 enzyme. However, CBM1 structures of the related GH7 enzymes TrCel7A and TrCel7B have been solved by NMR spectroscopy (Kraulis et al., 1989; Mattinen et al., 1998). The high homology between CBMs from family 1 enables structure prediction by homology modeling, as suggested earlier (Hoffren et al., 1995) and shown in Supplementary Fig. S2. Such models show that the CBMs investigated in this study have similar folds and that the three aromatic amino acids are exposed to the planar binding face as expected (Supplementary Fig. S2) (Kraulis et al., 1989; Hoffren et al., 1995; Mattinen et al., 1998). One module (CBMSi) did not align well with the remaining, probably due to a high level of prolines that introduces turns in the primary structure (Supplementary Fig. S2). The diversity of the CBMs from the GH6 family was further investigated by creating a phylogenetic tree (Fig. 2) based on the alignment mentioned above. The sequences are most often located in clades related to their taxonomic rank and the five selected CBM sequences spread across the phylogenetic tree. The phylogenetic tree was not statistically well supported in the bootstrap analysis as shown in Supplementary Fig. S1. However, the tree gave a fair indication that the selected CBMs were not phylogenetically closely related. Cellulose adsorption Adsorption of the purified chimeric enzymes onto cellulose was assessed by experiments with different cellulosic substrates (Avicel, 20 g L−1; RAC, 0.5 g L−1 or BMCC, 2.5 g L−1). The amount of adsorbed enzyme per gram substrate (in μmol g−1) was determined as |$\varGamma =\frac{E_0-{E}_{\mathrm{free}}}{S_0}$|⁠, where E0 is the initial enzyme concentration, Efree is the free enzyme concentration, and S0 is the initial substrate load. Figure 3 shows a plot of Γ as a function of Efree, indicating diverging adsorption behavior of the six chimeras. From the graphs in Fig. 3, we determined the slope for E0 → 0, which is the partitioning coefficient (Kp) reflecting the equilibrium between free and bound enzymes at low enzyme concentration (E0 ≪ Kd). We used Kp to quantify the affinity of the enzyme to cellulose, since this parameter has earlier been suggested as a good descriptor for that purpose (Linder and Teeri, 1996; Palonen et al., 1999). As reflected in Fig. 3, the KP values vary between the enzymes. A common trend was observed for the Kp values on all three substrates, since TrCel6Awt and TrCel6AVv exhibit the highest values while TrCel6APa and TrCel6ASi exhibit the lowest. The two remaining (TrCel6ACc and TrCel6ANf) fell between these both for Avicel and RAC. On BMCC, they had Kp values similar to TrCel6APa, and TrCel6ASi. Quasi-steady-state kinetics The chimeras were investigated by two quasi-steady-state approaches that are described in detail elsewhere (Cruys-Bagger et al., 2013a; Kari et al., 2017). In short, the first method is based on substrate excess experiments, where the specific quasi-steady-state rate (vss/E0) is determined at a fixed, low initial enzyme concentration (E0) at gradually increasing initial substrate loads (S0). This corresponds to the conventional MM framework, and when vss/E0 is plotted against the substrate load (in g L−1), we see the conventional hyperbolic saturation curves (Fig. 4). We fitted the conventional MM (convMM) equation (equation (1)) to the data points. $$\begin{equation} {v}_{\mathrm{ss}}=\frac{{}^{\mathrm{conv}}{V}_{\mathrm{max}}{S}_0}{{}^{\mathrm{conv}}{K}_{\mathrm{M}}+{S}_0} \end{equation}$$ (1) In equation (1), convVmax is the maximum reaction rate at enzyme saturation and convKM is the substrate load at half saturation in g L−1. As shown in Fig. 4A, the non-linear fits accounted well for the experimental data, and the derived quasi-steady-state parameters are given in Table I. We note that the formal requirement for quasi-steady-state in this approach is a large (molar) excess of substrate, S0 ≫ KM + E0 (Tzafriri and Edelman, 2005; Bajzer and Strehler, 2012), but this condition is not straightforward to validate because the molar concentration of substrate is unknown. Systematic empirical investigations of this question (Kari et al., 2019) suggested that 1 h end-point measurements provided a reasonable compromise for the determination of the initial steady-state rate for the current system. We note, however, that this is an approximation and that the derived kinetic parameters hence are apparent values. Fig. 3 Open in new tabDownload slide Adsorption experiments of TrCel6Awt and five chimeric enzymes. Bound enzyme (Γ) was plotted as a function of the free enzyme concentration (Efree) in the presence of different cellulosic substrates: (A) 20 g L−1 Avicel, (B) 0.5 g L−1 RAC, and (C) 2.5 g L−1 BMCC. Symbols are experimental data points and lines represent fits to a hyperbolic function to guide the eye. Error bars indicate ±SD of triplicate measurements. Bar charts in the left column are the initial slopes of the curves (Kp values). See the online manuscript for a version in colors Fig. 3 Open in new tabDownload slide Adsorption experiments of TrCel6Awt and five chimeric enzymes. Bound enzyme (Γ) was plotted as a function of the free enzyme concentration (Efree) in the presence of different cellulosic substrates: (A) 20 g L−1 Avicel, (B) 0.5 g L−1 RAC, and (C) 2.5 g L−1 BMCC. Symbols are experimental data points and lines represent fits to a hyperbolic function to guide the eye. Error bars indicate ±SD of triplicate measurements. Bar charts in the left column are the initial slopes of the curves (Kp values). See the online manuscript for a version in colors Fig. 4 Open in new tabDownload slide Quasi-steady-state kinetics of TrCel6Awt and five chimeric enzymes in conventional and inverse MM analysis. (A) The conventional MM where the specific quasi-steady-state rate (in the unit s−1) was determined at low enzyme concentration (20 nM) and increasing Avicel loads. (B) The inverse MM where the specific quasi-steady-state rate (in the unit μmol g−1 s−1) was measured at a low Avicel load (5 g L−1) and increasing enzyme concentrations. Symbols are experimental data points and lines are best non-linear fits to equation (1) (A) and equation (2). (B). Error bars represent ±SD of triplicate measurements. See the online manuscript for a version with colors Fig. 4 Open in new tabDownload slide Quasi-steady-state kinetics of TrCel6Awt and five chimeric enzymes in conventional and inverse MM analysis. (A) The conventional MM where the specific quasi-steady-state rate (in the unit s−1) was determined at low enzyme concentration (20 nM) and increasing Avicel loads. (B) The inverse MM where the specific quasi-steady-state rate (in the unit μmol g−1 s−1) was measured at a low Avicel load (5 g L−1) and increasing enzyme concentrations. Symbols are experimental data points and lines are best non-linear fits to equation (1) (A) and equation (2). (B). Error bars represent ±SD of triplicate measurements. See the online manuscript for a version with colors In the second approach, the specific steady-state rate (vss/S0) was determined at enzyme saturation conditions, i.e. a constant, low substrate load (5 g L−1) and gradually increasing concentration of enzyme (0.2–10 μM). When vss/S0 is plotted against E0, we again obtain hyperbolic curves (Fig. 4B). The data points were fitted to the so-called inverse MM (invMM) equation, which can be derived by using a quasi-steady-state approximation under enzyme excess condition (McLaren and Packer, 1970; Bailey, 1989). $$\begin{equation} {v}_{\mathrm{ss}}=\frac{{}^{\mathrm{inv}}{V}_{\mathrm{max}}{E}_0}{{}^{\mathrm{inv}}{K}_{\mathrm{M}}+{E}_0} \end{equation}$$ (2) In equation (2), invVmax is the maximum reaction rate where the substrate is fully covered with enzyme. More specifically, all accessible chain ends that the enzyme can turnover (henceforth called attack sites) are complexed with enzyme (the excess enzyme is in the bulk). The parameter invKM is the enzyme concentration where half of the attack sites are in complex with an enzyme. As shown in Fig. 4, equation (2) accounts well for the data and the parameters derived from these fits are also listed in Table 1. The specificity constant (η = (convVmax/E0)/(convKM)) given in Table 1 was highest for the wild-type enzyme (and TrCel6APa) and comparable to values determined earlier for TrCel6Awt (Christensen et al., 2019). In enzyme catalysis with insoluble substrates, such as cellulose, the molar substrate concentration (i.e. the number of attack sites on the surface) is usually unknown. However, if we introduce ΓAttack that denotes the number of attack sites per gram substrate in the unit μmol g−1, we can define the molar substrate concentration as molarS0 = ΓAttackS0. Since invVmax denotes the average reaction rate when all attack sites are complexed with enzyme, we may write invVmax = molarS0kcat = S0ΓAttackkcat, where kcat is the catalytic rate of the reaction. Since convVmax gives the turnover of each enzyme, convVmax = E0kcat, it follows that the two maximum parameters can be used to determine ΓAttack as shown in equation (3) (Kari et al., 2017). $$\begin{equation} \frac{{{}^{i\mathrm{nv}}V}_{\mathrm{max}}/\left[{S}_0\right]}{{{}^{\mathrm{conv}}V}_{\mathrm{max}}/\left[{E}_0\right]}={\varGamma}_{\mathrm{attack}} \end{equation}$$ (3) Equation (3) provides a rough estimate of the number of attack sites per gram substrate (Kari et al., 2017), and ΓAttack values derived from the parameters obtained from Fig. 4 are shown in Table 1. Results from the activity measurements on the lignocellulosic substrate PCS are shown in Supplementary Fig. S4. It appears that under the conditions used here (24-h hydrolysis at 50°C), differences in the performance of the chimeras are limited and within the experimental scatter. Table I Kinetic parameters for Avicel hydrolysis of TrCel6A chimeras, derived from regression analysis in Fig. 4 Enzyme . Conventional MM . Inverse MM . . convVmax/E0 . convKM . ηa . invVmax/S0 . invKM . ΓAttack . . (s−1) . (g L−1) . (L g−1 s−1 × 103) . (nmol g−1 s−1) . (μM) . (nmol g−1) . TrCel6Awt 0.95 ± 0.03 21.3 ± 2.0 4.5 16.1 ± 0.5 0.7 ± 0.1 17.0 ± 0.8 TrCel6APa 0.73 ± 0.02 16.2 ± 1.2 4.5 15.4 ± 0.9 0.9 ± 0.2 21.2 ± 1.4 TrCel6ANf 0.70 ± 0.05 22.4 ± 4.2 3.1 14.3 ± 0.8 1.0 ± 0.2 20.4 ± 1.8 TrCel6ASi 0.44 ± 0.03 26.0 ± 5.8 1.7 21.7 ± 1.6 2.5 ± 0.5 49.9 ± 5.6 TrCel6ACc 0.82 ± 0.04 25.7 ± 3.6 3.1 14.4 ± 0.7 1.0 ± 0.2 17.7 ± 1.3 TrCel6AVv 0.90 ± 0.07 30.1 ± 5.9 3.0 16.6 ± 0.6 1.0 ± 0.1 18.4 ± 1.6 Enzyme . Conventional MM . Inverse MM . . convVmax/E0 . convKM . ηa . invVmax/S0 . invKM . ΓAttack . . (s−1) . (g L−1) . (L g−1 s−1 × 103) . (nmol g−1 s−1) . (μM) . (nmol g−1) . TrCel6Awt 0.95 ± 0.03 21.3 ± 2.0 4.5 16.1 ± 0.5 0.7 ± 0.1 17.0 ± 0.8 TrCel6APa 0.73 ± 0.02 16.2 ± 1.2 4.5 15.4 ± 0.9 0.9 ± 0.2 21.2 ± 1.4 TrCel6ANf 0.70 ± 0.05 22.4 ± 4.2 3.1 14.3 ± 0.8 1.0 ± 0.2 20.4 ± 1.8 TrCel6ASi 0.44 ± 0.03 26.0 ± 5.8 1.7 21.7 ± 1.6 2.5 ± 0.5 49.9 ± 5.6 TrCel6ACc 0.82 ± 0.04 25.7 ± 3.6 3.1 14.4 ± 0.7 1.0 ± 0.2 17.7 ± 1.3 TrCel6AVv 0.90 ± 0.07 30.1 ± 5.9 3.0 16.6 ± 0.6 1.0 ± 0.1 18.4 ± 1.6 aThe specificity constant, determined as η = (convVmax/E0)/(convKM). Open in new tab Table I Kinetic parameters for Avicel hydrolysis of TrCel6A chimeras, derived from regression analysis in Fig. 4 Enzyme . Conventional MM . Inverse MM . . convVmax/E0 . convKM . ηa . invVmax/S0 . invKM . ΓAttack . . (s−1) . (g L−1) . (L g−1 s−1 × 103) . (nmol g−1 s−1) . (μM) . (nmol g−1) . TrCel6Awt 0.95 ± 0.03 21.3 ± 2.0 4.5 16.1 ± 0.5 0.7 ± 0.1 17.0 ± 0.8 TrCel6APa 0.73 ± 0.02 16.2 ± 1.2 4.5 15.4 ± 0.9 0.9 ± 0.2 21.2 ± 1.4 TrCel6ANf 0.70 ± 0.05 22.4 ± 4.2 3.1 14.3 ± 0.8 1.0 ± 0.2 20.4 ± 1.8 TrCel6ASi 0.44 ± 0.03 26.0 ± 5.8 1.7 21.7 ± 1.6 2.5 ± 0.5 49.9 ± 5.6 TrCel6ACc 0.82 ± 0.04 25.7 ± 3.6 3.1 14.4 ± 0.7 1.0 ± 0.2 17.7 ± 1.3 TrCel6AVv 0.90 ± 0.07 30.1 ± 5.9 3.0 16.6 ± 0.6 1.0 ± 0.1 18.4 ± 1.6 Enzyme . Conventional MM . Inverse MM . . convVmax/E0 . convKM . ηa . invVmax/S0 . invKM . ΓAttack . . (s−1) . (g L−1) . (L g−1 s−1 × 103) . (nmol g−1 s−1) . (μM) . (nmol g−1) . TrCel6Awt 0.95 ± 0.03 21.3 ± 2.0 4.5 16.1 ± 0.5 0.7 ± 0.1 17.0 ± 0.8 TrCel6APa 0.73 ± 0.02 16.2 ± 1.2 4.5 15.4 ± 0.9 0.9 ± 0.2 21.2 ± 1.4 TrCel6ANf 0.70 ± 0.05 22.4 ± 4.2 3.1 14.3 ± 0.8 1.0 ± 0.2 20.4 ± 1.8 TrCel6ASi 0.44 ± 0.03 26.0 ± 5.8 1.7 21.7 ± 1.6 2.5 ± 0.5 49.9 ± 5.6 TrCel6ACc 0.82 ± 0.04 25.7 ± 3.6 3.1 14.4 ± 0.7 1.0 ± 0.2 17.7 ± 1.3 TrCel6AVv 0.90 ± 0.07 30.1 ± 5.9 3.0 16.6 ± 0.6 1.0 ± 0.1 18.4 ± 1.6 aThe specificity constant, determined as η = (convVmax/E0)/(convKM). Open in new tab Discussion CBMs are non-catalytical modules found in various carbohydrate active enzymes including cellulases. The modules are currently categorized into 86 families in the CAZy database (Lombard et al., 2014). CBMs from family 1 are almost exclusively found among fungi and are most often connected with cellulolytic enzymes. However, family 1 CBMs are also found on enzymes with other activities such as putative cutinases and lipases, according to the Pfam database (https://pfam.xfam.org/) (Bateman et al., 2018). This indicates broad and divergent specificities of CBMs within this family, despite their quite conserved fold (Hoffren et al., 1995). The GH6 family has only been associated with cellulose hydrolysis so far. Therefore, CBMs found on GH6 enzymes are most likely optimized to adsorb to cellulose. Yet, different modules might have different adsorption preferences. For instance, it has been shown that different CBMs (e.g. the TrCel6A and TrCel7A CBM) target different structures on the cellulose surface and while some target amorphous regions, others prefer crystalline regions (Fox et al., 2013). We have previously demonstrated that different wild-type enzymes in the GH6 family exhibit quite divergent affinity to cellulose (Christensen et al., 2019) and the CBM might play an important role for this discrepancy. In the present study, we have investigated this by making chimeric enzymes with CBMs originating from different fungi. These CBMs spread across a phylogenetic tree with CBMs found on different GH6 enzymes (Fig. 2), which indicates that the selected modules may be good representatives for the phylogenetical breadth within this group. To elucidate if the modules are also functionally divergent, we made a thorough characterization by performing adsorption and quasi-steady-state kinetic experiments. CBMs have previously been investigated in their free form in biochemical studies (Linder et al., 1995a, 1996; Carrard and Linder, 1999; Taylor et al., 2012; Guo et al., 2014), which has been instrumental for our current understanding of these modules. However, this approach provides no information on their influence on the catalytic activity and the interplay between CBM and CD/linker. In the present study, we address this by using the same CD and linker backbone (from TrCel6Awt) connected to different CBMs. The enzymes were expressed in the same expression host (A. oryzae), which provides the best fundament for functional comparison of the enzymes. We observed that the six investigated chimeras exhibit quite different cellulose affinity, with KP values differing 3 to 4-fold on each of the investigated substrates (Fig. 3). The enzymes had high Kp values on RAC (~2–8) and lower values on Avicel (~0.1–0.3) and BMCC (~0.3–0.9). This tendency has previously been observed for other cellulases and is probably related to the different natures of substrates (such as different surface areas). A slight trend was observed in the Kp values, since TrCel6Awt and TrCel6AVv had the highest Kp on all substrates and TrCel6APa and TrCel6ASi had the lowest, while the remaining (TrCel6ACcTrCel6ANf) fell between these two extremes (at least on Avicel and RAC). This indicates a correlation between the CBM and the affinity that is independent on the type cellulosic substrate. Hence, our data provide no indications on different targeting of the CBMs, at least not on the three substrate types used here. This might be related to the fact that all the CBMs are found on enzymes from the same GH family, which might target the same regions on the cellulose substrate. Several studies have demonstrated that the CBM affects the catalytic activity of cellulases (Ståhlberg et al., 1991; Kotiranta et al., 1999; Varnai et al., 2014; Sørensen et al., 2015). However, studies comparing the functionality of different CBMs from the family 1 are rare (Carrard and Linder, 1999; Fox et al., 2013; Guo and M Catchmark, 2013; Rahikainen et al., 2013). We have previously observed that the deletion of the CBM in TrCel6Awt results in a ~3-fold increase in convKM, while the Kp decreases about an order of magnitude (Christensen et al., 2019). This is in accordance with the idea that the CBM promotes the affinity to cellulose. All the chimeras investigated in this study had KP and convKM values that are more similar to those of the wild-type enzyme, compared with the aforementioned CBM-less TrCel6Awt (Christensen et al., 2019). This suggests that all the selected CBMs contribute measurably to the affinity despite their sequential and phylogenetic variance. However, our results show that the different modules have different effect on the enzyme catalysis. For example, the catalytic turnover (convVmax/E0) of the investigated enzymes ranges from 0.44 to 0.95 s−1. One of the chimeras (TrCel6ASi) stands out kinetically by having a low catalytic turnover number (convVmax/E0) and high invVmax/S0 compared with the remaining enzymes. We interpret this as a particularly good ability to locate attack sites on the cellulose surface and thus promote the reaction rate at high enzyme concentrations. This ability is reflected in the parameters ΓAttack (Table 1) giving the number of attack sites per gram substrate. We have earlier observed a more than 3-fold lower ΓAttack for a CBM-less TrCel6A variant (Christensen et al., 2019), and thus, the CBM is quite important for the ability to recognize attack sites on the substrate. For most of the enzymes investigated in the present study, the ability to recognize attack sites was nearly equal (i.e. similar ΓAttack values), despite divergence in the binding affinities. However, TrCel6ASi stands out by having a more than 2-fold higher ΓAttack than the remaining enzymes. This is especially interesting since this particular chimera had the lowest affinity (KP). This implies that the tendency to make non-productive binding is low for CBMSi, and thus, the enzyme can initiate hydrolysis from a higher amount of the binding sites compared with the other enzymes investigated here. We have earlier observed cellulases with high ΓAttack that have a low turnover number (convVmax/E0) (Badino et al., 2017; Kari et al., 2017; Christensen et al., 2019), which was also the case for TrCel6ASi. This may represent two different strategies where a slow catalytic rate can be compensated by a good ability to locate attack sites (Badino et al., 2017; Christensen et al., 2019). However, this advantage of TrCel6ASi on pure cellulose did not appear on a lignocellulosic substrate. Thus, initial measurements on PCS did not reveal significant differences between the chimeras. To interpret the discrepancies observed on pure cellulose, we note that the reaction rate depends on both the chemical steps of catalysis and diffusive processes, including complexation, processive sliding, and decomplexation. Earlier works on TrCel6A and TrCel7A have indicated that decomplexation is particularly slow and that the maximal turnover, convVmax/E0, is governed by the off-rate (Kurasin and Valjamae, 2011; Cruys-Bagger et al., 2013b; Christensen et al., 2018). Conversely, the on-rate and sliding rate for these enzymes have been reported to be fast (Cruys-Bagger et al., 2012; Knott et al., 2014), and thus less important for overall rate limitation. One consequence of this is that the maximal turnover of processive cellulases may be increased by engineering enzyme variants with lower substrate affinity (and hence higher off-rates) (Kari et al., 2014; Sørensen et al., 2017). Interestingly, this relationship between weaker binding and higher turnover does not appear for the current enzymes. The wild-type TrCel6A, for example, had the highest values of both substrate affinity (Fig. 3) and turnover (Table 1). This suggests that substrate-CBM interactions may promote affinity without slowing down dissociation, and its occurrence in the wild type may reflect that the enzyme has evolved a degree of inter-domain synergy as proposed earlier for Cel7A (Kont et al., 2016). The CBMs investigated in this study were selected based on low sequence identity (within this group of CBMs), and some sequences were selected because they had amino acids that were uncommon on certain positions (Fig. 1). Thus, the functional variations observed here may have multiple molecular origins. Despite the sequential difference between the investigated CBMs, the homology model in Supplementary Fig. S2 revealed that the overall folds were quite similar. The one exception was CBMSi that also had a few quite unique sequential characteristics such as additional aromatic amino acids (position 25 and 35) and additional prolines. This is interesting since CBMSi was functionally different as discussed above. However, the current work does not have structural information to elucidate molecular origins of this. All investigated CBMs had all three aromatic residues in the binding face (Linder et al., 1995b; Takashima et al., 2007), but in different combinations (although Y34 was conserved). It has earlier been demonstrated that the combination and order of the aromatic amino acids affect the cellulose affinity (Linder et al., 1995b; Takashima et al., 2007), which might explain some of the functional difference observed here. It has been suggested that tryptophan residues bind stronger to cellulose compared with tyrosine (Hoffren et al., 1995; Linder et al., 1995a). However, chimeras with two tryptophan residues in the CBM binding face (TrCel6APa, TrCel6ASi, and TrCel6ANf) showed quite low KP values compared with the remaining enzymes. Thus, we could not find evidence for a positive correlation between the number of tryptophan residues and the affinity. This might indicate that too many tryptophan residues in the binding face disturb the binding face arrangement because the tryptophan side chains are too large (Takashima et al., 2007). Another factor that may affect the cellulose interaction is the additional aromatic amino acids found in the CBMs. Thus, it has been discussed if the aromatic residue on position 15 can interact with cellulose (Nimlos et al., 2007; Payne et al., 2015). In this context, it is interesting to note that three of the investigated CBMs (CBMTr, CBMSi, and CBMVv) have an additional aromatic residue (position 25, 29, and 35, respectively). Among these, CBMVv and CBMTr showed the highest affinity, but the extra aromatic residue is located on the rough (non-binding) surface (according to the homology model, Supplementary Fig. S2) and may have structural rather than cellulose affinity effects. Three amino acid residues in the binding face (corresponding to Q9, N31, and Q36) have been proposed to interact with cellulose via hydrogen bonding (Hoffren et al., 1995; Linder et al., 1995b). Additionally, N14 (present in CBMTr and CBMNf) is exposed to the binding surface (see Supplementary Fig. S2) and might be involved in hydrogen bonding to the cellulose surface. Contrary, CBMSi lacks N31, which might affect the cellulose interaction, though the asparagine is replaced by another hydrogen binder (serine—see Supplementary Fig. S2). Other factors that are likely to affect the adsorption to cellulose include the rigidity of the module caused by disulfide bridges (Hoffren et al., 1995) (CBMTr, CBMPa, and CBMNf all have three bridges) and glycosylation pattern (Beckham et al., 2012; Taylor et al., 2012; Chen et al., 2014) (CBMTr has an N-glycosylation site, N14). However, we were not able to single out the relative importance of these effects on the basis of the current data. For native GH6 enzymes, the linker may be optimized for functional interplay between CBM and CD. This phenomenon has been called inter-domain synergy (Kont et al., 2016), and with respect to this, we note that the linkers from the native enzymes (from which the investigated CBMs originate) differ substantially both in length (41–76 residues) and amino compositions (see Supplementary Fig. S5). These discrepancies might indicate different functionalities of the linkers and perhaps reflect an optimized interplay between the linker and CBM for each enzyme. Thus, TrCel6Awt might have high affinity (Kp) and turnover (kcat) because the inter-domain synergism is optimized for the wild-type enzyme, which may be disturbed when the CBM is exchanged. We found that the ability to adsorb to cellulose and locate attack sites varies substantially between CBMs from different fungi that are found in quite different habitats ranging from decaying wood to animal rumens (see references (Orpin, 1975; Chang et al., 1982; Kües, 2000; Couturier et al., 2016; Druzhinina and Kubicek, 2016; Jiang et al., 2018) for details). We were however not able to correlate the function to the different habitats. Conclusion We have investigated the functional diversity of CBMs that is naturally connected to GH6 enzymes. The modules were combined with the model enzyme TrCel6Awt to investigate the function of CBM in interplay with the enzyme. Our results demonstrated that the phylogenetically diverse modules had different effects on the adsorption and kinetics of the enzyme. The binding parameter KP varied 4-fold between the investigated enzymes, while the catalytic turnover varied almost 2-fold. The wild-type enzyme TrCel6Awt (with its native CBM) showed the highest binding affinity for all tested substrates. This might indicate a particularly strong CBM-substrate interaction for CBMTr or an evolutionary optimized interplay between the native CBM and the CD/linker (inter-domain synergism (Kont et al., 2016)). The ability to locate attack sites on the cellulose surface was similar for most of the investigated enzymes. However, the chimera TrCel6ASi was more effective in locating attack sites and had a ΓAttack that was ~2-fold higher than the remaining enzymes, though this enzyme had the lowest catalytic turnover. In conclusion, our results show that different CBMs contribute quite differently to the adsorption and catalysis of GH6 CBHs. In fact, the functional difference between the chimeras investigated here was in some cases comparable to the difference observed earlier for different GH6 wild-type enzymes (Christensen et al., 2019). We therefore suggest that the diversity of the CBMs plays an important role for the functional differences between GH6 wild-type enzymes from different cellulose degrading fungi. Supplementary data Supplementary data are available at PEDS online. Funding This work was supported by the Innovation Fund Denmark [grant number 5150-00020B], Carlsbergfondet [grant number 2013-01-0208], and the Novo Nordisk Foundation [grant numbers NNF15OC0016606 and NNFSA170028392]. 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TI - Functional analysis of chimeric TrCel6A enzymes with different carbohydrate binding modules
JF - Protein Engineering, Design and Selection
DO - 10.1093/protein/gzaa003
DA - 2019-12-31
UR - https://www.deepdyve.com/lp/oxford-university-press/functional-analysis-of-chimeric-trcel6a-enzymes-with-different-V0nOA5uiWD
DP - DeepDyve
ER -