The Mechanism of Cellulose Hydrolysis by a Two-Step, Retaining Cellobiohydrolase Elucidated by Structural and Transition Path Sampling Studies

A detailed sensitivity analysis identifies the key factors influencing the enzymatic saccharification of lignocellulosic biomass

Corn stover is the most abundant form of crop residue that can serve as a source of lignocellulosic biomass in biorefinery approaches, for instance for the production of bioethanol. In such biorefinery processes, the constituent polysaccharide biopolymers are typically broken down into simple monomeric sugars by enzymatic saccharification, for further downstream fermentation into bioethanol. However, the recalcitrance of this material to enzymatic saccharification invokes the need for innovative pre-treatment methods to increase sugar conversion yield. Here, we focus on experimental glucose conversion time-courses for corn stover lignocellulose that has been pre-treated with different acid-catalysed processes and intensities. We identify the key parameters that determine enzymatic saccharification dynamics by performing a Sobol's sensitivity analysis on the comparison between the simulation results from our complex stochastic biophysical model, and the experimental data that we accurately reproduce. We find that the parameters relating to cellulose crystallinity and those associated with the cellobiohydrolase activity are predominantly driving the enzymatic saccharification dynamics. We confirm our computational results using mathematical calculations for a purely cellulosic substrate. On the one hand, having identified that only five parameters drastically influence the saccharification dynamics allows us to reduce the dimensionality of the parameter space (from nineteen to five parameters), which we expect will significantly speed up our fitting algorithm for comparison of experimental and simulated saccharification time-courses. On the other hand, these parameters directly highlight key targets for experimental endeavours in the optimisation of pre-treatment and saccharification conditions. Finally, this systematic and two-fold theoretical study, based on both mathematical and computational approaches, provides experimentalists with key insights that will support them in rationalising their complex experimental results.

Elucidation of the mechanism underlying the sequential catalysis of inulin by fructotransferase

Glycoside hydrolase family 91 (GH91) inulin fructotransferase (IFTases) enables biotransformation of fructans into sugar substitutes for dietary intervention in metabolic syndrome. However, the catalytic mechanism underlying the sequential biodegradation of inulin remains unelusive during the biotranformation of fructans. Herein we present the crystal structures of IFTase from Arthrobacter aurescens SK 8.001 in apo form and in complexes with kestose, nystose, or fructosyl nystose, respectively. Two kinds of conserved noncatalytic binding regions are first identified for IFTase-inulin interactions. The conserved interactions of substrates were revealed in the catalytic center that only contained a catalytic residue E205. A switching scaffold was comprised of D194 and Q217 in the catalytic channel, which served as the catalytic transition stabilizer through side chain displacement in the cycling of substrate sliding in/out the catalytic pocket. Such features in GH91 contribute to the catalytic model for consecutive cutting of substrate chain as well as product release in IFTase, and thus might be extended to other exo-active enzymes with an enclosed bottom of catalytic pocket. The study expands the current general catalytic principle in enzyme-substrate complexes and shed light on the rational design of IFTase for fructan biotransformation.

An enzymatic hydrolysis-based platform technology for the efficient high-yield production of cellulose nanospheres

This study evaluates the feasibility of using enzymatic technology to produce novel nanostructures of cellulose nanomaterials, specifically cellulose nanospheres (CNS), through enzymatic hydrolysis with endoglucanase and xylanase of pre-treated cellulose fibers. A statistical experimental design facilitated a comprehensive understanding of the process parameters, which enabled high yields of up to 82.7 %, while maintaining a uniform diameter of 54 nm and slightly improved crystallinity and thermal stability. Atomic force microscopy analyses revealed a distinct CNS formation mechanism, where initial fragmentation of rod-like nanoparticles and subsequent self-assembly of shorter rod-shaped nanoparticles led to CNS formation. Additionally, adjustments in process parameters allowed precise control over the CNS diameter, ranging from 20 to 100 nm, highlighting the potential for customization in high-performance applications. Furthermore, this study demonstrates how the process framework, originally developed for cellulose nanocrystals (CNC) production, was successfully adapted and optimized for CNS production, ensuring scalability and efficiency. In conclusion, this study emphasizes the versatility and efficiency of the enzyme-based platform for producing high-quality CNS, providing valuable insights into energy consumption for large-scale economic and environmental assessments.

Bioethanol Production from Characterized Pre-treated Sugarcane Trash and Jatropha Agrowastes

Low production costs and a potential feedstock supply make lignocellulosic ethanol (bioethanol) an important source of advanced biofuels. The physical and chemical preparation of this kind of lignocellulosic feedstock led to a high ethanol yield. In order to increase the yield of fermentable sugars, pretreatment is an essential process step that alters the lignocellulosic structure and improves its accessibility for the expensive hydrolytic enzymes. In this context, the chemical composition of sugarcane trash (dry leaves, green leaves, and tops) and jatropha (shell and seed cake) was determined to be mainly cellulose, hemicellulose, and lignin. Hydrogen peroxide and sodium hydroxide were applied in an attempt to facilitate the solubilization of lignin and hemicelluloses in five agrowastes. The extraction of hydrogen peroxide was much better than that of sodium hydroxide. A comparative study was done using SEM, EDXA, and FTIR to evaluate the difference between the two methods. The pretreated wastes were subjected to saccharification by commercial cellulases (30 IU/g substrate). The obtained glucose was fortified with nutrients and fermented statically by Saccharomyces cerevisiae F-307 for bioethanol production. The results revealed the bioethanol yields were 325.4, 310.8, 282.9, 302.4 and 264.0 mg ethanol/g treated agrowastes from green leaves of sugarcane, jatropha deolied seed cake, tops sugarcane, dry leaves of sugarcane, and jatropha shell, respectively. This study emphasizes the value of lignocellulosic agricultural waste as a resource for the production of biofuels as well as the significance of the extraction process.

Single-molecule tracking reveals dual front door/back door inhibition of Cel7A cellulase by its product cellobiose

Degrading cellulose is a key step in the processing of lignocellulosic biomass into bioethanol. Cellobiose, the disaccharide product of cellulose degradation, has been shown to inhibit cellulase activity, but the mechanisms underlying product inhibition are not clear. We combined single-molecule imaging and biochemical investigations with the goal of revealing the mechanism by which cellobiose inhibits the activity of Trichoderma reesei Cel7A, a well-characterized exo-cellulase. We find that cellobiose slows the processive velocity of Cel7A and shortens the distance moved per encounter; effects that can be explained by cellobiose binding to the product release site of the enzyme. Cellobiose also strongly inhibits the binding of Cel7A to immobilized cellulose, with a Ki of 2.1 mM. The isolated catalytic domain (CD) of Cel7A was also inhibited to a similar degree by cellobiose, and binding of an isolated carbohydrate-binding module to cellulose was not inhibited by cellobiose, suggesting that cellobiose acts on the CD alone. Finally, cellopentaose inhibited Cel7A binding at micromolar concentrations without affecting the enzyme's velocity of movement along cellulose. Together, these results suggest that cellobiose inhibits Cel7A activity both by binding to the "back door" product release site to slow activity and to the "front door" substrate-binding tunnel to inhibit interaction with cellulose. These findings point to strategies for engineering cellulases to reduce product inhibition and enhance cellulose degradation, supporting the growth of a sustainable bioeconomy.

Engineering of glycoside hydrolase family 7 cellobiohydrolases directed by natural diversity screening

Protein engineering and screening of processive fungal cellobiohydrolases (CBHs) remain challenging due to limited expression hosts, synergy-dependency, and recalcitrant substrates. In particular, glycoside hydrolase family 7 (GH7) CBHs are critically important for the bioeconomy and typically difficult to engineer. Here, we target the discovery of highly active natural GH7 CBHs and engineering of variants with improved activity. Using experimentally assayed activities of genome mined CBHs, we applied sequence and structural alignments to top performers to identify key point mutations linked to improved activity. From ∼1500 known GH7 sequences, an evolutionarily diverse subset of 57 GH7 CBH genes was expressed in Trichoderma reesei and screened using a multiplexed activity screening assay. Ten catalytically enhanced natural variants were identified, produced, purified, and tested for efficacy using industrially relevant conditions and substrates. Three key amino acids in CBHs with performance comparable or superior to Penicillium funiculosum Cel7A were identified and combinatorially engineered into P. funiculosum cel7a, expressed in T. reesei, and assayed on lignocellulosic biomass. The top performer generated using this combined approach of natural diversity genome mining, experimental assays, and computational modeling produced a 41% increase in conversion extent over native P. funiculosum Cel7A, a 55% increase over the current industrial standard T. reesei Cel7A, and 10% improvement over Aspergillus oryzae Cel7C, the best natural GH7 CBH previously identified in our laboratory.

Molecular Free Energies, Rates, and Mechanisms from Data-Efficient Path Sampling Simulations

Molecular dynamics is a powerful tool for studying the thermodynamics and kinetics of complex molecular events. However, these simulations can rarely sample the required time scales in practice. Transition path sampling overcomes this limitation by collecting unbiased trajectories and capturing the relevant events. Moreover, the integration of machine learning can boost the sampling while simultaneously learning a quantitative representation of the mechanism. Still, the resulting trajectories are by construction non-Boltzmann-distributed, preventing the calculation of free energies and rates. We developed an algorithm to approximate the equilibrium path ensemble from machine-learning-guided path sampling data. At the same time, our algorithm provides efficient sampling, mechanism, free energy, and rates of rare molecular events at a very moderate computational cost. We tested the method on the folding of the mini-protein chignolin. Our algorithm is straightforward and data-efficient, opening the door to applications in many challenging molecular systems.

PREDIG: Web application to model and predict the enzymatic saccharification of plant cell wall

Enzymatic digestion of lignocellulosic plant biomass is a key step in bio-refinery approaches for the production of biofuels and other valuable chemicals. However, the recalcitrance of this material in conjunction with its variability and heterogeneity strongly hampers the economic viability and profitability of biofuel production. To complement both academic and industrial experimental research in the field, we designed an advanced web application that encapsulates our in-house developed complex biophysical model of enzymatic plant cell wall degradation. PREDIG (https://predig.cs.hhu.de/) is a user-friendly, free, and fully open-source web application that allows the user to perform in silico experiments. Specifically, it uses a Gillespie algorithm to run stochastic simulations of the enzymatic saccharification of a lignocellulose microfibril, at the mesoscale, in three dimensions. Such simulations can for instance be used to test the action of distinct enzyme cocktails on the substrate. Additionally, PREDIG can fit the model parameters to uploaded experimental time-course data, thereby returning values that are intrinsically difficult to measure experimentally. This gives the user the possibility to learn which factors quantitatively explain the recalcitrance to saccharification of their specific biomass material.

An exploration of machine learning models for the determination of reaction coordinates associated with conformational transitions

Determining collective variables (CVs) for conformational transitions is crucial to understanding their dynamics and targeting them in enhanced sampling simulations. Often, CVs are proposed based on intuition or prior knowledge of a system. However, the problem of systematically determining a proper reaction coordinate (RC) for a specific process in terms of a set of putative CVs can be achieved using committor analysis (CA). Identifying essential degrees of freedom that govern such transitions using CA remains elusive because of the high dimensionality of the conformational space. Various schemes exist to leverage the power of machine learning (ML) to extract an RC from CA. Here, we extend these studies and compare the ability of 17 different ML schemes to identify accurate RCs associated with conformational transitions. We tested these methods on an alanine dipeptide in vacuum and on a sarcosine dipeptoid in an implicit solvent. Our comparison revealed that the light gradient boosting machine method outperforms other methods. In order to extract key features from the models, we employed Shapley Additive exPlanations analysis and compared its interpretation with the "feature importance" approach. For the alanine dipeptide, our methodology identifies ϕ and θ dihedrals as essential degrees of freedom in the C7ax to C7eq transition. For the sarcosine dipeptoid system, the dihedrals ψ and ω are the most important for the cisαD to transαD transition. We further argue that analysis of the full dynamical pathway, and not just endpoint states, is essential for identifying key degrees of freedom governing transitions.

The interplay between lytic polysaccharide monooxygenases and glycoside hydrolases

In nature, enzymatic degradation of recalcitrant polysaccharides such as chitin and cellulose takes place by a synergistic interaction between glycoside hydrolases (GHs) and lytic polysaccharide monooxygenases (LPMOs). The two different families of carbohydrate-active enzymes use two different mechanisms when breaking glycosidic bonds between sugar moieties. GHs employ a hydrolytic activity and LPMOs are oxidative. Consequently, the topologies of the active sites differ dramatically. GHs have tunnels or clefts lined with a sheet of aromatic amino acid residues accommodating single polymer chains being threaded into the active site. LPMOs are adapted to bind to the flat crystalline surfaces of chitin and cellulose. It is believed that the LPMO oxidative mechanism provides new chain ends that the GHs can attach to and degrade, often in a processive manner. Indeed, there are many reports of synergies as well as rate enhancements when LPMOs are applied in concert with GHs. Still, these enhancements vary in magnitude with respect to the nature of the GH and the LPMO. Moreover, impediment of GH catalysis is also observed. In the present review, we discuss central works where the interplay between LPMOs and GHs has been studied and comment on future challenges to be addressed to fully use the potential of this interplay to improve enzymatic polysaccharide degradation.

Engineering cellulases for conversion of lignocellulosic biomass

Lignocellulosic biomass is a renewable source of energy, chemicals and materials. Many applications of this resource require the depolymerization of one or more of its polymeric constituents. Efficient enzymatic depolymerization of cellulose to glucose by cellulases and accessory enzymes such as lytic polysaccharide monooxygenases is a prerequisite for economically viable exploitation of this biomass. Microbes produce a remarkably diverse range of cellulases, which consist of glycoside hydrolase (GH) catalytic domains and, although not in all cases, substrate-binding carbohydrate-binding modules (CBMs). As enzymes are a considerable cost factor, there is great interest in finding or engineering improved and robust cellulases, with higher activity and stability, easy expression, and minimal product inhibition. This review addresses relevant engineering targets for cellulases, discusses a few notable cellulase engineering studies of the past decades and provides an overview of recent work in the field.

Enzyme kinetics by GH7 cellobiohydrolases on chromogenic substrates is dictated by non-productive binding: insights from crystal structures and MD simulation

Cellobiohydrolases (CBHs) in the glycoside hydrolase family 7 (GH7) (EC3.2.1.176) are the major cellulose degrading enzymes both in industrial settings and in the context of carbon cycling in nature. Small carbohydrate conjugates such as p-nitrophenyl-β-d-cellobioside (pNPC), p-nitrophenyl-β-d-lactoside (pNPL) and methylumbelliferyl-β-d-cellobioside have commonly been used in colorimetric and fluorometric assays for analysing activity of these enzymes. Despite the similar nature of these compounds the kinetics of their enzymatic hydrolysis vary greatly between the different compounds as well as among different enzymes within the GH7 family. Through enzyme kinetics, crystallographic structure determination, molecular dynamics simulations, and fluorometric binding studies using the closely related compound o-nitrophenyl-β-d-cellobioside (oNPC), in this work we examine the different hydrolysis characteristics of these compounds on two model enzymes of this class, TrCel7A from Trichoderma reesei and PcCel7D from Phanerochaete chrysosporium. Protein crystal structures of the E212Q mutant of TrCel7A with pNPC and pNPL, and the wildtype TrCel7A with oNPC, reveal that non-productive binding at the product site is the dominating binding mode for these compounds. Enzyme kinetics results suggest the strength of non-productive binding is a key determinant for the activity characteristics on these substrates, with PcCel7D consistently showing higher turnover rates (k_cat ) than TrCel7A, but higher Michaelis-Menten (K_M ) constants as well. Furthermore, oNPC turned out to be useful as an active-site probe for fluorometric determination of the dissociation constant for cellobiose on TrCel7A but could not be utilized for the same purpose on PcCel7D, likely due to strong binding to an unknown site outside the active site.

Perspective: Path Sampling Methods Applied to Enzymatic Catalysis

This Perspective reviews the use of Transition Path Sampling methods to study enzymatically catalyzed chemical reactions. First applied by our group to an enzymatic reaction over 15 years ago, the method has uncovered basic principles in enzymatic catalysis such as the protein promoting vibration, and it has also helped harmonize such ideas as electrostatic preorganization with dynamic views of enzyme function. It is now being used to help uncover principles of protein design necessary to artificial enzyme creation.

Enzyme Synergy in Transient Clusters of Endo- and Exocellulase Enables a Multilayer Mode of Processive Depolymerization of Cellulose

Biological degradation of cellulosic materials relies on the molecular-mechanistic principle that internally chain-cleaving endocellulases work synergistically with chain end-cleaving exocellulases in polysaccharide chain depolymerization. How endo-exo synergy becomes effective in the deconstruction of a solid substrate that presents cellulose chains assembled into crystalline material is an open question of the mechanism, with immediate implications on the bioconversion efficiency of cellulases. Here, based on single-molecule evidence from real-time atomic force microscopy, we discover that endo- and exocellulases engage in the formation of transient clusters of typically three to four enzymes at the cellulose surface. The clusters form specifically at regular domains of crystalline cellulose microfibrils that feature molecular defects in the polysaccharide chain organization. The dynamics of cluster formation correlates with substrate degradation through a multilayer-processive mode of chain depolymerization, overall leading to the directed ablation of single microfibrils from the cellulose surface. Each multilayer-processive step involves the spatiotemporally coordinated and mechanistically concerted activity of the endo- and exocellulases in close proximity. Mechanistically, the cooperativity with the endocellulase enables the exocellulase to pass through its processive cycles ∼100-fold faster than when acting alone. Our results suggest an advanced paradigm of efficient multienzymatic degradation of structurally organized polymer materials by endo-exo synergetic chain depolymerization.

Multifaceted Computational Modeling in Glycoscience

Glycoscience assembles all the scientific disciplines involved in studying various molecules and macromolecules containing carbohydrates and complex glycans. Such an ensemble involves one of the most extensive sets of molecules in quantity and occurrence since they occur in all microorganisms and higher organisms. Once the compositions and sequences of these molecules are established, the determination of their three-dimensional structural and dynamical features is a step toward understanding the molecular basis underlying their properties and functions. The range of the relevant computational methods capable of addressing such issues is anchored by the specificity of stereoelectronic effects from quantum chemistry to mesoscale modeling throughout molecular dynamics and mechanics and coarse-grained and docking calculations. The Review leads the reader through the detailed presentations of the applications of computational modeling. The illustrations cover carbohydrate-carbohydrate interactions, glycolipids, and N- and O-linked glycans, emphasizing their role in SARS-CoV-2. The presentation continues with the structure of polysaccharides in solution and solid-state and lipopolysaccharides in membranes. The full range of protein-carbohydrate interactions is presented, as exemplified by carbohydrate-active enzymes, transporters, lectins, antibodies, and glycosaminoglycan binding proteins. A final section features a list of 150 tools and databases to help address the many issues of structural glycobioinformatics.

Time-Lagged Flux in the Transition Path Ensemble: Flux Maximization and Relation to Transition Path Theory

The transition path ensemble is of special interest in reaction coordinate identification as it consists of reactive trajectories that start from the reactant state and end in the product one. As a theoretical framework for describing the transition path ensemble, the transition path theory has been introduced more than 10 years ago, and so far, its applications have only been illustrated in several low-dimensional systems. Given the transition path ensemble, expressions for calculating flux, current (a vector field), and principal curves are derived here in the space of collective variables from the transition path theory, and they are applicable to time series obtained from molecular dynamics simulations of high-dimensional systems, i.e., the position coordinates as a function of time in the transition path ensemble. The connection of the transition path theory is made to a density-weighted average flux, a quantity proposed in a previous work to appraise the relevance of a coordinate to the reaction coordinate [Li, W. J. Chem. Phys. 2022, 156, 054117]. Most importantly, as an extension of the existing quantities, time-lagged quantities such as flux and current are also proposed. The main insights and objects provided by these time-lagged quantities are illustrated in the application to the alanine peptide in vacuum.

QM/MM investigation of the catalytic mechanism of processive endoglucanase Cel9G from Clostridium cellulovorans

Carbohydrate degradation catalyzed by glucoside hydrolases (GHs) is a major mechanism in biomass conversion. GH family 9 endoglucanase (Cel9G) from Clostridium cellulovorans, a typical multimodular enzyme, contains a catalytic domain closely linked to a family 3c carbohydrate-binding module (CBM3c). Unlike the conventional behavior proposed for other carbohydrate-binding modules, CBM3c has a direct impact on catalytic activity. In this work, extensive molecular dynamics (MD) simulations were employed to clarify the functional role of CBM3c. Furthermore, the detailed catalytic mechanism of Cel9G was investigated at the atomistic level using the combined quantum mechanical and molecular mechanical (QM/MM) method. Based on these simulations, owing to the rigidity of the peptide linker, CBM3c may affect the enzymatic activity via direct interactions with alpha helix 4 of GH9, especially with the K123 and H125 residues. In addition, using cellohexaose as a substrate, the QM/MM MD simulations confirmed that this enzyme can cleave the β-1,4-glycosidic linkage via an inverting mechanism. An oxocarbenium ion-like transition state was located with a barrier height of 19.6 kcal mol^-1. Furthermore, the G(-1) pyranose unit preferentially adopted a distorted ¹S₅/⁴H₅ conformer in the enzyme-substrate complex. For the cleavage of the glycosidic bond, we were able to identify a plausible route (¹S₅/⁴H₅ → [⁴H₅/⁴E]^# → ⁴C₁) from the reactant to the product at the G(-1) site.

A Case Study of the Glycoside Hydrolase Enzyme Mechanism Using an Automated QM-Cluster Model Building Toolkit

Glycoside hydrolase enzymes are important for hydrolyzing the β-1,4 glycosidic bond in polysaccharides for deconstruction of carbohydrates. The two-step retaining reaction mechanism of Glycoside Hydrolase Family 7 (GH7) was explored with different sized QM-cluster models built by the Residue Interaction Network ResidUe Selector (RINRUS) software using both the wild-type protein and its E217Q mutant. The first step is the glycosylation, in which the acidic residue 217 donates a proton to the glycosidic oxygen leading to bond cleavage. In the subsequent deglycosylation step, one water molecule migrates into the active site and attacks the anomeric carbon. Residue interaction-based QM-cluster models lead to reliable structural and energetic results for proposed glycoside hydrolase mechanisms. The free energies of activation for glycosylation in the largest QM-cluster models were predicted to be 19.5 and 31.4 kcal mol⁻¹ for the wild-type protein and its E217Q mutant, which agree with experimental trends that mutation of the acidic residue Glu217 to Gln will slow down the reaction; and are higher in free energy than the deglycosylation transition states (13.8 and 25.5 kcal mol⁻¹ for the wild-type protein and its mutant, respectively). For the mutated protein, glycosylation led to a low-energy product. This thermodynamic sink may correspond to the intermediate state which was isolated in the X-ray crystal structure. Hence, the glycosylation is validated to be the rate-limiting step in both the wild-type and mutated enzyme.

Stochastic model of lignocellulosic material saccharification

The processing of agricultural wastes towards extraction of renewable resources is recently being considered as a promising alternative to conventional biofuel production. The degradation of agricultural residues is a complex chemical process that is currently time intensive and costly. Various pre-treatment methods are being investigated to determine the subsequent modification of the material and the main obstacles in increasing the enzymatic saccharification. In this study, we present a computational model that complements the experimental approaches. We decipher how the three-dimensional structure of the substrate impacts the saccharification dynamics. We model a cell wall microfibril composed of cellulose and surrounded by hemicellulose and lignin, with various relative abundances and arrangements. This substrate is subjected to digestion by different cocktails of well characterized enzymes. The saccharification dynamics is simulated in silico using a stochastic procedure based on a Gillespie algorithm. As we additionally implement a fitting procedure that optimizes the parameters of the simulation runs, we are able to reproduce experimental saccharification time courses for corn stover. Our model highlights the synergistic action of enzymes, and confirms the linear decrease of sugar conversion when either lignin content or crystallinity of the substrate increases. Importantly, we show that considering the crystallinity of cellulose in addition to the substrate composition is essential to interpret experimental saccharification data. Finally, our findings support the hypothesis of xylan being partially crystalline.

Leftover wastes generated by agriculture, such as inedible leaves and stalks of plants, represent an abundant and unexploited raw material that contains energy in the form of sugar polymers. Their breakdown and processing into bio-ethanol is recently being considered as a promising candidate for renewable fuel production. However, it is still poorly understood, how the microscopic structure and composition of plant waste materials impact their enzymatic digestion. Various experimental pre-processing methods are currently being tested to determine their effect on the material composition and structure, and the sugar conversion. In this study, we present a computational model to complement such experimental approaches. We simulate a microscopic plant fragment typically found in plant waste materials, whose structure and composition can be tailored. This fragment is then subjected to enzymatic digestion, whose dynamics is tracked in silico. The model reproduces experimentally observed time courses for plant fragments of known composition. It additionally provides new hypotheses for interpreting complex experimental results.

Machine learning reveals sequence-function relationships in family 7 glycoside hydrolases

Family 7 glycoside hydrolases (GH7) are among the principal enzymes for cellulose degradation in nature and industrially. These enzymes are often bimodular, including a catalytic domain and carbohydrate-binding module (CBM) attached via a flexible linker, and exhibit an active site that binds cello-oligomers of up to ten glucosyl moieties. GH7 cellulases consist of two major subtypes: cellobiohydrolases (CBH) and endoglucanases (EG). Despite the critical importance of GH7 enzymes, there remain gaps in our understanding of how GH7 sequence and structure relate to function. Here, we employed machine learning to gain data-driven insights into relationships between sequence, structure, and function across the GH7 family. Machine-learning models, trained only on the number of residues in the active-site loops as features, were able to discriminate GH7 CBHs and EGs with up to 99% accuracy, demonstrating that the lengths of loops A4, B2, B3, and B4 strongly correlate with functional subtype across the GH7 family. Classification rules were derived such that specific residues at 42 different sequence positions each predicted the functional subtype with accuracies surpassing 87%. A random forest model trained on residues at 19 positions in the catalytic domain predicted the presence of a CBM with 89.5% accuracy. Our machine learning results recapitulate, as top-performing features, a substantial number of the sequence positions determined by previous experimental studies to play vital roles in GH7 activity. We surmise that the yet-to-be-explored sequence positions among the top-performing features also contribute to GH7 functional variation and may be exploited to understand and manipulate function.

Physical constraints and functional plasticity of cellulases

Enzyme reactions, both in Nature and technical applications, commonly occur at the interface of immiscible phases. Nevertheless, stringent descriptions of interfacial enzyme catalysis remain sparse, and this is partly due to a shortage of coherent experimental data to guide and assess such work. In this work, we produced and kinetically characterized 83 cellulases, which revealed a conspicuous linear free energy relationship (LFER) between the substrate binding strength and the activation barrier. The scaling occurred despite the investigated enzymes being structurally and mechanistically diverse. We suggest that the scaling reflects basic physical restrictions of the hydrolytic process and that evolutionary selection has condensed cellulase phenotypes near the line. One consequence of the LFER is that the activity of a cellulase can be estimated from its substrate binding strength, irrespectively of structural and mechanistic details, and this appears promising for in silico selection and design within this industrially important group of enzymes.

Computational modeling of carbohydrate processing enzymes reactions

Carbohydrate processing enzymes are of biocatalytic interest. Glycoside hydrolases and the recently discovered lytic polysaccharide monooxygenase for their use in biomass degradation to obtain biofuels or valued chemical entities. Glycosyltransferases or engineered glycosidases and phosphorylases for the synthesis of carbohydrates and glycosylated products. Quantum mechanics-molecular mechanics (QM/MM) methods are highly contributing to establish their different chemical reaction mechanisms. Other computational methods are also used to study enzyme conformational changes, ligand pathways, and processivity, e.g. for processive glycosidases like cellobiohydrolases. There is still a long road to travel to fully understand the role of conformational dynamics in enzyme activity and also to disclose the variety of reaction mechanisms these enzymes employ. Additionally, computational tools for enzyme engineering are beginning to be applied to evaluate substrate specificity or aid in the design of new biocatalysts with increased thermostability or tailored activity, a growing field where molecular modeling is finding its way.

Paper Ageing: The Effect of Paper Chemical Composition on Hydrolysis and Oxidation

The degradation of cellulose is an important factor influencing its mechanical, optical, physical, and chemical properties and, hence, the lifetime of paper in libraries and archival collections. Regardless of the complexity of the paper material, the main chemical pathways for its degradation are hydrolysis and oxidation. This study presents an overview of the analytical techniques employed in the evaluation of the hydrolysis and oxidation processes; these techniques include size-exclusion chromatography, Fourier-transform infrared and ultraviolet–visible spectroscopy, and X-ray diffraction. This paper aims to determine the extent to which these instrumental methods are useful for studying the aforementioned processes and for which lignin contents. It also highlights how atmospheric humidity could affect the cellulose structure in paper containing lignin. It was found that humidity causes significant changes in the cellulose chain lengths and that a high lignin content in paper could suppress some cellulose degradation pathways. This knowledge can be applied to developing strategies and selective chemical treatments preventing the consequences of paper ageing.

QM/MM Simulations of Enzymatic Hydrolysis of Cellulose: Probing the Viability of an Endocyclic Mechanism for an Inverting Cellulase

Glycoside hydrolases (GH) cleave carbohydrate glycosidic bonds and play pivotal roles in living organisms and in many industrial processes. Unlike acid-catalyzed hydrolysis of carbohydrates in solution, which can occur either via cyclic or acyclic oxocarbenium-like transition states, it is widely accepted that GH-catalyzed hydrolysis proceeds via a general acid mechanism involving a cyclic oxocarbenium-like transition state with protonation of the glycosidic oxygen. The GH45 subfamily C inverting endoglucanase from Phanerochaete chrysosporium (PcCel45A) defies the classical inverting mechanism as its crystal structure conspicuously lacks a general Asp or Glu base residue. Instead, PcCel45A has an Asn residue, a notoriously weak base in solution, as one of its catalytic residues at position 92. Moreover, unlike other inverting GHs, the relative position of the catalytic residues in PcCel45A impairs the proton abstraction from the nucleophilic water that attacks the anomeric carbon, a key step in the classical mechanism. Here, we investigate the viability of an endocyclic mechanism for PcCel45A using hybrid quantum mechanics/molecular mechanics (QM/MM) simulations, with the QM region treated with the self-consistent-charge density-functional tight-binding level of theory. In this mechanism, an acyclic oxocarbenium-like transition state is stabilized leading to the opening of the glucopyranose ring and formation of an unstable acyclic hemiacetal that can be readily decomposed into hydrolysis product. In silico characterization of the Michaelis complex shows that PcCel45A significantly restrains the sugar ring to the ⁴C₁ chair conformation at the −1 subsite of the substrate binding cleft, in contrast to the classical exocyclic mechanism in which ring puckering is critical. We also show that PcCel45A provides an environment where the catalytic Asn92 residue in its standard amide form participates in a cooperative hydrogen bond network resulting in its increased nucleophilicity due to an increased negative charge on the oxygen atom. Our results for PcCel45A suggest that carbohydrate hydrolysis catalyzed by GHs may take an alternative route from the classical mechanism.

Transition Path Sampling Study of the Feruloyl Esterase Mechanism

Serine hydrolases cleave peptide and ester bonds and are ubiquitous in nature, with applications in biotechnology, in materials, and as drug targets. The serine hydrolase two-step mechanism employs a serine-histidine-aspartate/glutamate catalytic triad, where the histidine residue acts as a base to activate poor nucleophiles (a serine residue or a water molecule) and as an acid to allow the dissociation of poor leaving groups. This mechanism has been the subject of debate regarding how histidine shuttles the proton from the nucleophile to the leaving group. To elucidate the reaction mechanism of serine hydrolases, we employ quantum mechanics/molecular mechanics-based transition path sampling to obtain the reaction coordinate using the Aspergillus niger feruloyl esterase A (AnFaeA) as a model enzyme. The optimal reaction coordinates include terms involving nucleophilic attack on the carbonyl carbon and proton transfer to, and dissociation of, the leaving group. During the reaction, the histidine residue undergoes a reorientation on the time scale of hundreds of femtoseconds that supports the "moving histidine" mechanism, thus calling into question the "ring flip" mechanism. We find a concerted mechanism, where the transition state coincides with the tetrahedral intermediate with the histidine residue pointed between the nucleophile and the leaving group. Moreover, motions of the catalytic aspartate toward the histidine occur concertedly with proton abstraction by the catalytic histidine and help stabilize the transition state, thus partially explaining how serine hydrolases enable poor nucleophiles to attack the substrate carbonyl carbon. Rate calculations indicate that the second step (deacylation) is rate-determining, with a calculated rate constant of 66 s^-1. Overall, these results reveal the pivotal role of active-site dynamics in the catalytic mechanism of AnFaeA, which is likely similar in other serine hydrolases.

Molecular origins of reduced activity and binding commitment of processive cellulases and associated carbohydrate-binding proteins to cellulose III

Efficient enzymatic saccharification of cellulosic biomass into fermentable sugars can enable production of bioproducts like ethanol. Native crystalline cellulose, or cellulose I, is inefficiently processed via enzymatic hydrolysis but can be converted into the structurally distinct cellulose III allomorph that is processed via cellulase cocktails derived from Trichoderma reesei up to 20-fold faster. However, characterization of individual cellulases from T. reesei, like the processive exocellulase Cel7A, shows reduced binding and activity at low enzyme loadings toward cellulose III. To clarify this discrepancy, we monitored the single-molecule initial binding commitment and subsequent processive motility of Cel7A enzymes and associated carbohydrate-binding modules (CBMs) on cellulose using optical tweezers force spectroscopy. We confirmed a 48% lower initial binding commitment and 32% slower processive motility of Cel7A on cellulose III, which we hypothesized derives from reduced binding affinity of the Cel7A binding domain CBM1. Classical CBM–cellulose pull-down assays, depending on the adsorption model fitted, predicted between 1.2- and 7-fold reduction in CBM1 binding affinity for cellulose III. Force spectroscopy measurements of CBM1–cellulose interactions, along with molecular dynamics simulations, indicated that previous interpretations of classical binding assay results using multisite adsorption models may have complicated analysis, and instead suggest simpler single-site models should be used. These findings were corroborated by binding analysis of other type-A CBMs (CBM2a, CBM3a, CBM5, CBM10, and CBM64) on both cellulose allomorphs. Finally, we discuss how complementary analytical tools are critical to gain insight into the complex mechanisms of insoluble polysaccharides hydrolysis by cellulolytic enzymes and associated carbohydrate-binding proteins.

Computing Cellulase Kinetics with a Two-Domain Linear Interaction Energy Approach

While heterogeneous enzyme reactions play an essential role in both nature and green industries, computational predictions of their catalytic properties remain scarce. Recent experimental work demonstrated the applicability of the Sabatier principle for heterogeneous biocatalysis. This provides a simple relationship between binding strength and the catalytic rate and potentially opens a new way for inexpensive computational determination of kinetic parameters. However, broader implementation of this approach will require fast and reliable prediction of binding free energies of complex two-phase systems, and computational procedures for this are still elusive. Here, we propose a new framework for the assessment of the binding strengths of multidomain proteins, in general, and interfacial enzymes, in particular, based on an extended linear interaction energy (LIE) method. This two-domain LIE (2D-LIE) approach was successfully applied to predict binding and activation free energies of a diverse set of cellulases and resulted in robust models with high accuracy. Overall, our method provides a fast computational screening tool for cellulases that have not been experimentally characterized, and we posit that it may also be applicable to other heterogeneously acting biocatalysts.

Reduced type-A carbohydrate-binding module interactions to cellulose I leads to improved endocellulase activity

Dissociation of nonproductively bound cellulolytic enzymes from cellulose is hypothesized to be a key rate-limiting factor impeding cost-effective biomass conversion to fermentable sugars. However, the role of carbohydrate-binding modules (CBMs) in enabling nonproductive enzyme binding is not well understood. Here, we examine the subtle interplay of CBM binding and cellulose hydrolysis activity for three models type-A CBMs (Families 1, 3a, and 64) tethered to multifunctional endoglucanase (CelE) on two distinct cellulose allomorphs (i.e., cellulose I and III). We generated a small library of mutant CBMs with varying cellulose affinity, as determined by equilibrium binding assays, followed by monitoring cellulose hydrolysis activity of CelE-CBM fusion constructs. Finally, kinetic binding assays using quartz crystal microbalance with dissipation were employed to measure CBM adsorption and desorption rate constants k on and k off , respectively, towards nanocrystalline cellulose derived from both allomorphs. Overall, our results indicate that reduced CBM equilibrium binding affinity towards cellulose I alone, resulting from increased desorption rates ( k off ) and reduced effective adsorption rates ( nk on ), is correlated to overall improved endocellulase activity. Future studies could employ similar approaches to unravel the role of CBMs in nonproductive enzyme binding and develop improved cellulolytic enzymes for industrial applications.

Moderate Electric Field Treatment Enhances Enzymatic Hydrolysis of Cellulose at Below-Optimal Temperatures

Saccharification of cellulosic biomass for the fermentation of transportation fuels faces several challenges. Cellulose is highly stable, and even with enzymatic assistance, decomposition of cellulose is slow. Additionally, the enzymes are expensive and sensitive to thermal and mechanical inactivation. In this work, we studied the effects of moderate electric field (MEF, in the range from 1 to 1000 V per cm) treatments on the effectiveness of enzymatic saccharification. MEF treatments were applied to determine their effects on enzyme activity. We considered the effects of field strength, frequency, application regime and temperature. It was found that the enzyme responded to alterations in the frequency of the waveform, with 50 to 60 Hz maximizing the effects of the field, although the effects of field strength and application regime were more significant. It was found that the electric field could have a positive, negative, or negligible effect depending on the field strength. Most notably, when MEF treatments were applied over a range of temperatures, it was found that MEF treatment significantly improved enzyme activity at lower temperatures, leading to the observation that MEF treatment imitates a temperature increase. Calculations simulating the electrophoretic motion of the enzymes verified that the magnitude of motion associated with the MEF treatments was qualitatively similar to the change in molecular motion associated with temperature increases.

Characterization and engineering of a two-enzyme system for plastics depolymerization

Deconstruction of recalcitrant polymers, such as cellulose or chitin, is accomplished in nature by synergistic enzyme cocktails that evolved over millions of years. In these systems, soluble dimeric or oligomeric intermediates are typically released via interfacial biocatalysis, and additional enzymes often process the soluble intermediates into monomers for microbial uptake. The recent discovery of a two-enzyme system for polyethylene terephthalate (PET) deconstruction, which employs one enzyme to convert the polymer into soluble intermediates and another enzyme to produce the constituent PET monomers (MHETase), suggests that nature may be evolving similar deconstruction strategies for synthetic plastics. This study on the characterization of the MHETase enzyme and synergy of the two-enzyme PET depolymerization system may inform enzyme cocktail-based strategies for plastics upcycling.

Plastics pollution represents a global environmental crisis. In response, microbes are evolving the capacity to utilize synthetic polymers as carbon and energy sources. Recently, Ideonella sakaiensis was reported to secrete a two-enzyme system to deconstruct polyethylene terephthalate (PET) to its constituent monomers. Specifically, the I. sakaiensis PETase depolymerizes PET, liberating soluble products, including mono(2-hydroxyethyl) terephthalate (MHET), which is cleaved to terephthalic acid and ethylene glycol by MHETase. Here, we report a 1.6 Å resolution MHETase structure, illustrating that the MHETase core domain is similar to PETase, capped by a lid domain. Simulations of the catalytic itinerary predict that MHETase follows the canonical two-step serine hydrolase mechanism. Bioinformatics analysis suggests that MHETase evolved from ferulic acid esterases, and two homologous enzymes are shown to exhibit MHET turnover. Analysis of the two homologous enzymes and the MHETase S131G mutant demonstrates the importance of this residue for accommodation of MHET in the active site. We also demonstrate that the MHETase lid is crucial for hydrolysis of MHET and, furthermore, that MHETase does not turnover mono(2-hydroxyethyl)-furanoate or mono(2-hydroxyethyl)-isophthalate. A highly synergistic relationship between PETase and MHETase was observed for the conversion of amorphous PET film to monomers across all nonzero MHETase concentrations tested. Finally, we compare the performance of MHETase:PETase chimeric proteins of varying linker lengths, which all exhibit improved PET and MHET turnover relative to the free enzymes. Together, these results offer insights into the two-enzyme PET depolymerization system and will inform future efforts in the biological deconstruction and upcycling of mixed plastics.

Enzymatic processing of lignocellulosic biomass: principles, recent advances and perspectives

Efficient saccharification of lignocellulosic biomass requires concerted development of a pretreatment method, an enzyme cocktail and an enzymatic process, all of which are adapted to the feedstock. Recent years have shown great progress in most aspects of the overall process. In particular, increased insights into the contributions of a wide variety of cellulolytic and hemicellulolytic enzymes have improved the enzymatic processing step and brought down costs. Here, we review major pretreatment technologies and different enzyme process setups and present an in-depth discussion of the various enzyme types that are currently in use. We pay ample attention to the role of the recently discovered lytic polysaccharide monooxygenases (LPMOs), which have led to renewed interest in the role of redox enzyme systems in lignocellulose processing. Better understanding of the interplay between the various enzyme types, as they may occur in a commercial enzyme cocktail, is likely key to further process improvements.

The hydrolysis mechanism of a GH45 cellulase and its potential relation to lytic transglycosylase and expansin function

Family 45 glycoside hydrolases (GH45) are endoglucanases that are integral to cellulolytic secretomes, and their ability to break down cellulose has been successfully exploited in textile and detergent industries. In addition to their industrial relevance, understanding the molecular mechanism of GH45-catalyzed hydrolysis is of fundamental importance because of their structural similarity to cell wall-modifying enzymes such as bacterial lytic transglycosylases (LTs) and expansins present in bacteria, plants, and fungi. Our understanding of the catalytic itinerary of GH45s has been incomplete because a crystal structure with substrate spanning the -1 to +1 subsites is currently lacking. Here we constructed and validated a putative Michaelis complex in silico and used it to elucidate the hydrolytic mechanism in a GH45, Cel45A from the fungus Humicola insolens, via unbiased simulation approaches. These molecular simulations revealed that the solvent-exposed active-site architecture results in lack of coordination for the hydroxymethyl group of the substrate at the -1 subsite. This lack of coordination imparted mobility to the hydroxymethyl group and enabled a crucial hydrogen bond with the catalytic acid during and after the reaction. This suggests the possibility of a nonhydrolytic reaction mechanism when the catalytic base aspartic acid is missing, as is the case in some LTs (murein transglycosylase A) and expansins. We calculated reaction free energies and demonstrate the thermodynamic feasibility of the hydrolytic and nonhydrolytic reaction mechanisms. Our results provide molecular insights into the hydrolysis mechanism in HiCel45A, with possible implications for elucidating the elusive catalytic mechanism in LTs and expansins.

Substrate binding in the processive cellulase Cel7A: Transition state of complexation and roles of conserved tryptophan residues

Cellobiohydrolases effectively degrade cellulose and are of biotechnological interest because they can convert lignocellulosic biomass to fermentable sugars. Here, we implemented a fluorescence-based method for real-time measurements of complexation and decomplexation of the processive cellulase Cel7A and its insoluble substrate, cellulose. The method enabled detailed kinetic and thermodynamic analyses of ligand binding in a heterogeneous system. We studied WT Cel7A and several variants in which one or two of four highly conserved Trp residues in the binding tunnel had been replaced with Ala. WT Cel7A had on/off-rate constants of 1 × 105 m-1 s-1 and 5 × 10-3 s-1, respectively, reflecting the slow dynamics of a solid, polymeric ligand. Especially the off-rate constant was many orders of magnitude lower than typical values for small, soluble ligands. Binding rate and strength both were typically lower for the Trp variants, but effects of the substitutions were moderate and sometimes negligible. Hence, we propose that lowering the activation barrier for complexation is not a major driving force for the high conservation of the Trp residues. Using so-called Φ-factor analysis, we analyzed the kinetic and thermodynamic results for the variants. The results of this analysis suggested a transition state for complexation and decomplexation in which the reducing end of the ligand is close to the tunnel entrance (near Trp-40), whereas the rest of the binding tunnel is empty. We propose that this structure defines the highest free-energy barrier of the overall catalytic cycle and hence governs the turnover rate of this industrially important enzyme.

Molecular recognition in the product site of cellobiohydrolase Cel7A regulates processive step length

Cellobiohydrolase Cel7A is an industrial important enzyme that breaks down cellulose by a complex processive mechanism. The enzyme threads the reducing end of a cellulose strand into its tunnel-shaped catalytic domain and progresses along the strand while sequentially releasing the disaccharide cellobiose. While some molecular details of this intricate process have emerged, general structure-function relationships for Cel7A remain poorly elucidated. One interesting aspect is the occurrence of particularly strong ligand interactions in the product binding site. In this work, we analyze these interactions in Cel7A from Trichoderma reesei with special emphasis on the Arg251 and Arg394 residues. We made extensive biochemical characterization of enzymes that were mutated in these two positions and showed that the arginine residues contributed strongly to product binding. Specifically, ∼50% of the total standard free energy of product binding could be ascribed to four hydrogen bonds to Arg251 and Arg394, which had previously been identified in crystal structures. Mutation of either Arg251 or Arg394 lowered production inhibition of Cel7A, but at the same time altered the enzyme product profile and resulted in ∼50% reduction in both processivity and hydrolytic activity. The position of the two arginine residues closely matches the two-fold screw axis symmetry of the substrate, and this energetically favors the productive enzyme-substrate complex. Our results indicate that the strong and specific ligand interactions of Arg251 and Arg394 provide a simple proofreading system that controls the step length during consecutive hydrolysis and minimizes dead time associated with transient, non-productive complexes.

Removal of N-linked glycans in cellobiohydrolase Cel7A from Trichoderma reesei reveals higher activity and binding affinity on crystalline cellulose

BACKGROUND

Cellobiohydrolase from glycoside hydrolase family 7 is a major component of commercial enzymatic mixtures for lignocellulosic biomass degradation. For many years, Trichoderma reesei Cel7A (TrCel7A) has served as a model to understand structure-function relationships of processive cellobiohydrolases. The architecture of TrCel7A includes an N-glycosylated catalytic domain, which is connected to a carbohydrate-binding module through a flexible, O-glycosylated linker. Depending on the fungal expression host, glycosylation can vary not only in glycoforms, but also in site occupancy, leading to a complex pattern of glycans, which can affect the enzyme's stability and kinetics.

RESULTS

Two expression hosts, Aspergillus oryzae and Trichoderma reesei, were utilized to successfully express wild-types TrCel7A (WT _Ao and WT _Tr ) and the triple N-glycosylation site deficient mutants TrCel7A N45Q, N270Q, N384Q (ΔN-glyc _Ao and ΔN-glyc _Tr ). Also, we expressed single N-glycosylation site deficient mutants TrCel7A (N45Q _Ao , N270Q _Ao , N384Q _Ao ). The TrCel7A enzymes were studied by steady-state kinetics under both substrate- and enzyme-saturating conditions using different cellulosic substrates. The Michaelis constant (K _M ) was consistently found to be lowered for the variants with reduced N-glycosylation content, and for the triple deficient mutants, it was less than half of the WTs' value on some substrates. The ability of the enzyme to combine productively with sites on the cellulose surface followed a similar pattern on all tested substrates. Thus, site density (number of sites per gram cellulose) was 30-60% higher for the single deficient variants compared to the WT, and about twofold larger for the triple deficient enzyme. Molecular dynamic simulation of the N-glycan mutants TrCel7A revealed higher number of contacts between CD and cellulose crystal upon removal of glycans at position N45 and N384.

CONCLUSIONS

The kinetic changes of TrCel7A imposed by removal of N-linked glycans reflected modifications of substrate accessibility. The presence of N-glycans with extended structures increased K _M and decreased attack site density of TrCel7A likely due to steric hindrance effect and distance between the enzyme and the cellulose surface, preventing the enzyme from achieving optimal conformation. This knowledge could be applied to modify enzyme glycosylation to engineer enzyme with higher activity on the insoluble substrates.

Grafting metal complexes onto amorphous supports: from elementary steps to catalyst site populationsviakernel regression

We use transition state theory, kernel regression, and population balance modeling techniques to model the grafting of metal complexes onto amorphous catalyst supports.

Interfacial molecular interactions of cellobiohydrolase Cel7A and its variants on cellulose

BACKGROUND

Molecular-scale mechanisms of the enzymatic breakdown of cellulosic biomass into fermentable sugars are still poorly understood, with a need for independent measurements of enzyme kinetic parameters. We measured binding times of cellobiohydrolase Trichoderma reesei Cel7A (Cel7A) on celluloses using wild-type Cel7A (WT_intact), the catalytically deficient mutant Cel7A E212Q (E212Q_intact) and their proteolytically isolated catalytic domains (CD) (WT_core and E212Q_core, respectively). The binding time distributions were obtained from time-resolved, super-resolution images of fluorescently labeled enzymes on cellulose obtained with total internal reflection fluorescence microscopy.

RESULTS

Binding of WT_intact and E212Q_intact on the recalcitrant algal cellulose (AC) showed two bound populations: ~ 85% bound with shorter residence times of < 15 s while ~ 15% were effectively immobilized. The similarity between binding times of the WT and E212Q suggests that the single point mutation in the enzyme active site does not affect the thermodynamics of binding of this enzyme. The isolated catalytic domains, WT_core and E212Q_core, exhibited three binding populations on AC: ~ 75% bound with short residence times of ~ 15 s (similar to the intact enzymes), ~ 20% bound for < 100 s and ~ 5% that were effectively immobilized.

CONCLUSIONS

Cel7A binding to cellulose is driven by the interactions between the catalytic domain and cellulose. The cellulose-binding module (CBM) and linker increase the affinity of Cel7A to cellulose likely by facilitating recognition and complexation at the substrate interface. The increased affinity of Cel7A to cellulose by the CBM and linker comes at the cost of increasing the population of immobilized enzyme on cellulose. The residence time (or inversely the dissociation rates) of Cel7A on cellulose is not catalysis limited.

pH profiles of cellulases depend on the substrate and architecture of the binding region

Understanding the pH effect of cellulolytic enzymes is of great technological importance. In this study, we have examined the influence of pH on activity and stability for central cellulases (Cel7A, Cel7B, Cel6A from Trichoderma reesei, and Cel7A from Rasamsonia emersonii). We systematically changed pH from 2 to 7, temperature from 20°C to 70°C, and used both soluble (4-nitrophenyl β- d-lactopyranoside [pNPL]) and insoluble (Avicel) substrates at different concentrations. Collective interpretation of these data provided new insights. An unusual tolerance to acidic conditions was observed for both investigated Cel7As, but only on real insoluble cellulose. In contrast, pH profiles on pNPL were bell-shaped with a strong loss of activity both above and below the optimal pH for all four enzymes. On a practical level, these observations call for the caution of the common practice of using soluble substrates for the general characterization of pH effects on cellulase activity. Kinetic modeling of the experimental data suggested that the nucleophile of Cel7A experiences a strong downward shift in pK_a upon complexation with an insoluble substrate. This shift was less pronounced for Cel7B, Cel6A, and for Cel7A acting on the soluble substrate, and we hypothesize that these differences are related to the accessibility of water to the binding region of the Michaelis complex.

Modeling catalytic reaction mechanisms in glycoside hydrolases

Modeling catalysis in carbohydrate-active enzymes is a daunting challenge because of the high flexibility and diversity of both enzymes and carbohydrates. Glycoside hydrolases (GHs) are an illustrative example, where conformational changes and subtle interactions have been shown to be critical for catalysis. GHs have pivotal roles in industry (e.g. biofuel or detergent production) and biomedicine (e.g. targets for cancer and diabetes), and thus, a huge effort is devoted to unveil their molecular mechanisms. Besides experimental techniques, computational methods have served to provide an in-depth understanding of GH mechanisms, capturing complex reaction coordinates and the conformational itineraries that substrates follow during the whole catalytic pathway, providing a framework that ultimately may assist the engineering of these enzymes and the design of new inhibitors.

The dissociation mechanism of processive cellulases

Cellulase enzymes deconstruct recalcitrant cellulose into soluble sugars, making them a biocatalyst of biotechnological interest for use in the nascent lignocellulosic bioeconomy. Cellobiohydrolases (CBHs) are cellulases capable of liberating many sugar molecules in a processive manner without dissociating from the substrate. Within the complete processive cycle of CBHs, dissociation from the cellulose substrate is rate limiting, but the molecular mechanism of this step is unknown. Here, we present a direct comparison of potential molecular mechanisms for dissociation via Hamiltonian replica exchange molecular dynamics of the model fungal CBH, Trichoderma reesei Cel7A. Computational rate estimates indicate that stepwise cellulose dethreading from the binding tunnel is 4 orders of magnitude faster than a clamshell mechanism, in which the substrate-enclosing loops open and release the substrate without reversing. We also present the crystal structure of a disulfide variant that covalently links substrate-enclosing loops on either side of the substrate-binding tunnel, which constitutes a CBH that can only dissociate via stepwise dethreading. Biochemical measurements indicate that this variant has a dissociation rate constant essentially equivalent to the wild type, implying that dethreading is likely the predominant mechanism for dissociation.

Binding Process and Free Energy Characteristics of Cellulose Chain into the Catalytic Domain of Cellobiohydrolase TrCel7A

It was observed in experiments that the catalytic domain (CD) of Trichoderma reesei Cel7A (TrCel7A) hydrolyzes crystalline cellulose in a processive manner, but the underlying binding mechanism is still unknown. Here, through replica-exchange molecular dynamics simulations, we find that the loading and sucking-in process of the cellulose chain into CD is entropy-driven and enthalpy-unfavorable, which firmly relate to the desolvation of the binding channel of CD. During the loading process, hydrophobic interactions play a dominant role because several aromatic residues have been identified to guide the cellulose chain processing. At the active site, a transition from enthalpy- to entropy-driven is detected for the driving force. Such a finding reveals the indispensability of the catalytic reaction of the glycosidic bond to provide the energy to drive the movements of the cellulose chain. Our study reveals the interaction pictures between the cellulose chain and TrCel7A at the atomic level, which helps better understand the catalytic mechanism of TrCel7A.

A practical approach to steady-state kinetic analysis of cellulases acting on their natural insoluble substrate

Measurement of steady-state rates (v_SS) is straightforward in standard enzymology with soluble substrate, and it has been instrumental for comparative biochemical analyses within this area. For insoluble substrate, however, experimental values of v_ss remain controversial, and this has strongly limited the amount and quality of comparative analyses for cellulases and other enzymes that act on the surface of an insoluble substrate. In the current work, we have measured progress curves over a wide range of conditions for two cellulases, TrCel6A and TrCel7A from Trichoderma reesei, acting on their natural, insoluble substrate, cellulose. Based on this, we consider practical compromises for the determination of experimental v_SS values, and propose a basic protocol that provides representative reaction rates and is experimentally simple so that larger groups of enzymes and conditions can be readily assayed with standard laboratory equipment. We surmise that the suggested experimental approach can be useful in comparative biochemical studies of cellulases; an area that remains poorly developed.