Fungal systems for lignocellulose deconstruction: From enzymatic mechanisms to hydrolysis optimization

Lignocellulosic biomass is an abundant renewable feedstock, but its complex structure of lignocellulose poses barriers to its enzymatic hydrolysis and fermentation. Fungi possess diverse lignocellulolytic enzyme systems that synergistically deconstruct lignocellulose into soluble sugars for fermentation. This review elucidates recent advances in understanding the molecular mechanisms underpinning fungal degradation of lignocellulose. We analyze major enzyme classes tailored by fungi to depolymerize cellulose, hemicellulose, and lignin. Highlighted are the concerted actions and intimate partnerships between these biomass‐degrading enzymes. Current challenges impeding large‐scale implementation of enzymatic hydrolysis are discussed, along with emerging biotechnological opportunities. Advanced pretreatments, high‐throughput enzyme engineering platforms, and machine learning or artificial intelligence‐guided lignocellulolytic enzyme cocktail optimization represent promising ways to improve hydrolytic efficiencies. Elucidating the coordinated interplay and regulation of fungal lignocellulolytic machinery can facilitate optimization of fungal biotechnology platforms. Harnessing the efficiency of fungal biomass deconstruction promises to enhance the development of biorefinery processes for sustainable bioenergy.

studied for its ability to effectively break down complex lignocellulosic materials and produce a wide range of lignocellulose-degrading enzymes (Cragg et al., 2015;Saldarriaga-Hernández et al., 2020).Additionally, fungi can generate accessory proteins and transporters to facilitate the uptake of released nutrients, thereby enabling more efficient utilization of biomass resources (Walker & White, 2017).Consequently, fungi have been acknowledged as highly efficient biomass degraders.
The diversity of fungal species capable of degrading different types of lignocellulosic biomass is truly remarkable.Numerous fungal species have been identified that possess the ability to break down a wide range of biomass feedstocks, including agricultural and forestry residues, energy crops, and even municipal solid waste.For instance, white-rot fungi such as Phanerochaete chrysosporium and Pleurotus ostreatus are well-known for their capacity to degrade lignin (Ahmad et al., 2023;Datsomor et al., 2022), while brown-rot fungi such as Gloeophyllum trabeum can selectively break down the cellulose and hemicellulose components (Qi et al., 2023).Furthermore, filamentous fungi such as Aspergillus niger and Trichoderma reesei have been extensively studied for their ability to produce substantial amounts of lignocellulose-degrading enzymes, making them highly suitable for large-scale biomass conversion processes (Benatti & Polizeli, 2023;Champreda et al., 2019;Lopes et al., 2018).In summary, the utilization of fungi as biomass degraders offers numerous advantages.These include their efficient and specific enzymatic systems, their ability to produce accessory proteins and transporters that facilitate nutrient uptake, and their vast diversity in degrading various forms of lignocellulosic biomass.These characteristics make fungi an appealing and promising option for the development of sustainable and cost-effective processes for renewable energy production and environmental sustainability.
Despite the immense potential of fungi in biomass degradation, our current understanding of the molecular mechanisms involved remains limited.In order to optimize bioprocesses and develop innovative biotechnological applications, it is crucial to gain a better understanding of the complex interactions between fungal enzymes and lignocellulosic substrates (Monclaro et al., 2022).Additionally, exploring the diversity of fungal species and their unique capabilities in breaking down different types of biomass can provide valuable insights into the development of efficient and sustainable biorefinery processes (de Vries & Mäkelä, 2020).A comprehensive understanding of the mechanisms underlying biomass degradation by microorganisms is essential for optimizing the conversion of biomass into valuable products.Furthermore, studying these mechanisms can also lead to the discovery of novel enzymes and metabolic pathways that can be utilized in various biotechnological applications (Paul et al., 2023).In summary, biomass degradation plays a critical role in renewable energy production and environmental sustainability.In-depth studies on the mechanisms involved in microbial biomass degradation can pave the way for the development of efficient and environmentally friendly biotechnological processes for converting biomass into valuable products.

COMPOSITION AND STRUCTURE
Lignocellulosic biomass is a complex material consisting of three primary components: cellulose, hemicellulose, and lignin.In addition, small amounts of pectin, nitrogen compounds, and mineral residues can also be found in these feedstocks (Stech et al., 2014).Cellulose and hemicellulose are both polysaccharides, while lignin is a phenolic polymer.These components are distributed differently within the plant cell wall.Table 1 provides an overview of the composition of lignocellulosic biomass in major agro-industrial residues.Cellulose is the predominant component of the cell wall, forming crystalline microfibrils, whereas hemicellulose is a more amorphous polysaccharide that coats the cellulose microfibrils.Lignin, on the other hand, acts as a binder, holding the cellulose and hemicellulose together.Lignin consists of three primary structural monomers: the p-phenyl monomer (H type) derived from coumaryl alcohol, the guaiacyl monomer (G type) derived from coniferyl alcohol, and the syringyl monomer (S type) derived from sinapyl alcohol (Chen, 2014).The Figure 1 shows the schematic structure of lignocellulosic biomass composed of cellulose, hemicellulose, and lignin.
The structural complexity of lignocellulosic biomass presents a significant challenge for the efficient degradation by fungal enzymes.There are several factors that contribute to the recalcitrance of lignocellulose-containing raw materials.These include the presence of lignin, which acts as a protective barrier for cellulose, the interweaving of cellulose by hemicellulose, the high crystallinity and degree of polymerization of cellulose, and the limited accessible surface area of cellulose with strong fiber strength (Schirmaier et al., 2014).To optimize bioprocesses and develop innovative biotechnological applications, it is crucial to gain a better understanding of the structural complexity of lignocellulosic biomass and the molecular mechanisms involved in its degradation by fungal enzymes.The development of efficient enzymatic cocktails capable of overcoming the structural challenges of lignocellulosic biomass is vital for the sustainable production of biofuels and other valuable products from renewable resources.

| Lignocellulolytic enzymes
Fungi have developed a variety of enzymes that efficiently degrade the complex lignocellulosic biomass.These enzymes can be categorized into different types, including cellulases, hemicellulases, ligninases, and accessory enzymes (Figure 2).The enzymatic breakdown of lignocellulosic biomass relies on the collaborative action of various microbial secretases.These enzymes function together in a complex system, and their effectiveness is determined by their ability to access and break down the different components of the plant cell wall.Cellulose degradation involves the hydrolysis of cellulose by endocellulases (EC 3.2.1.4),exocellulases (or cellobiohydrolases [CBHs]; EC 3.2.1.91),cellobiases (or βglucosidases; EC 3.2.1.21),oxidative cellulases, and cellulose phosphorylases.Endocellulases randomly cleave internal bonds at amorphous sites, generating new chain ends.Exocellulases cleave two to four units from the ends of the exposed chains produced by endocellulases, resulting in tetrasaccharides or disaccharides such as cellobiose.Type I exocellulases work processively from the reducing end of the cellulose chain, while type II exocellulases work processively from the non-reducing end.Cellobiases hydrolyze the product of exocellulases into individual monosaccharides.Oxidative cellulases depolymerize cellulose through radical reactions, such as cellobiose dehydrogenase (CDH; acceptor).Cellulose phosphorylases depolymerize cellulose using phosphates instead of water.Hemicellulose hydrolysis involves three main enzymes: xylanase, mannanase, and arabinanase (Ma et al., 2020).Lignin biodegradation is a sophisticated oxidative process orchestrated by a repertoire of peroxidases and phenol oxidases, among which lignin peroxidase (LiP), manganese peroxidase (MnP), versatile peroxidase (VP), dye decolorizing peroxidase (DyP), and laccase play pivotal roles (Welinder, 1992).Complementing these primary enzymes are a suite of accessory enzymes, including aryl-alcohol dehydrogenases (AADs), quinone reductases (QRs), glyoxylate oxidase (GLOX), and alcohol oxidase (AAO).The dynamic landscape of ligninolytic enzymes is continually evolving, with recent discoveries such as aromatic peroxygenases (APOs) reshaping our understanding and classification of these enzymatic systems.Accessory enzymes, such as pectinase, esterase, and protease, play a critical role in the degradation of lignocellulosic biomass.These enzymes modify the lignocellulosic substrate, making it more accessible to ligninolytic enzymes.The synergistic collaboration of these enzymes is crucial for the effective degradation of plant biomass and is a key focus of research for enhancing the efficiency of bioprocesses in renewable energy production and biorefinery applications.The mode of action of these enzymes is determined by their unique structural and biochemical properties, which dictate their substrate specificity and catalytic activity.

| Cellulose degradation by cellulolytic enzymes system
The cellulase enzyme, produced by aerobic fungi, currently plays a pivotal and predominant role in numerous industrial applications.Among filamentous fungi, the genera Penicillium, Trichoderma, and Aspergillus, have emerged as quintessential models for cellulase production, spanning from laboratory-scale investigations to largescale industrial processes (de França Passos et al., 2018).
These enzymes work in a coordinated manner, targeting various aspects of cellulose and ultimately converting it into glucose through hydrolysis.
Cellulases consist of multiple catalytic domains that collaborate to break glycosidic bonds between glucose residues, releasing glucose monomers.The process of cellulose degradation involves various types of cellulases and accessory enzymes working synergistically in a series of steps that include substrate binding, glycosidic bond hydrolysis, and product release.Cellulases bind to the cellulose substrate through a combination of hydrophobic and polar interactions.The presence of a substrate-binding domain (SBD) in cellulases facilitates their binding to the substrate.The SBD interacts with the crystalline regions of the cellulose substrate and disrupts the interchain hydrogen bonds, enabling the enzyme to access the glycosidic bonds.The subsequent step in the mechanism of cellulase action involves the hydrolysis of the β-1,4-glycosidic bonds that connect the glucose residues in the cellulose chain.This reaction is catalyzed by the catalytic domain of the cellulase, which contains the active site residues responsible for catalysis.The hydrolysis reaction proceeds through the nucleophilic attack of a water molecule on the anomeric carbon of the glucose residue, resulting in the formation of a covalent glycosyl-enzyme intermediate.This intermediate is then cleaved by the addition of a second water molecule, leading to the release of the glucose monomer.
Following the hydrolysis reaction, the cellulase enzyme is released from the substrate and can proceed to bind to another cellulose chain.The final step in the mechanism of cellulase action involves the processive action of the enzyme on the cellulose substrate.Processivity refers to the ability of the cellulase enzyme to hydrolyze multiple glycosidic bonds in a single binding event without dissociating from the substrate.This processivity is facilitated by the presence of a cellulose-binding domain in the cellulase, which enables the enzyme to move along the cellulose chain and continue hydrolyzing glycosidic bonds until it is released.The primary function of endoglucanase (endo-1,4-βglucanases) (EC 3.2.1.4),also known as carboxymethyl cellulase, is the hydrolysis of carboxymethyl cellulose (CMC).The endoglucanase complex consists of endo-β-1,4-dglucanase and endo-β-1,4-d-glucan-4-glucanohydrolase.Its main objective is to reduce the length of cellulose polymer chains by breaking the glycosidic bonds present in amorphous cellulose (Sajith et al., 2016).It is important to note that endoglucanase primarily acts on amorphous cellulose and does not exhibit activity toward crystalline cellulose.It also functions on cellodextrins, which are intermediate products formed during cellulose hydrolysis.
Exoglucanase, also known as CBHs or 1,4-βd-glucan cellobiohydrolase (EC 3.2.1.91),cleaves the ends of cellulose chains, resulting in the release of oligosaccharides, such as cellobiose units.There are two types of CBHs: CBH I acts on the non-reducing end, and CBH II acts on the reducing end of the cellulose chain.These enzymes are aided by endoglucanases, which randomly break and cleave internal glycosidic bonds of the cellulose polymer, providing free cellulose chain ends for CBHs.This enzyme is active when exposed to microcrystalline substrates but is inactive toward CMC.β-Glucosidases (EC 3.2.1.21)complete the collaborative action of cellulases by hydrolyzing the released oligosaccharides into glucose molecules.They play a crucial role in converting cellobiose, produced by exoglucanase, into glucose, thus concluding the hydrolysis process (Lu et al., 2024).In addition, some fungi also produce other enzymes to facilitate the decomposition of cellulose.For example, cellodextrinase (EC 3.2.1.74)can remove disaccharide (cellobiose) from cellooligosaccharides.Furthermore, cellodextrin phosphorylase (EC 2.4.1.49),cellobiose phosphorylase (EC 2.4.1.20),and cellobiose epimerase (EC 5.1.3.11) have also been found to be involved in cellulose degradation (Sharma et al., 2016;Wang et al., 2020).Recently, lytic polysaccharide monooxygenases (LPMOs) have been uncovered for their ability to directly oxidize crystalline substrate surfaces, which extremely enhances the overall degradability of cellulose (Várnai et al., 2014).Several studies have demonstrated that LPMOs are oxidative enzymes acting in synergy with CDH that gives a new view on a cellulose degradation (Langston et al., 2011;Quinlan et al., 2011).Figure 3 illustrates a schematic representation of the collaborative action of cellulases (endoglucanase, exoglucanase, and βglucosidase) and LPMO enzymes in degrading cellulose chains.
The hydrolysis of hemicelluloses by hemicellulases involves the breaking of glycosidic bonds between the constituent sugars.This can occur through different mechanisms depending on the specific enzyme and substrate involved.Different types of hemicellulases have specific actions on hemicellulosic substrates.For instance, endo-β-1,4-xylanases cleave the backbone of xylan, while βxylosidases act on xylooligosaccharides and xylobiose (Godoy et al., 2018).Acetyl xylan esterases are responsible for removing acetyl groups from xylan chains.Ferulic and p-cumaric acid esterases, on the other hand, cleave the ester bond between arabinose and ferulic acid side chains and the ester bond between arabinose and p-coumaric acid, respectively (Wang & Lü, 2021).There are also other hemicellulases that target different hemicellulosic substrates.These include mannanases, galactanases, glucuronoyl esterases, and arabinofuranosidases.
Therefore, complete hemicellulose hydrolysis requires the concerted action of endo-and exo-acting enzymes tailored to specific structures and linkages.Elucidating the catalytic mechanisms provides insight into their synergism in deconstruction.See Figure 4b for further details.

| Lignin degradation by lignin-modifying enzymes
Fungi have a diverse range of enzymes involved in biomass degradation, which can vary greatly between different fungal species and even among strains of the same species.This diversity is a result of fungal adaptation to various ecological niches and substrates, as well as the complex structure of lignocellulosic biomass.Fungi possess both intracellular degradation systems, which work in conjunction with the outer cell envelope layer, and extracellular systems that are crucial for polysaccharide degradation.The extracellular enzymatic system consists of two types of enzymes: hydrolytic enzymes, responsible

Aspergillus fumigatus
Aspergillus nidulans for polysaccharide degradation, and oxidative enzymes, which break down lignin and open phenyl rings (Andlar et al., 2018).
Effective degradation of biomass by fungi relies on the regulation of gene expression and enzyme secretion.Lignin-modifying enzymes exhibit various oxidoreductase activities and act on different lignin substructures, producing a range of aromatic compounds.The enzymatic hydrolysis mechanism of ligninase involves the oxidative degradation of lignin by laccases (EC 1.10.3.2;CAZy AA1), LiP (EC 1.11.1.14;CAZy AA2), and MnP (EC 1.11.1.13;CAZy AA2).These enzymes generate reactive species, which oxidize lignin and form reactive intermediates that can undergo depolymerization, resulting in smaller lignin fragments.Additionally, various free radicals such as hydroxyl radical, carboxylate anion radical, and superoxide radical are also involved in the depolymerization of lignocellulose (Floudas et al., 2012).
In summary, the generation of radical species through Fenton chemistry-like reactions and the oxidative cleavage of key lignin interunit linkages are central to the depolymerization and mineralization of lignin by lignin-modifying enzymes.Numerous studies have provided extensive kinetic and spectroscopic evidence to support these enzymatic mechanisms.For instance, Xie et al. ( 2020) investigated the crystal structure of a maize laccase, revealing its ability to oxidize monolignols that are crucial for lignin polymerization.Additionally, Miki et al. (2011) employed crystallographic, kinetic, and spectroscopic techniques to study ligninolytic peroxidase, identifying and characterizing a novel lignin-degrading peroxidase that uses a tyrosine residue instead of tryptophan as the catalytic base, providing insights into alternative lignin breakdown strategies in white rot fungi.Furthermore, Hofrichter and Ullrich (2006) provide an overview of radical mechanisms employed by various haloperoxidases, including their generation of radical species and cleavage of different aromatic compounds.These studies offer kinetic, spectroscopic, and structural evidence supporting the generation of radical species and the oxidative cleavage mechanisms employed by LiP and other oxidative enzymes for lignin degradation (Figure 5).
Lytic polysaccharide monooxygenases employ an unprecedented oxidative reaction mechanism to cleave and locally decrystallize these biopolymers, thereby enhancing the activity of associated hydrolases (Quinlan et al., 2011;Song et al., 2018;Vaaje-Kolstad et al., 2010).The detailed catalytic mechanism of LPMOs and the nature of their cosubstrate are subjects of ongoing debate.Extensive research in recent years has proposed several models to explain the catalytic mechanism of these enzymes.The current consensus suggests that LPMOs catalyze the oxidative cleavage of glycosidic bonds in the polysaccharide substrate through an unusual mechanism involving the formation of a high-valent copper-oxo intermediate (Maiti et al., 2008;Wu et al., 2020).This intermediate is generated when the LPMO copper center reacts with molecular oxygen and a reducing agent, such as ascorbate, which donates electrons to activate the copper center for oxygen binding.Once the copper-oxo intermediate is formed, it can react with the polysaccharide substrate to cleave glycosidic bonds through various mechanisms, including hydroxylation, C1-oxidation, and C4-oxidation, depending on the type of LPMO and substrate (Beeson et al., 2012;Phillips et al., 2011;Walton & Davies, 2016).The exact mechanism of bond cleavage by LPMOs is still a matter of debate and likely involves a complex interplay of factors, including the orientation of the substrate relative to the copper-oxo intermediate, the electron density of the substrate, and the presence of other cofactors or active site residues.Recent studies have also revealed the importance of LPMOs in plant-microbe interactions, particularly in the degradation of plant cell walls during pathogenesis (Vandhana et al., 2022).Lytic polysaccharide monooxygenases have been shown to be secreted by plant-pathogenic fungi and to play a role in the degradation of plant cell walls, thereby facilitating nutrient acquisition and infection (Kubicek et al., 2014;Sabbadin et al., 2021).Overall, the discovery and characterization of LPMOs have greatly expanded our understanding of the molecular mechanisms underlying biomass degradation in fungi and other microorganisms, with significant implications for the development of sustainable biotechnological applications.Besides, the catalytic mechanism of LPMOs represents a unique and fascinating example of enzyme-mediated oxidative chemistry, and the continued study of these enzymes has the potential to unlock new strategies for the efficient degradation of lignocellulosic biomass and the production of sustainable biofuels and chemicals.
To summarize, the efficiency of fungal-based bioprocesses in biomass degradation is influenced by the diversity of fungal enzyme systems, as well as the regulation of gene expression and enzyme secretion.A deeper understanding of the molecular mechanisms involved in these processes offer valuable insights for optimizing and developing new biotechnological applications.It is importto note that these enzymes also interact with other biomass components, including lignin and pectin, which can impede their activity by binding to the enzyme surface or physically obstructing access to the substrate.Furthermore, the physical and chemical properties of the biomass matrix, such as porosity, crystallinity, and degree of polymerization, can impact enzyme accessibility and substrate availability.Therefore, comprehending these intricate interactions is crucial for the efficient degradation of biomass through the optimization of fungal enzyme cocktails.

ENHANCEMENT OF LIGNOCELLULOSE HYDROLYSIS
The efficient depolymerization of lignocellulosic biomass via enzymatic hydrolysis is critical for the industrialization and commercialization of sustainable biorefinery processes.However, the substantial variability in biomass composition and structure across different lignocellulosic feedstocks poses significant challenges for optimizing enzymatic cocktails matching for different biomass.Due to complexity lignocellulosic biomass, more than 30 classes of enzymes produced by microorganisms are involved in the deconstruction of lignocellulose, which provides the possibility of millions of combinations of enzymes.Traditionally, enzyme formulations have been developed through time-consuming and labor-intensive trial-and-error experimental efforts, which are economically burdensome and environmentally polluting (Kim et al., 2016).Fortunately, recent advances in AI offer new opportunities to expedite and enhance the design of customized enzyme blends matching for specific biomass substrates.Machine learning, a subset of the broader category of artificial intelligence (AI), can be benefit from the integration with traditional experiments (Chen et al., 2020).The comprehensive review of literature from 2013 to 2022 revealed that implementing AI, especially in the form of hybrid models and artificial neural networks (ANN), has significantly impacted the design and process engineering strategies in bioprocessing fields (Yang, Kristiani, et al., 2023).This advancement underscores the potential of AI in revolutionizing bioengineering strategies, enhancing both the design and process engineering aspects of bioprocessing.
Machine learning algorithms have the capability to construct predictive models between the structural composition of various biomasses and the performance of different enzyme combinations (Ge et al., 2023).These models can be trained using datasets that combine measurements of cellulose, hemicellulose, and lignin content with the hydrolysis efficacies of specific cocktail formulations.Through this training, the models can learn intricate relationships between substrate attributes and optimal enzymes (Hu, Qian, et al., 2023).Once adequately trained, these AI systems can propose novel, highperforming cocktails for untested biomass materials based solely on rapid compositional characterization.Machine learning algorithms, such as ANN, random forests, and support vector machines, can construct nonlinear multivariate models correlating the structural composition of various biomasses to the performance of different enzyme combinations (Ahmad Sobri et al., 2023;Yang et al., 2022).
By training these models on datasets that encompass characterized biomass samples and their corresponding hydrolysis efficacies for specific enzyme cocktails, the algorithms can learn complex mapping functions that relate input variables, such as cellulose, hemicellulose, and lignin content, to the desired output of enzymatic hydrolysis yield (Haldar et al., 2023).Furthermore, advanced feature engineering techniques can also be incorporated to generate additional inputs to the model based on chemical or physical characterizations of the biomass substrate (e.g., crystallinity index, degree of polymerization, and accessibility) (Fransen et al., 2023;Hu, Qian, et al., 2023).Properly trained models can then predict the enzymatic hydrolysis potential of novel, untested biomass materials based solely on a rapid compositional analysis as input to the model.The prediction accuracy depends on the robustness of the generated mapping function, which is influenced by the quantity and diversity of training data, suitability of the selected machine learning algorithm, and appropriateness of the input features (Jyoti et al., 2022).As more validated data accumulates, the performance of model can be continually improved via re-training.Overall, data-driven machine learning modeling enables the composition-enzyme performance relationships underlying effective lignocellulose hydrolysis to be captured based on empirical evidence, supporting the rational design of optimized enzyme cocktails.
Near infrared (NIR) spectroscopy is a promising technique for rapidly gauging lignin, cellulose, and hemicellulose content in various biomass samples to feed AI optimization models (Pancholi et al., 2023).When combined with multivariate calibration methods such as partial least squares regression, NIR can accurately predict composition with minimal sample processing.ANN have demonstrated their proficiency in modeling the intricate connections between input variables such as NIR-measured composition and target outputs like enzymatic hydrolysis yields (Baum et al., 2011;Cousins et al., 2022).Evolutionary algorithms can be employed to optimize the suggested enzyme ratios, maximizing the synergistic against the specific biomass composition.
learning techniques cannot only optimize enzyme cocktail formulations but also aid in identifying ideal process parameters to enhance enzymatic hydrolysis of biomass.Models can be trained on experimental data correlating factors like substrate loading, temperature, pH, and incubation time to measured hydrolysis yields and kinetics.This allows for empirical modeling of parameter-performance relationships, enabling the selection of optimized conditions that maximize hydrolysis efficacy of specific biomass feedstocks.For example, recent studies have demonstrated the efficacy of ANN modeling for optimizing critical process parameters to enhance enzymatic hydrolysis of lignocellulosic substrates.Chouaibi et al. (2020) developed an ANN model to optimize the enzyme hydrolysis of pumpkin peel wastes.The model achieved a high prediction accuracy with an R 2 exceeding 0.99 during training, validation, and testing.Using the model, the optimal conditions identified were 56.40 U/ mL amyloglucosidase concentration, 7.5 U/g αamylase, 17.5 g/L substrate loading, and 120 min hydrolysis time.Experimental evaluation of these optimized parameters resulted in 50.60 g/L reducing sugar and 84.36 g/L bioethanol, which closely aligned with the ANN-predicted value of 50.69 and 84.27 g/L.In another study, Vani et al. (2015) reported an ANN model to predict xylose and glucose concentrations during the enzymatic hydrolysis of alkali pretreated rice straw.This model also demonstrated strong predictive power, with overall R 2 values of 0.96 and 0.97 for xylose and glucose concentrations respectively.Furthermore, Gao et al. (2023) developed an EAnet model utilizing embedding and attention operations to effectively learn these interactions between substrate parameters and enzyme cocktail.This data-driven approach allows for more precise prediction of optimal enzymesubstrate combinations.In their study, over 90% of experimentally determined top combinations were identified within the model's predicted ranges.Practical validation demonstrated the effectiveness of the method in speeding up targeted screening and improving biomass hydrolysis efficiency.
These data-driven strategies can be integrated into closed-loop systems performing high-throughput enzymatic assays on cocktails generated by the AI.This integration allows for the expansion of training data through the continual acquisition of new data, which in turn refines the model predictions, provides validation, and facilitates on-site optimization of enzyme formulations for different feedstocks.In addition to cocktail design, machine learning techniques offer new possibilities to enhance enzymatic deconstruction of lignocellulose via data-driven optimization of hydrolytic parameters and processes.Furthermore, by enhancing sugar release from biomass feedstocks such as straw, these AI-guided enzymatic hydrolysis approaches can increase yields in the subsequent production of single-cell protein through solid-state fermentation.Tailored enzyme cocktails can produce optimal mixtures of hexoses and pentoses to support growth of protein-rich fungal and bacterial cultures.The singlecell protein can then supply sustainable nutrition and functional ingredients for food, feed, and nutritional applications (Figure 6).While there is still significant work to be done in accumulating training data, constructing robust models, and experimentally validating AI-designed cocktails and hydrolytic parameters, these emerging techniques hold exciting potential for accelerating the tailored design of enzyme blends for a wide range of lignocellulosic substrates.

PROSPECTS FOR ENZYMATIC LIGNOCELLULOSE HYDROLYSIS
Despite the significant potential of enzymatic hydrolysis in converting lignocellulosic biomass into renewable fuels, chemicals, and food, there are still substantial challenges that hinder its widespread implementation on a large scale.The recalcitrant structure of lignocellulose itself severely restricts enzyme access to cellulose and hemicellulose due to the protective barrier imposed by lignin and hemicellulose encasement.Additionally, the crystalline structure of cellulose also limits hydrolytic efficiency.The variability in the composition and structure of lignocellulosic materials necessitates tailored enzyme cocktails for effective degradation.These cocktails, consisting of a broad range of specific enzymes, are influenced by the substrate's properties.However, gaps in understanding the molecular mechanisms of substrate-enzyme interactions present a major obstacle in optimizing enzyme systems for efficient lignocellulosic biomass conversion (Hu, Priya, et al., 2023).Moreover, the cost-effectiveness of the overall process is hindered by several factors such as the cost of enzyme production, the substrate pretreatment requirements, and the downstream processing steps.Furthermore, the activity and stability of lignocellulolytic enzymes can pose a bottleneck.Factors such as temperature, pH, proteolysis, and the presence of inhibitors can all negatively impact enzyme activity over the timescales required for industrial processes.This instability further drives up enzyme dosage requirements.Finally, inhibitory compounds generated during pretreatment and fungal degradation processes can interfere with enzymatic hydrolysis and microbial fermentation, thereby reducing yields.Collectively, these challenges present significant obstacles that must be overcome to and sustainable lignocellulosic biorefining.
Fortunately, emerging opportunities help overcome these obstacles through technological innovations.For instance, the identification and characterization of novel fungal enzymes with improved properties such as thermostability and pH tolerance.This can increase the efficiency and reduce the cost of lignocellulosic biomass deconstruction.Furthermore, the development of sustainable and environmentally friendly pretreatment methods for lignocellulosic biomass can have a positive impact on the bioprocess.More importantly, novel pretreatment methods such as steam explosion (autohydrolysis), ammonia fiber expansion, and ionic liquids can partially deconstruct lignocellulose and increase biomass porosity to promote enzyme access while minimizing inhibitor generation (Karimi et al., 2013;Lee et al., 2009;Maurya et al., 2015).In addition, genetic and protein engineering approaches have generated more robust cellulases with higher activity under industrial conditions (Contreras et al., 2020;Dadwal et al., 2020).Moreover, the utilization of genetic engineering and synthetic biology approaches can facilitate the design of fungal strains with enhanced biomass degradation capabilities and the production of high-value products from lignocellulosic biomass (Martins-Santana et al., 2018).The development of fungal cell factories for enhanced cellulase production can be advanced through the selection of robust strains and the integration of multi-omics data.Utilizing tools such as CRISPR-Cas9 for genome engineering, along with promoter and transcription factor manipulation, can further refine these strains.Integrating synthetic biology with AI techniques could significantly boost the efficiency of these engineered systems (Yang, Yue, et al., 2023).Besides, in order to achieve highly efficient enzymatic hydrolysis of lignocellulosic biomass, it is crucial to not only improve industrial properties of cellulase, but also match appropriate lignocellulolytic enzymes to the biomass.
guided cocktail optimization is a promising approach tailoring enzyme formulations to specific feedstocks.Another opportunity lies in integrating of fungal biomass degradation with other industrial processes to generate value-added products such as biofuels, bioplastics, and biochemicals, thereby enhancing the economic viability of the lignocellulosic deconstruction.
In summary, addressing the challenges related to the cost-effectiveness and enzymatic efficiency is essential to fully harness the potential of fungal biomass degradation for sustainable bioenergy and bioproducts production.A systematic, multifaceted perspective accounting for the entire conversion process will be essential to develop competitive and sustainable lignocellulosic deconstruction.

| CONCLUSIONS
In conclusion, fungi possess an impressive range of metabolic versatility and extensive enzymatic systems specifically adapted for efficient lignocellulose deconstruction.Recent advances in elucidating the molecular mechanisms of biomass degradation by fungi are enabling discovery of novel enzymes, activities, and synergies.Nevertheless, realizing the full potential of fungal lignocellulolytic biotechnology requires overcoming challenges in enzyme production, secretion, substrate interactions, and inhibition.Data-driven approaches leveraging machine learning and high-throughput experiments promise to accelerate progress.For example, modeling the relationship between biomass composition and enzyme performance can aid in optimizing tailored cocktails and hydrolytic conditions.By mimicking the innate ability of fungi to degrade plant biomass, we can develop bioprocesses to convert lignocellulose into sustainable biofuels, biochemicals, and feed protein.These fungal-based platforms offer renewably-sourced alternatives to petroleum-derived energy, chemicals and food.Further systems-level understanding of coordinated enzymatic activity and regulation will be crucial to optimizing these advances.Therefore, combining fundamental insights from fungi with data-driven biotechnology promises exciting potential for developing robust and efficient lignocellulose biorefining processes.

F
Schematic representation of lignocellulosic biomass structure composed by cellulose, hemicelluloses, and lignin.

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Classification of lignocellulolytic related enzymes.
β-1,4-xylanases randomly hydrolyze internal β-1,4-xylosidic bonds in the xylan backbone.They utilize general acid-base catalysis, with two glutamate residues serving as proton donors and acceptors.The hydrolysis can follow either retaining or inverting mechanisms depending on the stereochemistry of the active site.Exo-β-1,4-xylosidases release xylose units from the nonreducing end of xylooligomers.Retaining xylosidases use two aspartate residues as nucleophile and acid/base catalysts, while inverting xylosidases use glutamates as acid/ base residues.Acetyl xylan esterases catalyze the hydrolysis of ester linkages connecting acetyl decorations to the xylan backbone.They use a serine nucleophile in a charge relay system with histidine and aspartate for catalysis.Feruloyl esterases cleave ester bonds between ferulic acid and arabinose side chains, while coumaroyl esterases release p-coumaric acid.These esterases utilize a serine-histidineaspartate catalytic triad.Mannans contain β-1,4-linked mannopyranose backbones.Endo-β-1,4-mannanases randomly hydrolyze internal backbone glycosidic bonds using inverting acid-base mechanisms.Exo-β-1,4-mannosidases liberate mannose F I G U R E 3 Scheme of the enzymatic degradation of cellulose chain via synergistic interaction of cellulases.

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I G U R E 6 Schematic representation of enhancing efficacy of lignocellulose hydrolysis via artificial intelligence optimization.
Lignocellulosic biomass composition of the main agro-industrial residues on a dry basis.
T A B L E 1 Overview of fungi and enzymes involved in cellulose degradation.
T A B L E 3 Overview of lignin-degrading fungi and enzymes involved in lignin degradation.
A B L E 4