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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5586_Библиотеки_им_академика_М_И_Перельмана.pdf
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- •Acknowledgement
- •Author biographies
- •Professor Ahmed Al-Harrasi
- •Dr Saurabh Bhatia
- •Dr Ajmal Khan
- •1.1 Introduction
- •1.2 Properties of enzymes
- •1.3 Catalysis
- •1.4 The structure of enzymes
- •1.5 Structural features: primary and secondary structures
- •1.6 Nomenclature and classification
- •1.6.1 Class 1—oxidoreductase
- •1.6.2 Class 2—transferase
- •1.6.3 Class 3—hydrolases
- •1.6.4 Class 4—lyases
- •1.6.5 Class 5—isomerases
- •1.6.6 Class 6—ligases
- •1.7 The mechanism of action of enzymes
- •1.7.3 Covalent catalysis
- •1.8 Catalysis via chymotrypsin
- •1.8.1 Intermediary stages of chymotrypsin
- •1.8.2 Kinetic behavior of α-chymotrypsin
- •1.8.3 Selective proteolysis in creation of the catalytic sites of enzymes
- •1.8.4 Kinetic models for enzymes
- •1.8.5 Enzyme mediated acid–base (general) catalysis
- •1.8.6 Metallozymes
- •1.9 Enzyme inhibition
- •1.10 Pharmaceutical applications
- •1.10.1 Diagnostic applications of enzymes
- •1.10.2 Enzymes in therapeutics
- •1.11 Plants and algae enzyme systems
- •1.12 Enzyme safety
- •1.13 Enzyme structure determination
- •1.13.1 X-ray crystallography
- •1.13.2 NMR spectroscopy
- •1.13.3 Cryo-electron microscopy
- •1.14 Enzyme engineering and design
- •1.14.1 Directed evolution of enzymes
- •1.14.2 Rational design of enzymes
- •1.14.3 Applications of engineered enzymes
- •1.15 Enzymes in medicine and healthcare
- •1.15.1 Enzyme-targeted drug delivery
- •1.15.2 Enzymes as drug targets
- •1.15.3 Challenges and opportunities in enzyme drug discovery
- •1.15.4 Enzymes in gene therapy
- •1.15.5 Enzymes in personalized medicine
- •1.15.6 Enzyme biomarkers in disease diagnosis
- •1.15.7 Pharmacogenomics and enzyme variability
- •1.15.8 Enzyme-based therapies for personalized treatment
- •1.16 Enzymes in bioremediation
- •1.17 Enzymes in agriculture and crop production
- •1.18 Enzymes in waste management
- •References
- •2.1 Introduction
- •2.1.1 Sources of enzymes
- •2.2 Enzyme production technology
- •2.2.1 Selection of microorganisms
- •2.2.2 Medium selection
- •2.2.3 Production process
- •2.2.5 Cell debris removal
- •2.2.6 Nucleic acid removal
- •2.2.7 Precipitation of enzymes
- •2.2.8 Liquid–liquid partition
- •2.2.9 Chromatographic separation
- •2.2.10 Drying and packing
- •2.2.11 Regulation of microbial enzyme production
- •2.2.12 Induction
- •2.2.13 Feedback repression
- •2.2.14 Nutrient repression
- •2.3 Procedures involved in enzyme production
- •2.3.1 Source and location of enzymes
- •2.3.2 The variety of microorganisms
- •2.3.3 Media for fermentation
- •2.3.4 Fermentation
- •2.3.5 Enzyme extraction
- •2.3.7 Finishing operations
- •2.4 Recombinant proteins from algae
- •2.5 Enzyme immobilization techniques
- •2.5.1 Advantages and applications of enzyme immobilization
- •2.5.2 Methods of enzyme immobilization
- •2.6 Enzyme engineering for enhanced stability and activity
- •2.6.1 Protein engineering strategies
- •2.6.2 Improving enzyme thermostability
- •2.7 Upstream process intensification
- •2.7.1 High cell density fermentation
- •2.7.2 Solid-state fermentation
- •2.7.3 Continuous fermentation
- •2.7.4 Microbial consortia for enzyme production
- •2.7.5 In situ product removal strategies
- •2.8 Enzyme production from extreme environments
- •2.8.1 Psychrophiles (cold-loving)
- •2.9.4 Automation and robotics in downstream processing
- •References
- •2.8.2 Thermophiles (heat-loving)
- •2.8.3 Acidophiles (acid-loving)
- •2.8.4 Alkaliphiles (alkaline-loving)
- •2.8.5 Halophiles (salt-loving)
- •2.8.6 Applications of extremozymes in biotechnology
- •2.9 Downstream process intensification
- •2.9.1 Continuous chromatography
- •2.9.2 Process integration and optimization
- •3.1 Industrial enzymes
- •3.2 Bacterial α-amylases
- •3.3 Fungal α-amylases
- •3.4 Bacterial proteases
- •3.5 Fungal proteases
- •3.6 Glucose isomerase (d-xylose ketol-isomerase; EC. 5.3.1.5)
- •3.7 Penicillinase
- •3.8 Chloramphenicol acetyltransferase
- •3.9 Aminoglycoside antibiotic inactivating enzymes
- •3.10 Fibrinolytic enzymes
- •3.10.1 Streptokinase
- •3.10.2 Urokinase
- •3.10.3 Tissue plasminogen activator (t-PA)
- •3.11 Biotechnological applications of enzymes
- •3.11.1 Algae and plant research
- •3.11.2 Immobilization
- •3.12 Industrial enzymes
- •3.12.1 Glucoamylase
- •3.12.2 Cellulases
- •3.13 The role of enzymes in the synthesis of functional foods
- •3.13.1 Lipases
- •3.13.2 Proteases
- •3.13.3 Carbohydrate-modifying enzyme
- •3.13.4 Tannase
- •3.13.5 Asparaginase
- •3.13.6 The phytases
- •3.14 Enzymes used as additives to food
- •3.14.1 The enzymatic synthesis of dietary antioxidants
- •3.14.2 The use of ascorbyl esters
- •3.14.3 Polyphenolic esters
- •3.14.4 Synthesis of sugars esters surfactants by enzymes
- •References
- •4.1 Introduction
- •4.2 Types of immobilization
- •4.2.1 Surface immobilization by covalent coupling
- •4.2.2 Adsorption
- •4.2.3 Complexation and chelation
- •4.2.4 Within-support immobilization
- •4.2.5 Cell immobilization
- •4.2.6 Commercial production of enzymes
- •4.3 Genetic engineering for microbial enzyme production
- •4.3.1 Cloning methods
- •4.4 Protein studies for modification of commercial enzymes
- •4.5 Enzyme and cell immobilization
- •4.6 Immobilization methods
- •4.6.1 Adsorption methods
- •4.6.3 Ionic binding
- •4.6.4 Hydrophobic adsorption
- •4.6.6 Entrapment method
- •4.6.7 Covalent binding
- •4.6.8 Cross-linking
- •4.7 Choice of immobilization technique
- •4.7.1 Immobilization of l-amino acid acylase
- •4.7.2 Stabilization of soluble enzymes
- •4.8 Immobilization of cells
- •4.8.1 Immobilization of viable cells
- •4.8.2 Immobilized non-viable cells
- •4.8.3 Drawbacks of immobilizing eukaryotic cells
- •4.8.4 The effect of immobilization on enzyme properties
- •4.8.5 Immobilized enzyme reactors
- •4.8.6 Applications of immobilized enzymes and cells
- •4.9 Manufacture of commercial products
- •4.9.1 Production of l-amino acids
- •4.9.2 Production of high-fructose syrup
- •4.9.3 Immobilized enzyme and cell analytical applications
- •4.10 Immobilized enzymes for biomedical applications
- •4.11.1 Bioluminescence
- •4.11.2 The measurement of biomass using bioluminescence-based techniques
- •4.11.4 Biosensors relying on bioluminescence
- •4.12 Bioluminescence-based microbial biosensors
- •4.12.1 The microencapsulation process involves the utilization of polymers and cells
- •4.12.2 Microcapsule evaluation
- •4.12.4 Modern developments in cell encapsulation
- •4.13 Immobilization of microalgae
- •4.13.1 Techniques for immobilization
- •4.13.2 Use of cryopreserved algae
- •4.13.3 Removal of nitrogen and phosphorous
- •4.13.4 Disposal of metals
- •4.13.5 Biosensor development
- •References
- •5.1 Introduction
- •5.2 Principles of a biosensor
- •5.3 Different types of biosensors
- •5.3.1 Electrochemical biosensors
- •5.3.2 Thermometric biosensors
- •5.3.3 Optical biosensors
- •5.3.4 Piezoelectric biosensors
- •5.3.5 Whole-cell biosensors
- •5.3.6 Immunobiosensors
- •5.4 Applications of biosensors
- •5.4.1 Applications in medicine and health
- •5.4.2 Applications in industry
- •5.4.3 Applications in pollution control
- •5.4.4 Applications in the military
- •5.4.5 Immobilized enzymes and cell therapeutic applications
- •5.5 Recent advancements in biosensor technology
- •5.5.1 Electrochemical biosensors
- •5.5.2 Optical/visual biosensors
- •5.5.3 Silica, quartz/crystal, and glass biosensors
- •5.5.4 Nanomaterials-based biosensors
- •5.5.5 Fluorescent biosensors that are either genetically encoded or synthetic
- •5.7 Technological comparison of biosensors
- •5.9 Grand challenges in biosensors and biomolecular electronics
- •5.9.1 Sensitivity
- •5.9.2 Multiplex capability
- •5.9.3 Continuous monitoring in vivo
- •5.10.1 Sustainability to the ecosystem
- •References
- •6.1 Introduction
- •6.2 Types of biotransformation reactions
- •6.3 Sources of biocatalysts and techniques for biotransformation
- •6.3.1 Growing cells
- •6.3.2 Non-growing cells
- •6.3.3 Immobilized cells
- •6.3.4 Immobilized enzymes
- •6.4 Product recovery in biotransformations
- •6.5 Application of biotransformation in the production of pharmaceutical products
- •6.5.1 Biotransformation of steroids
- •6.5.2 Biotransformation of antibiotics
- •6.5.3 Biotransformation of arachidonic acid to prostaglandins
- •6.5.4 Biotransformation for the production of ascorbic acid
- •6.5.5 Biotransformation of glycerol to dihydroxyacetone
- •6.5.6 Biotransformation for the production of indigo
- •6.6 Mechanisms of enzyme action in biotransformation
- •6.6.1 Enzyme kinetics and biotransformation
- •6.6.2 Cofactors and coenzymes in biotransformation
- •6.6.3 Enzyme inhibition and activation
- •6.7 Biotransformation in environmental applications
- •6.7.1 Degradation of pollutants
- •6.7.2 Enzymatic breakdown of pesticides
- •6.8 Emerging technologies in biotransformation
- •6.8.1 Enzyme engineering and directed evolution
- •6.8.3 Biotransformation of lipids for healthy oils
- •6.9 Biotransformation challenges and future perspectives
- •6.9.1 Scalability issues in industrial applications
- •6.9.2 Regulatory and safety concerns
- •6.9.3 Challenges in enzyme storage and stability
- •6.9.4 Future trends and emerging areas of research
- •6.9.5 Biotransformation in biofuel production
- •6.9.6 Biotransformation in the cosmetic industry
- •6.9.7 Specialized enzyme systems: lignin-modifying enzymes in biotransformation
- •References
- •7.1 Introduction
- •7.2 Characterizations in genomics
- •7.3 Historical background
- •7.4 Genome sequencing
- •7.4.1 Clone-by-clone sequencing
- •7.4.2 Human whole-genome shotgun sequencing
- •7.4.3 Compilation of genome resources
- •7.5 Understanding bioinformatics and sequencing
- •7.6 Comparative genomics as a technique to understand evolution
- •7.6.2 Horizontal or lateral gene transfer
- •7.6.3 Genome similarity or homology
- •7.6.4 SNPs
- •7.6.5 Inferences from comparative genomics
- •7.6.6 Gene order comparisons (for phylogenetic inference)
- •7.6.7 Phylogenetic footprinting (computational method)
- •7.6.8 Origins, evolution and phenotypic impact of new genes
- •7.6.9 The concept of minimum genome size
- •7.6.10 Comparative genomics analysis of mitochondria and chloroplasts
- •7.7 Gene estimation and counting
- •7.7.1 Genome similarity, SNPs and comparative genomics
- •7.8 Genomes: genome evolution
- •7.8.1 Microbial genome reduction in bacteria
- •7.8.2 Role of duplications in the origin and evolution of the eukaryotic genome
- •7.8.3 Gene duplications increase genetic diversity and complexity
- •7.9 Algae bioinformatics
- •7.9.1 Scope of algae bioinformatics
- •7.9.2 What is involved in algae bioinformatics
- •7.9.3 Role of algae bioinformatics
- •7.9.4 Steps involved in obtaining the data for analysis using bioinformatics
- •7.10 Functional genomics
- •7.10.1 Introduction to functional genomics
- •7.10.2 Transcriptomics: studying the RNA molecules
- •7.10.3 Proteomics: understanding the world of proteins
- •7.10.4 Metabolomics: exploring cellular metabolites
- •7.10.5 Interactomics investigating protein–protein interactions
- •7.11 Structural genomics
- •7.11.1 Introduction to structural genomics
- •7.11.2 The approaches used in the domain of structural genomics
- •7.11.3 Importance of structural genomics in drug design
- •7.12 Epigenomics and epigenetics
- •7.12.1 Epigenetic inheritance and diseases
- •7.13 Pharmacogenomics
- •7.13.1 The importance of personalized medicine
- •7.13.2 The impact of genetic variations on drug response
- •7.13.3 Additional insights on pharmacogenomics
- •7.13.4 Pharmacogenomic tests in the market
- •7.13.5 Challenges in implementing pharmacogenomics
- •7.14 Population genomics
- •7.14.1 Studying genetic variation across populations
- •7.14.2 Population genomics techniques
- •7.14.3 Understanding human migration and evolution through population genomics
- •7.14.4 Conservation genomics in endangered species
- •7.15 Microbiome genomics
- •7.15.1 Introduction to the human microbiome
- •7.15.2 Techniques in studying microbial communities
- •7.15.3 Role of microbiome in human health and disease
- •7.15.4 Environmental microbiomes and their importance
- •7.16 Synthetic biology and genome editing
- •7.16.1 Techniques like CRISPR/Cas9 in genome editing
- •7.17 Systems biology and genomics
- •7.17.1 Integrative approaches in genomics
- •7.17.2 Modeling biological systems and networks
- •7.17.3 Challenges and opportunities in systems biology
- •7.18 Genome-wide association studies (GWAS)
- •7.18.1 Introduction to GWAS
- •7.18.2 Techniques and platforms for GWAS
- •7.18.3 Challenges in interpreting GWAS results
- •7.19 Future of genomics
- •7.19.1 Next-generation sequencing technologies
- •7.19.2 Ethical considerations in genomics research
- •7.19.3 The role of AI and machine learning in genomics
- •7.19.4 Personalized medicine and its potential impact
- •8.1 Introduction
- •8.2 Types of proteomics
- •8.2.1 Structural proteomics
- •8.2.2 Functional proteomics (strategy)
- •8.2.3 Expression proteomics
- •8.3 Basic techniques involved in proteomics
- •8.3.1 Sequence alignment (algorithms)
- •8.3.2 Protein structure (annotation resources)
- •8.3.3 Protein structural investigation
- •8.3.4 Two-dimensional gel electrophoresis in proteomics
- •8.3.5 Domain fusion method (or rosetta stone method)
- •8.4 Complete proteome of Mycoplasma genitalium
- •8.5 Architecture and design of the nuclear pore complex
- •8.6 Functional genomics and systems biology
- •8.6.2 Transcriptome, proteome and genomes
- •8.6.3 DNA arrays: a potential genomic tool
- •8.6.4 Gene function determination from sequence information
- •8.6.5 Protein interactions
- •8.7 Synthetic genomics
- •8.8 Advanced techniques in proteomics
- •8.8.1 Mass spectrometry in proteomics
- •8.8.2 Tandem mass spectrometry
- •8.8.3 Quantitative proteomics using mass spectrometry
- •8.8.4 Other advanced techniques in proteomics
- •8.8.5 Chromatography in proteomics
- •8.9 Proteogenomics
- •8.9.1 Proteogenomics role in precision medicine
- •8.10 Single-cell proteomics
- •8.10.1 Technologies enabling single-cell proteomics
- •8.11 Clinical and diagnostic proteomics
- •8.12 Metaproteomics
- •8.13 Emerging topics in proteomics
- •8.13.1 Data-independent acquisition (DIA)
- •8.13.2 Top-down proteomics
- •8.13.3 Targeted proteomics and selected reaction monitoring (SRM)
- •8.13.4 Proteomics in plant research
- •8.14 Ethical and data management issues in proteomics
- •8.14.1 Open-source platforms for proteomic analysis
- •8.15 Cellular and molecular dynamics
- •8.15.1 Molecular mechanisms of protein function
- •8.15.2 Protein degradation pathways
- •8.15.4 Cellular signaling pathways
- •8.15.5 Proteomic analysis of signaling networks
- •8.15.6 Signaling pathway dysregulation in disease
- •8.15.7 Targeting signaling pathways in drug discovery
- •8.15.8 Crosstalk between signaling pathways
- •8.16 Membrane proteomics
- •8.16.1 Techniques for membrane protein analysis
- •8.16.2 Membrane protein structure and function
- •8.16.3 Membrane proteins in disease
- •8.16.4 Drug targeting of membrane proteins
- •8.17 Subcellular proteomics
- •8.17.3 Proteomics of cellular compartments
- •8.17.4 Techniques for subcellular proteomic analysis
- •References
- •9.1 Introduction
- •9.2 History of bioinformatics
- •9.3 Sequences and nomenclature
- •9.3.1 DNA sequences
- •9.3.2 Amino acid sequences of proteins
- •9.3.3 Types of sequences in nucleotide sequence databases
- •9.3.4 Databases
- •9.3.5 Search engines and analysis tools
- •9.3.6 Various indian databases
- •9.4 Investigation by means of bioinformatics tools
- •9.4.4 Detection of noncoding RNA
- •9.4.5 Genome annotation
- •9.4.6 Molecular phylogenetics
- •9.5 Computational approaches in bioinformatics
- •9.5.1 Algorithm development
- •9.5.2 Phylogenetic tree construction algorithms
- •9.5.3 Machine learning algorithms in bioinformatics
- •9.5.4 High-performance computing (HPC) in bioinformatics
- •9.5.5 Cloud computing in genomics
- •9.5.6 GPGPU (general-purpose computing on graphics processing units)
- •9.5.7 Big data analytics in bioinformatics
- •9.5.8 Systems biology modelling
- •9.5.9 Systems pharmacology
- •9.5.10 Multiscale modeling
- •9.5.11 Computational genomics
- •9.5.12 Functional genomics
- •9.5.13 Comparative genomics
- •9.5.14 Epigenomics
- •9.5.15 Metagenomics
- •9.6 Bioinformatics in precision medicine
- •9.7 Translational bioinformatics
- •9.8 Bioinformatics in drug discovery and development
- •9.8.2 AI-driven drug discovery
- •9.9 CRISPR and genome editing in bioinformatics
- •9.10 Integrative and multi-omics analysis
- •References
- •10.1 Protein and enzyme engineering
- •10.2 Designing macromolecules
- •10.3 Protein engineering versus enzyme engineering
- •10.4 Protein engineering
- •10.5 Foundation of protein (enzyme) engineering
- •10.6 Basic assumptions for protein engineering
- •10.7 Steps involved in protein engineering
- •10.7.1 Studying three-dimensional protein structure
- •10.7.2 Protein modeling
- •10.7.3 Perturbation theory
- •10.8 Methods of protein engineering
- •10.9 Mutagenesis and selection of mutant enzymes
- •10.10 Gene modifications or gene synthesis for protein engineering
- •10.11 Multi-enzyme systems
- •10.12 Chemical modification of enzyme
- •10.13 Some early achievements of protein engineering
- •10.14 Computational approaches in protein engineering
- •10.14.1 Molecular dynamics simulations
- •10.14.2 Quantum mechanical calculations
- •10.14.3 Docking and ligand optimization
- •10.14.4 Machine learning algorithms in protein design
- •10.15 Directed evolution techniques
- •10.15.1 Error-prone PCR
- •10.15.3 Saturation mutagenesis
- •10.15.4 Phage display
- •10.16 Post-translational modifications
- •10.16.1 Glycosylation engineering
- •10.16.2 Phosphorylation engineering
- •10.16.3 Methylation and acetylation
- •10.16.4 PEGylation for enzyme stability
- •10.17 Structural flexibility and allosteric regulation
- •10.17.1 Intraprotein communication pathways
- •10.17.3 Modulator design
- •10.17.4 Coupling allosteric regulation with catalytic function
- •10.18 Protein–protein and protein–ligand interactions
- •10.18.1 Characterizing binding sites
- •10.18.3 Interaction networks
- •10.18.4 Biophysical methods for interaction studies
- •10.19 Applications in synthetic biology
- •10.19.1 Metabolic pathway engineering
- •10.19.2 Genetically encoded sensors
- •10.19.3 Protein-based logic gates
- •10.19.4 Gene circuits for dynamic control
- •10.20 Engineering multi-functional proteins
- •10.20.1 Fusion proteins
- •10.20.2 Protein scaffolds
- •10.20.3 Modular protein design
- •10.20.4 Dual-enzyme systems
- •10.21 Ethical and safety considerations
- •10.21.1 Bioethics in protein engineering
- •10.21.2 Biosafety and environmental concerns
- •10.21.3 Intellectual property rights
- •10.21.4 Regulatory frameworks
- •10.22 Studies in protein engineering
- •10.22.1 Therapeutic proteins
- •10.22.2 Industrial enzymes
- •10.22.3 Diagnostic proteins
- •10.23 Single-molecule techniques in protein engineering
- •10.23.1 Atomic force microscopy
- •10.23.2 Single-molecule FRET
- •10.23.3 Optical tweezers
- •10.23.4 Patch-clamp technique
- •10.24 High throughput screening methods
- •10.24.1 Fluorescence-activated cell sorting (FACS)
- •10.24.3 Yeast surface display
- •10.24.4 Mass spectrometry-based methods
- •10.25 Protein engineering for nanotechnology
- •10.25.1 Protein-based nanocarriers
- •10.25.2 Biosensors
- •10.25.3 Protein nanowires and nanotubes
- •10.25.4 DNA–protein hybrid structures

Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
The production of this drug in plants such as cucumber, one of the more common
vegetables in the world, could reduce its production costs. In a study it was
concluded that the presence of three expressions of regulatory factors (CaMV
35S, Kozak, NOS) and the KDEL signal in the construct caused the increase of t-PA
gene expression in cucumber plants [28]. In another study a protease-deficient strain
of Aspergillus niger was used as a host for the production of human tissue
plasminogen activator (t-PA). Production was increased (up to 1.9 mg t-PA (g
biomass)(−1)) by the addition of soy peptone to the defined medium [29]. However,
the total t-PA (detected by enzyme-linked immunoassay) also eventually disappeared from culture supernatants, confirming significant extracellular proteolytic
activity, even though the host strain was protease-deficient [29].
3.11 Biotechnological applications of enzymes
3.11.1 Algae and plant research
As mentioned above, there are various applications of enzymes in different
disciplines, however, some of the applications are restricted to plant discipline.
Recently, interest related with plant enzymes has augmented considerably. Plants
are considered as the main source for secondary metabolites. Plant-based enzymes
such as peroxidases are extensively used in medicine as diagnostic tools and in the
bioremediation and biobleaching industries, among others. Earlier these enzymes
were derived from a natural source, a process that is sometimes difficult and
influenced by environmental conditions and low yields. To prevent this obstacle,
some inputs have been made to develop plant cell cultures in vitro to use the system
as a continuous source of plant enzymes.
3.11.2 Immobilization
Different types of carriers and procedures have been implemented in the recent past
to improve traditional enzyme immobilization targeted to increase enzyme loading,
activity and stability to reduce the enzyme biocatalyst cost at large scale. These
include:
• recently nanoparticle-based immobilization of enzymes;
• microwave-assisted immobilization;
• mesoporous supports;
• cross-linked enzyme aggregates;
• click chemistry technology.
In nanotechnology method, mixture of the specific physical, chemical, optical and
electrical properties of nanoparticles, especially catalytic properties of biomolecules,
has resulted in the appearance of countless novel biotechnological applications.
Nanoparticles offer high surface-to-volume ratio resulting in an increase in the
concentration of the immobilized entity that is considerably higher than that
afforded by experimental protocols based on immobilization on planar 2D surfaces.
Enzymes immobilized on nanoparticles presented a broader working pH and
temperature range and higher thermal stability than the native enzymes. In contrast
3-18

Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
with traditional procedures, nanoparticle-based immobilization served three important features:
• nano-enzyme particles are easy to synthesize in high solid content without
using surfactants and toxic reagents;
• homogeneous and well-defined core–shell nanoparticles with a thick enzyme
shell can be obtained;
• particle size can be conveniently changed within utility limits. Moreover, with
the growing attention paid to cascade enzymatic reaction and in vitro
synthetic biology, it is possible that co-immobilization of multi-enzymes
could be achieved on these nanoparticles.
3.12 Industrial enzymes
3.12.1 Glucoamylase
Theoretically, glucoamylase (GA) can convert 100% of the starch in your diet into
glucose. As the chain length of the dextrin substrate gets shorter, the reaction rate
drops. To produce mostly maltose and isomaltose, GA can catalyze a reversal of the
typical hydrolysis reaction [30]. High quantities of sugars (up to 40%) can occur in
industrial processes, and these conditions favor some maltose production. The
saccharification of starch, brewing, and distilling are just a few examples of how GA
is used in the food and fermentation sectors. The production of glucose, fructose
syrups, and other sweeteners relies heavily on fungal GA [31]. It is common practice
in the food business to use an enzymatic technique to create high-glucose syrups, and
glucose may also play a crucial role as a substrate in fermentative processes that
generate by-products like ethanol, amino acids, and organic acids. Bread’s texture
and appearance can be enhanced by using GA because of its decrease in the dough’s
viscosity. In addition, researchers discovered use in manufacturing pharmaceutically
active gastrointestinal supplements [32]. The glucoamylase catalytic domain (CD) of
A. niger is shown in figure 3.6.
3.12.1.1 Classification of GA
When it comes to converting starch and pertaining to dextrin’s into glucose, Fleming
in 1968 classified GA into two groups: those that turn 80% of the starches and 40%
of the restrict dextrin’s into glucose, and those that convert just 80% of the starch.
However, both classes can convert panose and α-limit dextrins to glucose without
intermediate products. The rate of hydrolysis by the GA is determined by the
substrate’s molecular size, structure, bond position, and bond type. Substrate
pretreatment increases the hydrolysis rate and enhances product recovery.
Amylopectin, starch, amylose, maltodextrins, dextrin, malt sugar, isomaltose,
dextrin, panosian, oligo, di, and polysaccharides, etc, are all substrates for the
enzyme [33].
3.12.1.2 Sources
All living things, from plants to mammals, bacteria, fungi, and yeasts, produce GA.
The fungi Aspergillus, Rhizopus, and Endomyces species are responsible for most GA
3-19

Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
Figure 3.6. Structure of glucoamylase. The catalytic domain (CD) of Aspergillus niger GA (blue) to 487 (red)
residues. 13 α-helices, indicated as cartoon, are counted from N-terminus. The active-site-bound Tris (orange)
and glycerol (cyan) are highlighted.
production, this process is typically extracellular, and the enzyme could be retrieved
from culturing remains [34]. GA derived from Aspergillus awamori or A. niger is
widely used in manufacturing. In addition to Rhizopus, R. oryzae, R. niveus, R.
Delmar, and R. javanicus, several other Rhizopus species, are significant GA
producers. Some Penicillium species are also known to generate GA. In addition
to pigment, Monascus species can also generate GA. T. viride and a few other
Trichoderma species have also been reported to produce GA. GA has also been
traced back to several thermophilic fungi [35]. Thermomyces lanuginosus,
Scytalidium thermophilum, and Thermomucor 05 °C for 5–10 min, followed by 95
°C for 2 h. GA then sacrifice starch at a pH of 6.0 and 60 °C. If maltose is desired as
a by-product, only then will amylase be utilized in the saccharification process. To
increase the ratio of dextrose to maltose, pullulanase must be used. The benefits of
amylases have led to their replacement of acid hydrolysis of starch. Stamfordii,
Humicola grisea, Talaromyces flavus, and Streptosporangium spp. [30, 36],
Flavobacterium species, Bacillus stearothermophillus, Halobacterium sodamense,
Sclerotinia sclerotiorum, Lactobacillus-amylovorus, and Sclerotium-rolfsii are some
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Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
of the aerobic bacteria that are known to create GA. Other known producers of GA
include Sclerotinia sclerotiorum. Clostridium thermosaccharolyticum and Clostridium
thermohydrosulfuricum, two anaerobic bacteria, have also been implicated as GA
producers [30]. GA has been traced back to various yeast species, including Candida
antarctica, Saccharomyces’s fibuligera, Pichia sub-pelliculosa, and Saccharomyces
diasticus [37].
3.12.1.3 Production of GA
Submerged fermentation (SmF) has been the standard industrial method for
producing GA for many years; however, solid-state fermentation (SSF) has recently
been seen as a viable alternative. By-products encompass several substances such as
wheat bran, rice bran, rice husk, gram flour, flour, tea waste, wheat, corn flour, and
copra waste, among others [38]. Fragment size, humidity content, and liquid
endeavor of the substrate are significant determinants in enzyme synthesis in SSF.
Due to the significance of the plane zone on hydrolysis/growing rates, the substrate’s
atom dimension significantly impacts the development rate and enzyme-generating
action of the organisms [39]. The ability of oxygen to fill empty space is influenced by
the dimension of the particles. For substrates like wheat bran particles between 425
and 500 μm in size, dissolution improves with decreasing particle size because
smaller particles give greater surface area. [40]. GA activity was minimized on
surfaces with particles between 1.4 and 180 μm in size. Therefore, a trade-off must be
made when deciding on an appropriate particle size to improve mass transfer. The
efficient transport of both water and solutes along the cellular membrane is highly
contingent upon water-related activity present in the surrounding environment.
Greater GA yields were seen at higher initial substrate water activity values [41]. The
moisture content of the substrate used in SSF for GA production varies from about
50%–70% for starchy substrates (based on the chemical makeup of the substrate for
carbohydrates, cellulose, and so forth) to higher values (depending on the temperature used in the process). Adding a carbon source (easily accessible source) to the
substrates can sometimes boost culture activity [42]. It could be polymeric polymers
like starch or simple sugars like glucose, maltose, sucrose, etc. For instance, adding
com starch to a wheat bran medium usually results in increased GA synthesis by
fungal culture. Similarly, increasing GA yields in SSF has been achieved through the
successful application of substrate supplementation with exogenous nitrogen sources
(of either organic or inorganic form). A wide array of biological compounds,
spanning from basic ammonia or nitrates salt to the urea as well as intricate
substances such as steep liquid, can be employed as a source of nitrogen. The latter
method has been extensively employed in sustainable seafood farming for genetic
improvement and increased production of genetically advanced individuals [30].
Aspergillus sp. and A. niger produce more GA in SSF when the substrate, such as
wheat bran, is supplemented with fructose, ammonium sulfate, urea, and yeast
extract. Using urea instead of ammonium sulfate resulted in a 100% increase in GA
production by A. awamori, however, using C/P ratios between 5.1 and 28.7 did not
affect GA production. The ef
ficiency and impact of bioreactor construction on the
SSF processing of GA has been evaluated using the laboratory large-scale biological
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reactors such as Erlenmeyer flasks, Erlenmeyer trays, roux bottles, and the glass
columns. [43]. The A. niger enzyme synthesis was shown to be quicker in trays (36 h)
than in flasks (96 h). As was previously indicated, SmF has historically been
employed in GA manufacturing. The nutritional needs of SmF are typically more
nuanced than those of SSF. The formation of GA is profoundly affected by the
nature of the media used. The fermentation process requires a continual supply of
oxygen and a pH of −4.5, with starch as the carbon source [44]. High concentrations
of GA were generated by Rhizopus sp. A-11 in a liquor medium with added zinc and
calcium. The type of nitrogen and carbon sources, as well as the pH of the medium
and salts like K2HPO4 and KH2PO4, had a signifi cant impact on GA synthesis by
Thermomyces lanuginosus. Cultures of Streptosporangium sp. on starch-czapek
medium yielded a thermostable glucoamylase. pH 4.5 and 70 °C were optimal for
glucoamylase activity. To distinguish between the definite growing rate and the GA
production rate, Ricci Queiroz investigated GA making through A [45] Awamori to
develop the rheological parameter uniformity index (K) from the Power law. The
presence of oxygen is crucial during the synthesis of GA. The kinetic properties of a
fungal culture play a vital role in formulating a fermentation medium for the
industrial-scale synthesis of GA. The environmental conditions, including temperature, oxygen levels, pH, and other factors, can signifi cantly influence the kinetic
parameters of a fungal growth. The GA indicator and feed rate control were derived
from the specific glucose consumption rate. A feeding regimen for the cultivation
was devised utilizing the statistical regression model. The organism’s growth rate
exhibited a 34% increase when compared to the outcomes achieved under conditions
of a consistent feeding rate [46].
3.12.1.4 Genetic engineering of GA
Using recombinant DNA technology and genetic engineering has significantly
enhanced the production of innovative gene products. Now, gene modification is
the predominant approach utilized to produce microbial enzymes with
notable commercial importance [47]. Various genetic engineering strategies have
been utilized to enhance the production of the GA-generating strain, attain
thermodynamic stability of GA, and enhance selectivity in glucose production,
among other aims. Considerable research has been undertaken about the methodology of site-directed mutagenesis, which involves the intentional modification of
specific amino acid residues [48]. The combination of beneficial genetic changes has
led to a notable decrease, around 50%, in the enzyme’s ability to produce isomaltose.
Isomaltose is a key secondary product in the glucose production process facilitated
by the enzyme GA. Furthermore, incorporating these mutations has significantly
augmented the enzyme’s thermodynamic stability, resulting in a remarkable increase
of several orders of magnitude [49]. Moreover, there has been a significant increase
of 15% in enzyme activity. In addition, investigations using mutational analyses
have improved the catalytic efficiencies of the hydrolysis of maltose compared to the
hydrolysis of isomaltose. Reilly conducted a study on the improvements in genetic
algorithms achieved through protein engineering, focusing on the role of heterologous gene expression in these advancements [50
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]. The complementary DNA

Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
(cDNA) of R. oryzae GA was inserted into the area downstream of the alcohol
oxidase (AOD l) promoter obtained from Candida boidinii. The transformant C.
boidinii’s gibberellic acid was purified and analyzed next to the enzyme made by S.
cerevisiae. It was discovered that the enzyme that C. boidinii generated had a larger
molecular size than the enzyme produced by S. cerevisiae. The reason for this change
in molecular weight was that the proteins that were made had different N-linked
glycosylated sugar structures [51]. The study also showed how a hybrid yeast strain
was created that can make both GA and isoamylase enzymes. The achievement of
this conclusion was realized by the effective integration of the (Pseudomonas
amylodermosa) gene into a chromosome of the integrant G23-8. The recombinant
yeast reached a utilization rate of 95% for soluble starch. The employment of both
growth rate independent and dependent promoters can exemplify the phenomenon
of boosting the production of recombinant proteins is carried out in fed-batch
growing platforms. [52]. The point was successfully shown by using Fusarium
venanatin JeRS 325, a strain exhibiting transgenic GA expression according to the
control of an expansion rate distinct promoter. The strain was transformed by
utilizing a plasmid that carried the A. niger GA gene. This gene’s expression was
controlled by its promoter, which was linked to the strain’s growth rate [53]. When
grown in fed-batch cultures, the double transformant produced an amount of GA
comparable to that produced by the JeRS 325 strain. Recombinant yeast, namely S.
cerevisiae SR93, was bred and tested for its ability to produce GA. The overexpression of the GA gene can be traced back to the disruption of the MAT locus,
which caused a repressor protein to be produced [54]. As a result, there was a 1.6fold spike in GA activity per cellular concentration. The specific growth rate and
rate of GA synthesis were found to be significantly greater in comparison to S.
cerevisiae SR93. The gene responsible for producing a thermally stable GA (glycosyl
hydrolase) derived from Talaromyces emersonii was successfully cloned and then
expressed heterologously in the organism A. niger [55]. The gene under investigation
in this study encodes a protein consisting of six to eight amino acids, resulting in a
predicted molecular weight of 62 827 Da. T. emersonii GA is classified within the
glucoside hydrolase family 15, with a sequence similarity of around 60% to the GA
enzyme found in A. niger [56]. The enzyme under investigation exhibits significant
specificity towards maltose, isomaltose, and maltoheptaose, with a kcat value that is
3–6 times higher than that of the GA enzyme derived from A. niger. The
thermosensitivity of T. emersonii GA was greatly enhanced, exhibiting a half-life
of 48 h at 65 °C in a 30% (w/v) glucose solution [57
]. In comparison, GA derived
from A. niger had a half-life of just 10 h under the same conditions. The expression
of the catalytical domain (GAc) of A. awamori GA was reported in Pichia pastoris,
resulting in the production of GAc at a concentration of 0.4 g per liter of medium. In
an independent inquiry, the investigators analyzed the consequences of upregulating
and releasing a comparable prototype glycoprotein, GA the ‘GAM-1’ [58]. This
study investigates glycosylation patterns in A. niger, comparing a wild-type parent
strain with a single gene copy to transformants with numerous gene copies. The
strain of A. niger that was overexpressed exhibited an increased number of copies
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Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
Figure 3.7. Overview of recombinant DNA technology: restriction enzyme-mediated gene splicing and
plasmid vector insertion.
(20 and 80) of the glaA (GAM-1) gene, resulting in the secretion of a protein at levels
5–10 times higher than the normal strain [59](figure 3.7).
3.12.2 Cellulases
The yearly production of biomass in terrestrial and marine ecosystems on Earth is
estimated to be around 1.9 × 11
13
tons. Approximately 60% of the biomass consists
of lignocellulose, the primary structural component of plant cell walls.
Approximately 30%–60% of the composition of lignocellulose consists of cellulose.
Lignocellulose, specifically cellulose, represents a consistent and renewable reservoir
of power and feed stocks for many chemicals [60]. Cellulose is a structured
arrangement of lined ∼-1,4-D glucan chain up, while hemicelluloses encompass a
diverse range of substances, including xylenes, xyloglucans, arabinoxylans, and
mannans. These compounds form complicated branching structures with various
substituents near their backbone, such as acetyl ester. Hemicelluloses primarily
establish hydrogen bonds with cellulose and other hemicelluloses, stabilizing the cell
wall matrix and providing the cell wall with insolubility in aqueous conditions [61].
Hemicelluloses are found in plants. Cellulose comprises elongated, non-branching
glucose polymers that are densely arranged in a manner that gives rise to exceedingly
insoluble crystalline structures. Cellulolytic organisms with high enzymatic activity,
such as Trichoderma, can produce intricate combinations of essential enzymes for
effectively breaking down the substrate. The enzyme Endo and Exo glucanase and
cellobiohydrolases (CBH) are the main components of T. reesei’s extrinsic enzyme
framework, which also includes β-glucosidases and cellob [62]. Endoglucanases
exhibit specificity in hydrolyzing the internal 1,4-glycosidic linkages in celluloses that
are amorphous, swollen, and substituted. This enzymatic activity results in the
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Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
liberation of glucose, cellobiose, and cello-oligosaccharides. Endoglucanases facilitate the generation of additional chain ends for CBH by inducing random cleavage
inside the central region of long cellulose chains. In contrast to CBH, endoglucanases possess the ability to hydrolyze substituted celluloses, including carboxymethylcellulose (CMC) and hydroxyethyl cellulose (HEC). The exoglycanases, also
known as CBH, are enzymes that catalyze the cleavage of cellobiose units from the
terminals of polysaccharide chains. These enzymes are known to possess significant
activity on crystalline cellulose. On the other hand, glucosidases are responsible for
cleaving cellobiose and other soluble oligosaccharides into glucose [63]. This step is
crucial as cellobiose has been observed to hinder the effectiveness of various cellulase
components. In natural environments, organic matter containing lignocellulose
undergoes various physio-chemical and biological degradation processes, producing
simpler molecules. In animals, this lignocellulosic material is metabolized as a
primary nutrient in symbiotic relationships with microorganisms, such as those seen
in ruminants and termites. The lignocellulolytic enzyme complex serves as the agent
for this bioconversion process. The primary constituents of the lignocellulolytic
enzyme complexes consist of cellulases, hemicelluloses, pectinases, and other
enzymes involved in lignin degradation [64]. These enzymes engage in a symbiotic
connection during plant cell wall disintegration. Cellulases, a category of enzymes
that catalyze the hydrolysis of cellulose, have significant industrial importance.
Considerable focus has been directed towards the production and characteristics of
cellulases due to the significant role of cellulose as a primary structural element in
textiles, paper, and building materials, as well as its importance as a primary food
for ruminant animals. The degradation of cotton textiles utilized by the US Army
during tropical warfare in World War II was a significant catalyst for advancing
cellulase research. During the period spanning from 1950 to 2000, extensive research
efforts were dedicated to the description and characterization of the cellulase system
[65]. The most efficient microorganisms with high production capabilities have been
identified and subjected to genetic enhancements. The industrial-scale generation of
cellulase by SmF has been successfully achieved. Currently, cellulase preparations
are mostly targeted towards specialized markets characterized by low volume and
high value. These industries include food administration, fabric management, and
washing cleaners, which can accommodate the recent elevated cost of cellulase
enzymes. Cellulases find application in the food industry to extract and/or clarify
fruit and vegetable juices, treat wines, extract oils, and enhance the quality of baked
products. Cellulase is employed within the textile industry for bio-stoning and/or
fading denim, as well as for the polishing of cellulosic fabric. Cellulases have the
potential to be utilized in the recycling of wastepaper within the pulp and paper
sector [66]. The increasing demand for sources of renewable energy and the
imperative for responsible resource management has prompted a significant emphasis on cellulase development and manufacturing within the realm of agricultural and
biotechnological usage. This research primarily aims to convert lingo-cellulose into
solvable sugar, which can be further treated as biofuel or other biological and
chemical products. The primary objective of the industrial processing strategy for
lignocellulose is the process through which cellulose, hemicellulose, and pectin are
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Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
converted into reducing sugars by bioconversion. The aforementioned procedure
might potentially provide lignin as an additional item or need the improving
delignification of the feed stock prior to biological conversion. Consequently, the
key enzymes implicated in the process of bioconversion encompass cellulases,
hemicelluloses, and pectinases. Cellulase stands out as the most prominent enzyme
among the group of enzymes [67].
Cellulases and related enzymes have significant agro-biotechnological implications, particularly in the context of animal feed supplements. These enzymes play a
crucial role in enhancing the digestibility of lignocellulosic feed materials. The use of
agro-biotechnological applications necessitates the availability of more precise,
focused, specific targets that are more cost-effective than the enzymes presently
accessible [68]. Cellulases, hemicelluloses, and pectinases collectively constitute
around 20% of the global enzyme market. The phenomenon is expected to exhibit
substantial growth because of the heightened utilization of agrobiotechnology
solutions. Extensive reviews have been conducted in the past on cellular research
and production. This chapter focuses on current endeavors in economic cellulase
production, particularly evaluating submerged and solid-substrate fermentation
technologies. Additionally, the chapter explores the latest developments in developing highly efficient microbial strains for specific applications [69].
3.12.2.1 Sources
Numerous species, both marine and terrestrial, produce cellulases in their native
habitats. Commercial cellulase production relies primarily on filamentous fungi
found in various habitats, including soil, plants, and the ocean. Some microorganisms can potentially act as important sources for the production of cellulase
on a large scale [70]. Biochemical and enzyme studies of cellulases have historically
focused primarily on aerobic mesophilic fungi. Included in this group of molds were
Trichoderma viride, Sporotrichum pulverulentum, Trichoderma reesei, Penicillium
pinophilum, Trichoderma koningii, Fusarium solani, Penicillium funiculosum, and
Aspergillus niger [71].
Over the last 20 years, there has been an increasing acknowledgment of diverse
microorganisms that can synthesize cellulase and hemicellulase enzymes. The
microorganisms in question include thermophilic fungi, including Humicola insolens,
Chaetomium thermophilum, Talaromyces emersonii, Sporotrichum thermophile, and
Thermoascus aurantiacus. Furthermore, it has been observed that certain types of
anaerobic fungi, specifically N. patriciarum, Orpinomyces sp., Neocallimastix
frontalis, and Sphaeromonas communis, as well as aerobic bacteria of both
mesophilic and thermophilic nature, such as Bacillus spp., Cellulomonas fimi,
Cellvibrio sp., and Pseudomonas fluorescens subsp. cellulosa exhibit the presence
of these enzymatic systems [72
]. Furthermore, mesophilic and thermophilic anaerobic bacteria, including Fibrobacter succinogenes, R. flavefaciens Bacteroides
cellulosomes, Clostridium thermocellum, Ruminococcus albus, and Clostridium
stercorarium, have been identified as cellulase and hemicellulase producers. Lastly,
actinomycetes such as Microbispora bispora, Streptomyces flavogriseus, and
Thermomonospora fusca have also been observed to exhibit highly active cellulase
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Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
and hemicellulase systems. Furthermore, it is worth noting that hyperthermophilic
microbes, like T. neapolitana, Thermotoga maritema, Pyrococcus furiosus, and
Anaerocellum thermophilum microbial organisms that exhibit optimal growth within
the temperature range of 85 °C to 110 °C can synthesize cellulases and hemicelluloses with improved stability [73]. Typically, cellulases demonstrate comparatively reduced specific activity, frequently exhibiting a minimum of a 100-fold
decrease compared to amylases. The goal of the research study was to find wild types
that are more productive. After that, strain mutation was used to boost the specific
activity of enzymes and speed up the catalytic cycle rate at the active site. The first
modification program was done at the US Army Natick Laboratories using the wild
strain Trichoderma reesei. In the early 1970s, the mutant QM 9414 was effectively
isolated [74]. A research study was undertaken to isolate indigenous strains that
exhibit enhanced productivity. Following this, the introduction of strain mutation
has been employed to enhance the activity of enzymes and elevate the catalytic
turnover rate at the active site. The mutation experiment was carried out at the US
Army Natick Laboratory Services via the natural strain of Trichoderma reesei ‘QM
6a,’ formerly known as T. viride QM 6a. During this study, a mutant designated as
QM 9414 was successfully identified in the early 1970s. The use of UV light and
nitrosoguanidine resulted in mutagenesis [75]. Numerous T. reesei mutants, such as
Rut NG-14, that can produce cellulase despite considerable catabolite suppression,
have been identified and isolated using this method. The cellulolytic enzyme activity
of the mutant strain NG-14 was about three times greater than that of the wild-type
strain T. reesei QM 6a. After exposure to ultraviolet (UV) radiation, the strain
T. reesei Rut NG-14 was isolated, leading to the discovery of a new strain known as
T. reesei Rut C30. The cellulase production level of the mutant strain Rut C30
exhibited a significant increase, about 4–5 times greater, compared to the wild-type
parent strain QM 6a. Several other labs, including Cetus Corporation, VTT in
Finland, and a French laboratory, used the same procedures to identify other highly
cellulolytic T. reesei mutants [71]. The plate clearance technique has since been
successfully employed in isolating cellulase-producing mutants from Penicillium
pinophilum, P. occitanis, and P. purpurogenum. While the mutant strains of T. reesei
have shown an enhancement in the quantity of released protein, the relative
distribution of cellulase components has exhibited no variation compared to the
original strains [76]. Therefore, screening several mutant colonies on plates has
shown to be impractical for developing customized enzyme mixes for diverse
biotechnological applications. Genetically engineered T. reesei strains, capable of
producing modified combinations of cellulases, have been developed since the late
1980s. Novel strains have been developed by employing cloned
T. reesei genes and
their corresponding promoters, resulting in cellulase profiles that differ significantly
from the original strains [77]. These modified strains exhibit the absence of one or
more cellulase components. Additionally, the expression of fungal cellulase genes
can be regulated by the T. reesei promoter. These strategies facilitate the large-scale
manufacture of a certain cellulase component, often endoglucanase, while minimizing the presence of other components with substantial activity. Mono-component
enzymes exhibit a higher degree of selectivity in their actions, making them
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