Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5440_Библиотеки_им_академика_М_И_Перельмана.pdf
X
- •Computational Methods for Rational Drug Design
- •Contents
- •1.1.2.2 GROMACS
- •1.1.2.3 Amber
- •1.1.2.4 CHARMM
- •1.1.2.5 AutoDock
- •1.1.2.6 VMD
- •1.1.2.7 PyMOL
- •1.1.2.8 Open Babel
- •List of Contributors
- •Preface
- •1. Molecular Modeling and Drug Design
- •1.1 Introduction
- •1.1.1 What Is Molecular Modeling?
- •1.1.2 Software Used for Molecular Modeling
- •1.1.2.1 Schrodinger
- •1.1.2.9 Avogadro
- •1.1.2.10 Discovery Studio
- •1.1.3 Molecular Mechanics
- •1.1.3.1 Prediction of Binding Affinity
- •1.1.3.2 Conformational Analysis
- •1.1.3.3 Virtual Screening
- •1.1.3.4 Lead Discovery
- •1.1.3.5 Mechanism of Action
- •1.2 Types of Molecular Models
- •1.2.1 Ball-and-Spoke Model
- •1.2.1.1 Future Directions
- •1.2.2 Space-filling Models
- •1.2.2.1 Future Directions
- •1.2.3 Crystal Lattice Models
- •1.2.3.1 Future Directions
- •1.3 Computational Methods in Drug Discovery
- •1.3.1 What Is Drug Discovery?
- •1.3.2 Computational Platforms for Drug Discovery
- •1.3.2.1 NCBI
- •1.3.2.2 Chemical Databases
- •1.3.2.3 PDB
- •1.3.2.5 UniProt
- •1.3.2.6 QSAR
- •1.3.2.8 Desmond
- •1.3.2.9 OpenBabel
- •1.3.2.10 DeepChem and Cheminformatics for Python (RDKit)
- •1.3.2.11 SBML
- •1.3.2.12 Virtual Screening
- •1.3.3 Applications of Computer-Based Methods in Steps of Drug Discovery
- •1.4 Potential Use and Application of AI in Drug Designing
- •1.4.1 Target Identification and Validation
- •1.4.2 Drug Screening and Lead Optimization
- •1.4.3 De Novo Drug Design
- •1.4.4 Predictive Toxicology and ADMET
- •1.4.5 Clinical Trial Optimization
- •1.4.6 Drug Repurposing
- •1.4.7 Concept of Personalized Medicine
- •1.4.8 Drug Combination Optimization
- •1.5 Limitations of Current Methods
- •1.5.1 Data Restrictions
- •1.5.2 Interpretability
- •1.5.3 Generalization
- •1.5.4 Resources and Computation
- •1.5.5 Ethical Considerations
- •1.5.6 Validation and Experimentation
- •1.5.7 Regulatory Obstacles
- •1.6 Case Studies
- •1.7 Molecular Docking
- •1.7.1 What Is Molecular Docking?
- •1.7.1.1 Procedure
- •1.7.1.2 Biophysical Laws
- •1.7.1.3 Rigid and Flexible Docking
- •1.7.1.4 Types of Docking
- •1.7.1.5 Challenges and Future Perspectives
- •1.7.2 Applications of Molecular Docking in Drug Designing
- •1.7.3 Success of Molecular Docking Cases in Drug Designing
- •1.8 Conclusion and Future Works
- •References
- •2. Bioactive Small Molecules and Drug Discovery
- •2.1 Introduction
- •2.1.1 Introduction to Drug Design and Discovery
- •2.1.2 Brief History of Small-Molecule Drug Discovery
- •2.1.3 Importance of Bioactive Small Molecules in Drug Discovery
- •2.2.1 Structure-Based Methods
- •2.2.2 Ligand-Based Methods
- •2.2.3 Network-Based Methods
- •2.3 Natural Products in Bioactive Small-Molecule Discovery
- •2.3.1 Plant Primary and Secondary Molecules as Bioactive Molecules
- •2.3.2 Anticancer Agents as Bioactive Molecules
- •2.3.3 Antiviral Agents as Bioactive Molecules
- •2.3.4 Antimalarial Agents as Bioactive Molecules
- •2.6.6 Toxicity and Side Effects
- •2.6.7 Cost-Effectiveness, Synthetic Feasibility, and Scalability
- •2.6.8 Structural Diversity and Novelty
- •2.6.9 Patentability and Intellectual Property
- •2.3.5 Marine Bioactive Products
- •2.4.1 Importance of DFT in Small-Molecule Drug Discovery
- •2.5 Application of DFT to Bioactive Small Molecules
- •2.5.1 HOMO–LUMO Calculation
- •2.5.1.1 Molecular Electrostatic Potential (MEP) Map
- •2.5.1.3 Natural Bond Orbital (NBO) Analysis
- •2.5.1.4 Implementations and Tools
- •2.6.1 Target Identification and Validation
- •2.6.2 Target Specificity
- •2.6.3 Bioavailability and Pharmacokinetics
- •2.6.4 Chemical Structure and Drug-likeness
- •2.6.5 Safety and Toxicity
- •2.7 Conclusion
- •References
- •3. Novel Drug Targets for Small Molecule-based Drug Discovery
- •3.1 Introduction
- •3.2 Drug Target Identification
- •3.3 Classification of Novel Drug Targets
- •3.3.1 Transcription Factors
- •3.3.2 Cytokines
- •3.3.3 Chaperones
- •3.3.4 Viral Targets
- •3.3.5 G Protein-coupled Receptors
- •3.3.6 Transporters
- •3.3.7 Enzymes
- •3.3.8 RNA Targets
- •3.4 Small Molecules as Drugs
- •3.5 Conclusion
- •References
- •4.1 Introduction
- •4.2 Structure-Based Drug Discovery Concept
- •4.2.1 Structure Generation of the Target
- •4.2.1.1 The Detailed Description of Each Tool
- •4.2.2 Active Binding Site Within the Target
- •4.2.2.1 The Detailed Description of Each Tool
- •4.2.2.2 Molecular Docking Analysis
- •4.2.2.3 The Detailed Description of Each Tool
- •4.2.3 Molecular Dynamic Simulations
- •4.2.3.1 The Detailed Description of Each Tool
- •4.3 Ligand-Based Drug Discovery Concept
- •4.3.1.1 The Detailed Description of Each Tool
- •4.4 Structure- and Ligand-Based Assisted Studies
- •4.4.1 The Detailed Description of Each Tool
- •4.4.2 The Detailed Description of Each Tool
- •4.5 Advancement and Challenges in SBDD and LBDD
- •4.6 Conclusion
- •References
- •5. Virtual Screening and Lead Discovery
- •5.1 Introduction to Virtual Screening and Lead Discovery
- •5.1.1 Overview of Drug Discovery Process
- •5.1.2 Role of Virtual Screening
- •5.1.3 Importance of Lead Discovery
- •5.2 Molecular Targets and Biomolecular Structures
- •5.3 Virtual Screening Approaches
- •5.3.1 Structure-based Virtual Screening
- •5.3.2 Ligand-based Virtual Screening
- •5.3.3 Hybrid Approaches
- •5.4 Databases and Compound Collections
- •5.4.1 Overview of Chemical Databases
- •5.4.2 Compound Filtering and Preparation
- •5.4.3 Diversity and Size of Compound Collections
- •5.5 Molecular Docking
- •5.5.1 Principles of Molecular Docking
- •5.5.2 Docking Algorithms and Scoring Functions
- •5.5.3 Validation of Docking Results
- •5.6 Pharmacophore Modeling
- •5.6.1 Concept of Pharmacophores
- •5.6.2 Generating Pharmacophore Models
- •5.6.3 Applications in Lead Discovery
- •5.7 Quantitative Structure–Activity Relationship (QSAR)
- •5.7.1 Basics of QSAR
- •5.7.2 Model Development and Validation
- •5.7.3 QSAR in Virtual Screening
- •5.8 Machine Learning and AI in Virtual Screening
- •5.8.1 Introduction to Machine Learning and AI
- •5.8.2 Feature Selection and Model Training
- •5.8.3 Applications in Virtual Screening
- •5.9 Hit-to-Lead Optimization
- •5.9.1 Prioritizing Hits from Virtual Screening
- •5.9.2 SAR Analysis and Iterative Design
- •5.9.2.1 SAR Analysis (Structure–Activity Relationship)
- •5.9.2.2 Iterative Design
- •5.9.3 ADME/Tox Considerations
- •5.9.3.1 ADME (Absorption, Distribution, Metabolism, Excretion)
- •5.9.3.2 Toxicity Considerations
- •5.10 Case Studies and Examples
- •5.10.1 Exploration Protocol for Mutant-targeted PI3K Inhibitors
- •5.11 Challenges and Future Directions
- •5.11.1 Limitations of Virtual Screening
- •5.11.2 Emerging Technologies and Trends
- •5.11.3 Integration with High-throughput Experimentation
- •5.12 Ethical and Regulatory Considerations
- •5.12.1 Intellectual Property and Patents
- •5.12.2 Ethical Use of Computational Tools
- •5.12.3 Regulatory Approval Process
- •5.13 Conclusion
- •5.13.1 Future Prospects in Virtual Screening and Lead Discovery
- •5.13.2 Summary of Key Points
- •References
- •6. ADMET and Physicochemical Assessments in Drug Design
- •6.1 ADMET
- •6.1.1 Absorption
- •6.1.1.1 Solubility and Dissolution
- •6.1.1.2 Lipophilicity
- •6.1.1.3 Permeability
- •6.1.2 Distribution
- •6.1.3 Metabolism
- •6.1.4 Excretion
- •6.1.5 Toxicity
- •6.2 Physicochemical Assessments
- •6.2.1 Partition Coefficient
- •6.2.2 Log D: Ionizable Compound Lipophilicity
- •6.2.2.1 Methods for Calculating Lipophilicity
- •6.2.2.2 Direct Experimental Determination of Lipophilicity
- •6.2.2.3 Indirect Experimental Determination of Lipophilicity
- •6.2.3 Acid–Base Properties and Ionization
- •6.2.4 Solubility
- •6.2.5 Polymorphism
- •6.2.6 Molecular Weight
- •6.2.7 Number of Hydrogen Bond Donors (HDB) and Acceptors (HDA)
- •References
- •7. In Silico Modeling and Drug Design
- •7.1 Introduction
- •7.2 Target Identification
- •7.2.1 Experimental Approaches
- •7.2.2 Computational Target Identification
- •7.2.3 Target Validation
- •7.3 Computer-Aided Drug Design
- •7.3.1 Ligand-based CADD
- •7.3.2 Structure-Based CADD
- •7.4 ADMET Assessment
- •7.5 Conclusion
- •References
- •8. Pharmacophore Modeling in Drug Design
- •8.1 Introduction
- •8.1.1 The Role of Pharmacophore Modeling in Drug Design
- •8.1.2 Historical Perspective and Evolution of Pharmacophore Concepts
- •8.2 Essential Concepts in Pharmacophore Hypothesis Generation
- •8.2.1.1 Partitioning Initial Data into Distinctive Datasets
- •8.3 Diverse Approaches to Pharmacophore Modeling
- •8.3.1 Ligand-Based Pharmacophore Modeling
- •8.3.2 Structure-Based Pharmacophore Modeling
- •8.4 Application of Pharmacophore Modeling
- •8.4.1 Applications of Pharmacophore-Based Virtual Screening
- •8.4.1.1 Drug Discovery
- •8.4.2 Applications in Drug Target Fishing
- •8.4.3 Applications in Ligand Profiling
- •8.4.4 Applications in Docking
- •8.4.5 Applications in ADMET
- •8.4.6 Modulation of the Immune System
- •8.5 Emerging Trends in Pharmacophore Model Development
- •8.5.1 Involvement of Machine Learning
- •8.5.2 Prediction of Pharmacokinetic Properties
- •8.5.3 Structural Biology and Protein Functionality Studies
- •8.5.4 Integration with MDs Simulations
- •8.6 Case Studies
- •8.6.1 Case 1
- •8.6.2 Case 2
- •8.7 Challenges in Pharmacophore Modeling
- •8.8 Conclusion
- •Acknowledgments
- •References
- •9. Scaffold Hopping and De Novo Drug Design
- •9.1 Introduction
- •9.2 Scaffold Hopping
- •9.2.1 Classification of Scaffold Hopping
- •9.2.1.1 1° Hop: Heterocycle Replacement
- •9.2.1.2 2° Hop: Ring Opening and Closure: Pseudo Ring Structures
- •9.2.1.3 3° Hop: Pseudopeptides and Peptidomimetics
- •9.2.1.4 4° Hop: Topology/Shape-Based Scaffold Hopping
- •9.2.2 Advantages of Scaffold Hopping
- •9.2.3 Disadvantages of Scaffold Hopping
- •9.2.4 Reasons for Scaffold Hopping
- •9.2.5 Properties and Key Methods of Scaffold Hopping
- •9.3 De Novo Drug Design
- •9.3.1 Classification of De Novo Drug Design
- •9.3.1.1 Structure-based Drug Design
- •9.3.1.2 Ligand-based Drug Design
- •9.3.1.3 De Novo Design Strategies
- •9.3.1.4 Artificial Intelligence (AI) and Machine Learning-based Design
- •9.3.1.5 Hybrid Approaches
- •9.3.2 Basic Principle of De Novo Drug Design
- •9.3.3 Application of De Novo Drug Design
- •9.3.4 Historical Overview of Scaffold Hoping and De Novo Drug Design
- •9.3.5 Methodological Approaches in De Novo Drug Design
- •9.3.5.1 Structure-based De Novo Drug Design
- •9.3.5.2 Ligand-based De Novo Drug Design
- •9.3.5.3 Generation of Drug-Like Molecular Fragments
- •9.3.5.4 Similarity Searching
- •9.3.5.5 Selection of Target Reference Structure
- •9.3.5.6 Similarity Analysis of De Novo-generated Compounds
- •9.3.5.7 Evaluation of Scaffold Diversity
- •9.4 Results and Discussion
- •9.4.1 Generation of Drug-Like Molecular Fragments
- •9.4.2 De Novo Design with a Single Reference Structure
- •9.4.3 De Novo Design with a Focused Set of Five Similar Templates
- •9.4.4 De Novo Design with a Diverse Set of Five Templates
- •9.6 Case Study
- •9.6.1 De Novo Drug Design
- •9.6.2 Scaffold Hopping
- •9.7 Conclusion
- •References
- •10. Fragment-based Drug Design and Drug Discovery
- •10.1 Introduction
- •10.2 The Process of Finding Fragments
- •10.3 FBDD Strategies
- •10.4 Case Studies
- •10.5 Conclusion and Future Perspectives
- •References
- •11. AI/ML Approaches in Drug Design
- •11.1 Introduction
- •11.2 Traditional Drug Design Methods
- •11.2.1 The Rise of Computational Methods
- •11.2.2 The Importance of AI/ML in Modern Drug Design
- •11.3 AI/ML Landscape in Drug Design
- •11.3.1 AI/ML Algorithms and Methods
- •11.3.1.1 Machine Learning Models
- •11.3.1.2 Neural Networks
- •11.3.2 Applications in Drug Design
- •11.3.2.1 Peptide Synthesis
- •11.3.2.2 Molecular Design
- •11.3.2.3 Virtual Screening (VS)
- •11.3.2.4 Quantitative Structure–Activity Relationship Models
- •11.3.2.5 Drug Repurposing
- •11.3.3 Challenges and Failures
- •11.4 Ethics, Reliability, and Regulatory Issues
- •11.5 Future Directions
- •11.6 Conclusion
- •References
- •12. Network-based Methods in Drug Discovery
- •12.1 Introduction
- •12.1.1 Background of Drug Discovery Future Challenges
- •12.1.2 Single Target Approach Limitations
- •12.1.3 Emergence of Network Biology and Polypharmacology
- •12.2 Network Pharmacology: Practical Guide
- •12.2.1 Common Network Pharmacology Databases
- •12.2.1.1 Network Pharmacology-Related Databases and Data Analysis Tools
- •12.2.1.2 Exploring IMPPAT Network Pharmacology Databases
- •12.2.1.3 Target Genes of Phytoconstituents
- •12.2.2 Network Analysis and Visualization
- •12.2.3 Applications of Network Pharmacology in Drug Discovery
- •12.3 Ayurveda and Traditional Indian Medicine
- •12.3.1 Overview of Ayurveda and Its Complex Formulations
- •12.3.2 Diversity of Ingredients and Bioactive Compounds in Ayurvedic Medicines
- •12.4 Network Pharmacology in Herbal Remedies
- •12.4.1 Application of Network Pharmacology in Herbal Drug Discovery
- •12.4.1.1 Cancer
- •12.4.1.2 Cardiovascular Diseases (CVDs)
- •12.4.1.3 Diabetes Mellitus (DM)
- •12.4.2 Screening Pharmacological Efficacy of Herbal Remedies
- •12.4.3 Utilizing Network Pharmacology to Understand Complex Diseases
- •12.5 Conclusion and Future Prospects
- •References
- •13. Rational Design of Natural Products for Drug Discovery
- •13.1 Introduction
- •13.2 Natural Products for the Development of New Drugs
- •13.3 Criteria for Selecting Natural Products for Drug Design
- •13.4 Importance of Biodiversity in Sourcing Natural Products
- •13.5 Structural Elucidation of Natural Products
- •13.6.3 High-Throughput Screening Methods for Efficient Compound Selection
- •13.6.4 Molecular Dynamics Simulations for Predicting Solubility and Stability
- •13.6.5 ADMET Attributes Predicted In Silico
- •13.7 Formulation Challenges with Natural Products
- •13.8 Quality by Design (QbD) Approaches
- •13.8.1 Use of Computational Models for Formulation Optimization
- •13.9 Conclusion
- •References
- •14. Design of Enzyme Inhibitors in Drug Discovery
- •14.1 Introduction
- •14.3 Classification of Enzyme Inhibitors
- •14.3.1 Reversible Inhibitors
- •14.3.2 Irreversible Inhibitors
- •14.3.3 Competitive Inhibitors
- •14.3.4 Noncompetitive Inhibitors
- •14.3.5 Allosteric Modulators
- •14.4.1 Structure-Based Design
- •14.4.2 Computer-Aided Design
- •14.4.3 Fragment-Based Design
- •14.4.4 Virtual Screening Method
- •14.4.4.1 Ligand Based
- •14.4.4.2 Receptor Based
- •14.4.5 Natural Product-Based Discovery
- •14.4.6 Using Iterative Protein Crystallographic Analysis
- •14.4.7 Utilization of Covalent Inhibitors
- •14.4.8 Encapsulation Techniques
- •14.4.9 Based on Active-Site Specificity
- •14.4.10 Machine Learning Inhibitor Design
- •14.4.11 Enzyme-Templated Dynamic Combinatorial Chemistry
- •14.5 Limitations and Challenges
- •14.6 Future Directions
- •14.7 Conclusion
- •References
- •15.1 Introduction
- •15.2 Peptides as Therapeutics
- •15.2.1 Peptide Antibiotics
- •15.2.1.1 Peptides in Bone Diseases
- •15.2.1.2 Peptides in Cancer
- •15.2.1.3 Peptides in Metabolic Diseases
- •15.2.1.4 Peptides in Gastrointestinal Diseases
- •15.2.2 Advantages and Limitations of Peptide Therapeutics
- •15.2.3 FDA-Approved Peptide Therapeutics
- •15.2.4 Peptide-Based Entities in Clinical Trials
- •15.2.5 Peptide Synthesis and Diversification
- •15.2.5.1 Chemical Synthesis of Peptides
- •15.2.5.2 Chemical Modification of Peptide and Peptidomimetics
- •15.2.5.3 Backbone Modification of Peptides
- •15.2.5.4 Side-Chain Modification of Peptides
- •15.2.5.5 Peptide Cyclization
- •15.2.5.6 Peptide Mimicking of α-Helices and Stabilization
- •15.2.5.7 Peptide Mimicking of β-Strands and β-Sheets
- •15.2.5.8 Peptide Production by Recombinant Technology
- •15.2.5.9 Peptides Modification by Genetic Code Expansion
- •15.2.5.10 PEGylation of Peptides and Proteins
- •15.3 New Technologies for Peptide-Based Drug Discovery
- •15.3.1 Phage Display
- •15.3.2 mRNA Display
- •15.3.3 DNA-Encoded Libraries
- •15.3.4 Cell-Penetrating Peptides
- •15.3.5 Macrocyclic Peptides
- •15.4 Computational Approaches in Peptide Drug Discovery
- •15.5 Conclusion
- •References
- •16. Rational Design of Drugs for Neurodegenerative Disorders
- •16.1 Introduction
- •16.2 Common Mechanism of Neurodegeneration
- •16.3 Brief Overview of Computational Methods in Drug Design
- •16.4 Parkinson’s Disease as Prevalent Neurodegenerative Disorder
- •16.4.1 Epidemiology of Parkinson’s Disease
- •16.4.2 Pathogenesis of PD
- •1) Accumulation of Lewy bodies in substantia nigra
- •2) Mitochondrial dysfunction
- •3) Genetic factors
- •4) Neuroinflammation
- •5) Impaired protein handling
- •6) Oxidative stress
- •7) Environmental toxins
- •16.4.3 Signaling Pathway of Parkinson’s Disease
- •1) DA signaling
- •2) MAPK/ERK pathway
- •3) PI3K/Akt/mTOR pathway
- •4) Wnt/β-catenin pathway
- •5) NF-κB (nuclear factor-κB) pathway
- •6) Autophagy-lysosomal pathway
- •7) JNK (c-Jun N-terminal kinase) pathway
- •8) AMPK (AMP-activated protein kinase) pathway
- •9) Nrf2 (nuclear factor erythroid 2-related factor 2) pathway
- •16.4.4 Enzymatic Targets in Parkinson’s Disease
- •1) MAO-B (monoamine oxidase B)
- •2) COMT (catechol-O-methyltransferase)
- •3) LRRK2
- •4) GCase (glucocerebrosidase)
- •5) PARP-1 [poly(ADP-ribose) polymerase-1]
- •6) PINK1
- •7) DJ-1 (Parkinson protein 7)
- •8) Nrf2
- •16.4.5 Current Therapeutic Approaches to Treat PD
- •1) Drugs to treat motor symptoms of PD
- •2) Drugs to treat non-motor symptoms of PD
- •3) Disease-modifying therapies to treat PD
- •16.4.6 Current Therapeutic Challenges to Treat Parkinson’s disease
- •1) Symptomatic relief only
- •2) Motor fluctuations and dyskinesias
- •3) Limited efficacy in nonmotor symptoms
- •4) Disease progression
- •5) Side effects
- •6) Limited treatment options for advanced PD
- •7) Individual variability
- •16.4.7 Unmet Needs in Parkinson’s Disease Therapeutics
- •16.4.8 Significance of Computational Approaches in Parkinson’s Disease
- •16.4.9 Use of Computational Tools in Identifying Biomarkers
- •16.4.10 Neuroprotective Strategies Through Computational Insights
- •16.4.10.1 Computational Models for Neuroprotection
- •1) Target identification and validation
- •2) Drug repurposing
- •3) Alpha-synuclein aggregation inhibitors
- •4) Deep learning in biomarker discovery
- •5) Personalized medicine
- •6) Drug-induced neuroprotection
- •7) Optimizing clinical trials
- •1) ML and AI-based diagnostics
- •2) Wearable technology integration
- •3) Multimodal data fusion
- •4) Predictive modeling of disease progression
- •5) Network analysis of brain connectivity
- •6) Personalized treatment optimization
- •7) Data sharing and collaboration platforms
- •16.5 Conclusion
- •References
- •17. Rational Design of Anti-inflammatory Therapeutics
- •17.1 Introduction
- •17.2 Navigating Inflammation and its Microenvironment
- •17.2.1 Inflammatory Cell Infiltration and Vascular Permeability
- •17.2.2 Acidosis
- •17.2.3 Increased Oxidative Stress in Tissues
- •17.3 The Demand for Advanced Anti-inflammatory Medications
- •17.5 Rational Design of Anti-inflammatory Agents
- •17.5.2 New Anti-inflammatory Agent with Indoyl-imidazole Hybrids
- •17.5.3 Rational Design of Novel Aminopiperidinyl Amide
- •17.5.4 Lipid Nanoparticles (LNPs) as Anti-inflammatory Agents
- •17.6 Conclusion and Future Perspectives
- •Authors’ Contribution
- •References
- •18.1 Introduction
- •18.2 Treatment
- •18.3 Antibacterial Resistance
- •18.3.1 Mutation
- •18.3.2 Horizontal Gene Transfer (HGT)
- •18.3.3 Enzymatic Modification or Degradation
- •18.3.4 Target Site Modification
- •18.3.5 Decreased Permeability
- •18.3.6 Efflux Pumps
- •18.3.7 Plasmids
- •18.3.8 Transposons
- •18.3.9 Gene Amplification
- •18.3.10 Formation of Biofilms
- •18.3.11 Modified Metabolic Pathways
- •18.3.12 Adaptive Evolution
- •18.4.1 Structure- Based Drug Design
- •18.4.2 Modification of Existing Antibiotics
- •18.4.3 Bioisosterism
- •18.4.4 Prodrug Strategies
- •18.4.5 Similar Bacterial Components Target
- •18.4.6 Combine or Combination Therapy
- •18.4.7 Drug Repurposing
- •18.4.8 Resistant Mechanism Blocking
- •18.4.9 Improving Drug Delivery by Nanotechnology
- •18.4.10 Phage Intervention
- •18.4.11 Host Targeting
- •18.4.12 CRISPR-Cas Technique
- •18.4.13 Peptides as Antibacterials
- •18.4.14 Immunizations and Immunotherapy
- •18.4.15 Natural Product Derivatives
- •18.4.16 Fragment- Based Drug Discovery (FBDD)
- •18.4.17 Metabolomics and Genetics
- •18.4.18 Cheminformatics
- •18.5 Summary and Conclusion
- •References
- •19. Rational Design of Antiviral Therapeutics
- •19.1 Introduction to Antiviral Therapeutics
- •19.1.1 Overview
- •19.1.2 Blueprints for Antiviral Drug Interventions
- •19.1.2.1 Protein Folding and Binding Sites
- •19.1.2.2 Conformational Changes
- •19.1.2.3 Protein–Protein Interactions (PPIs)
- •19.1.2.4 Capsid and Envelope Structures
- •19.1.2.5 Structural Vulnerabilities
- •19.1.2.6 Enzymatic Activities
- •19.1.2.7 Viral Attachment
- •19.1.2.8 Viral Assembly and Replication Machinery
- •19.1.2.9 The Host’s Immune Response
- •19.2 Targets for Antiviral Therapeutics and Inhibition Strategies
- •19.2.1 Enzyme Inhibitors
- •19.2.2 Antiviral Peptides
- •19.2.3 Antiviral Antibodies
- •19.2.4 Lipid-Mimicking Compounds
- •19.2.5 Vaccines
- •19.2.6 Immunomodulation
- •19.3 Rational Strategies for Antiviral Therapeutics
- •19.3.1 CADD and QSAR (Quantitative Structure–Activity Relationship)
- •19.3.2 AI and ML
- •19.3.3 Systems Biology and Network Pharmacology
- •19.3.4 CRISPR Systems
- •19.3.5 Nanotechnology-Based Design and Delivery Systems
- •19.3.6 Reverse Vaccinology
- •19.4 Conclusion
- •References
- •20. Rational Design of Anticancer Therapeutics
- •20.1 Introduction
- •20.2 Rational Design of Nanomedicine for Cancer Treatment
- •20.4.1 Particle Size
- •20.4.2 Shape
- •20.4.3 Surface Modification
- •20.6 Artificial Intelligence’s Progress in Anticancer Drug Development
- •20.6.1 Identification of Anticancer Drug Targets Using Artificial Intelligence
- •20.6.3 Artificial Intelligence-Based De Novo Anticancer Drug Design
- •20.6.4 Artificial Intelligence for Repurposing Anticancer Drugs
- •20.7 Conclusion
- •References
- •21. PROTAC and ProTide Strategies in Drug Design
- •21.1 Introduction
- •21.2 Drug Design: Past to Present
- •21.3 PROTAC Strategy in Drug Design
- •21.3.1 Ubiquitin Proteasome System and PROTACs
- •21.3.2 Chemical Formulations of PROTACs
- •21.3.3 Advent of PROTACs as Antiviral
- •21.3.4 NS3/4A-Targeting PROTACs Against HCV
- •21.3.4.1 Neuraminidase-Targeting PROTACs
- •21.4 Emergence of ProTide Technology in Drug Design
- •21.5 Approaches of ProTides in Drug Development
- •21.6 Implementation of ProTides as Nucleoside Analogs
- •21.6.1 Antiviral Applications of ProTides
- •21.7 Conclusion
- •References

References 353
42 Strosberg, J., El-Haddad, G., Wolin, E. et al. (2017). Phase 3 trial of (177) Lu-dotatate for midgut
neuroendocrine tumors. The New England Journal of Medicine 376: 125–135.
43 Tagawa, S.T., Vallabhajosula, S., Christos, P.J. et al. (2019). Phase 1/2 study of fractionated
dose lutetium-177-labeled anti-prostate-specific membrane antigen monoclonal antibody
J591 ((177) LuJ591) for metastatic castration-resistant prostate cancer. Cancer 125:
2561–2569.
44 Escala Cornejo, R.A., García-Talavera, P., Navarro Martin, M. et al. (2018). Large cell
neuroendocrine carcinoma of the lung with atypical evolution and a remarkable response to
lutetium Lu 177 dotatate. Annals of Nuclear Medicine 32: 568–572.
45 Hagimori, M., Fuchigami, Y., and Kawakami, S. (2017). Peptide-based cancer-targeted DDS and
molecular imaging. Chemical & Pharmaceutical Bulletin (Tokyo) 65: 618–624.
46 Chen, Y., Wu, J.J., and Huang, L. (2010). Nanoparticles targeted with NGR motif deliver c-myc
siRNA and doxorubicin for anticancer therapy. Molecular Therapy 18: 828–834.
47 Ruoslahti, E. (2017). Tumor penetrating peptides for improved drug delivery. Advanced Drug
Delivery Reviews 110–111: 3–12.
48 Kotsakis, A., Papadimitraki, E., Vetsika, E.K. et al. (2014). A phase II trial evaluating the clinical
and immunologic response of HLA-A2(+) non-small cell lung cancer patients vaccinated with an
hTERT cryptic peptide. Lung Cancer 86: 59–66.
49 Kumai, T., Kobayashi, H., Harabuchi, Y., and Celis, E. (2017). Peptide vaccines in cancer-old
concept revisited. Current Opinion in Immunology 45: 1–7.
50 Tsoras, A.N. and Champion, J.A. (2018). Cross-linked peptide nanoclusters for delivery of oncofetal
antigen as a cancer vaccine. Bioconjugate Chemistry 29: 776–785.
51 Boohaker, R.J., Sambandam, V., Segura, I. et al. (2018). Rational design and development of a
peptide inhibitor for the PD-1/PD-L1 interaction. Cancer Letters 434: 11–21.
52 Zhou, K., Lu, J., Yin, X. et al. (2019). Structure-based derivation and intramolecular cyclization of
peptide inhibitors from PD-1/PD-L1 complex interface as immune checkpoint blockade for breast
cancer immunotherapy. Biophysical Chemistry 253: 106213.
53 Abbas, A.B., Lin, B., Liu, C. et al. (2019). Design and synthesis of A PD-1 binding peptide
and evaluation of its anti-tumor activity. International Journal of Molecular Sciences
20: 572.
54 Sasikumar, P.G., Ramachandra, R.K., Adurthi, S. et al. (2019). A rationally designed peptide
antagonist of the PD-1 signaling pathway as an immunomodulatory agent for cancer therapy.
Molecular Cancer Therapeutics 18: 1081–1091.
55 Mahadevappa, R., Ma, R., and Kwok, H.F. (2017). Venom peptides: improving specificity in cancer
therapy. Trends Cancer 3: 611–614.
56 Okada, M., Ortiz, E., Corzo, G., and Possani, L.D. (2019). Pore-forming spider venom peptides
show cytotoxicity to hyperpolarized cancer cells expressing K+ channels: a lentiviral vector
approach. PLoS One 14: e0215391.
57 Ghaly, G., Tallima, H., Dabbish, E. et al. (2023). Anti-cancer peptides: status and future prospects.
Molecules 28 (3): 1148.
58 Strzelecka, P., Czaplinska, D., Sadej, R. et al. (2017). Simplified, serine-rich theta-defensin
analogues as antitumour peptides. Chemical Biology & Drug Design 90: 52–63.
59 Rodbard, H.W. (2018). The clinical impact of GLP-1 receptor agonists in type 2 diabetes: focus on
the long-acting analogs. Diabetes Technology & Therapeutics 20: S233–S241.
60 George, C., Byun, A., and Howard-Thompson, A. (2018). New injectable agents for the treatment
of type 2 diabetes part 2-glucagon-like peptide-1 (GLP-1) agonists. The American Journal of
Medicine 131: 1304–1306.

354
61 Tonneijck, L., Muskiet, M.H., Blijdorp, C.J. et al. (2019). Renal tubular effects of prolonged therapy
with the GLP-1 receptor agonist lixisenatide in patients with type 2 diabetes mellitus. American
Journal of Physiology – Renal Physiology 316: F231–F240.
62 von Scholten, B.J., Hansen, T.W., Goetze, J.P. et al. (2015). Glucagon-like peptide 1 receptor agonist
(GLP-1 RA): long-term effect on kidney function in patients with type 2 diabetes. Journal of
Diabetes and Its Complications 29: 670–674.
63 Greco, E.V., Russo, G., Giandalia, A. et al. (2019). GLP-1 receptor agonists and kidney protection.
Medicina (Kaunas, Lithuania) 55: 233.
64 Correa, T.D., Takala, J., and Jakob, S.M. (2015). Angiotensin II in septic shock. Critical Care
19 (98): 302.
65 Montone, C.M. et al. (2018). Peptidomic strategy for purification and identification of potential
ACE-inhibitory and antioxidant peptides in Tetradesmus obliquus microalgae. Analytical and
Bioanalytical Chemistry 410: 3573–3586.
66 Liao, W., Fan, H., Davidge, S.T., and Wu, J. (2019). Egg white-derived antihypertensive peptide IRW
(Ile-Arg-Trp) reduces blood pressure in spontaneously hypertensive rats via the ACE2/Ang (1-7)/
Mas receptor axis. Molecular Nutrition & Food Research 63: e1900063.
67 Kuwahara, K. (2021). The natriuretic peptide system in heart failure: diagnostic and therapeutic
implications. Pharmacology & Therapeutics 227: 107863.
68 Burnett, J.C. Jr. (2019). Atrial natriuretic peptide, heart failure and the heart as an endocrine organ.
Clinical Chemistry 65: 1602–1603.
69 Shah, S.J. and Teerlink, J.R. (2007). Nesiritide: a reappraisal of efficacy and safety. Expert Opinion
on Pharmacotherapy 8: 361–369.
70 Sackner-Bernstein, J.D., Kowalski, M., Fox, M., and Aaronson, K. (2005). Short-term risk of death
after treatment with nesiritide for decompensated heart failure: a pooled analysis of randomized
controlled trials. JAMA 293: 1900–1905.
71 Forte, M., Madonna, M., Schiavon, S. et al. (2019). Cardiovascular pleiotropic effects of natriuretic
peptides. International Journal of Molecular Sciences 20: 3874.
72 Ichiki, T., Dzhoyashvili, N., and Burnett, J.C. Jr. (2019). Natriuretic peptide based therapeutics for
heart failure: Cenderitide: a novel first-in-class designer natriuretic peptide. International Journal
of Cardiology 281: 166–171.
73 Lee, C.Y., Chen, H.H., Lisy, O. et al. (2009). Pharmacodynamics of a novel designer natriuretic
peptide, CDNP, in a first-in-human clinical trial in healthy subjects. Journal of Clinical
Pharmacology 49: 668–673.
74 Duggan, K.A., Hodge, G., Chen, J., and Hunter, T. (2019). Vasoactive intestinal peptide infusion
reverses existing myocardial fibrosis in the rat. European Journal of Pharmacology 862: 172629.
75 Dong, Y., Bai, Y., Zhang, S. et al. (2019). Cyclic peptide RD808 reduces myocardial injury induced
by beta1-adrenoreceptor autoantibodies. Heart and Vessels 34: 1040–1051.
76 Gao, H.R. and Gao, H.Y. (2019). Cardiovascular functions of central corticotropin-releasing factor
related peptides system. Neuropeptides 75: 18–24.
77 Barchetta, I., Ciccarelli, G., Barone, E. et al. (2019). Greater circulating DPP4 activity is associated
with impaired flow-mediated dilatation in adults with type 2 diabetes mellitus. Nutrition,
Metabolism, and Cardiovascular Diseases 29: 1087–1094.
78 Holani, R., Shah, C., Haji, Q. et al. (2016). Proline-arginine rich (PR-39) cathelicidin: structure,
expression and functional implication in intestinal health. Comparative Immunology, Microbiology
and Infectious Diseases 49: 95–101.
79 Jeppesen, P.B. (2015). Gut hormones in the treatment of short-bowel syndrome and intestinal
failure. Current Opinion in Endocrinology, Diabetes, and Obesity 22 (1): 14–20.

References 355
80 Billiauws, L., Bataille, J., Boehm, V. et al. (2017). Teduglutide for treatment of adult patients with
short bowel syndrome. Expert Opinion on Biological Therapy 17 (5): 623–632.
81 Lim, D.W., Levesque, C.L., Vine, D.F. et al. (2017). Synergy of glucagon-like peptide-2 and epidermal
growth factor coadministration on intestinal adaptation in neonatal piglets with short bowel
syndrome. American Journal of Physiology. Gastrointestinal and Liver Physiology 312: G390–G404.
82 Hong, J., Zhang, P., Yoon, I.N. et al. (2017). The American cockroach peptide periplanetasin-2
blocks Clostridium difficile toxin a-induced cell damage and inflammation in the gut. Journal of
Microbiology and Biotechnology 27: 694–700.
83 Shrestha, A., Robertson, S.L., Garcia, J. et al. (2014). A synthetic peptide corresponding to the
extracellular loop 2 region of claudin-4 protects against Clostridium perfringens enterotoxin in vitro
and in vivo. Infection and Immunity 82: 4778–4788.
84 Marin, M., Holani, R., Blyth, G.A. et al. (2019). Human cathelicidin improves colonic epithelial
defenses against Salmonella typhimurium by modulating bacterial invasion, TLR4 and
proinflammatory cytokines. Cell and Tissue Research 376: 433–442.
85 Smith, A.J. (2015). New horizons in therapeutic antibody discovery: opportunities and challenges
versus small-molecule therapeutics. Journal of Biomolecular Screening 20: 437–453.
86 Räder, A.F., Weinmüller, M., Reichart, F. et al. (2018). Orally active peptides: is there a magic
bullet? Angewandte Chemie International Edition 57 (44): 14414–14438.
87 Merrifield, R.B. (1963). Solid phase peptide synthesis. I. The synthesis of a tetrapeptide. Journal of
the American Chemical Society 85 (14): 2149–2154.
88 Hou, W., Zhang, X., and Liu, C.F. (2017). Progress in chemical synthesis of peptides and proteins.
Transactions of Tianjin University 23: 401–419.
89 Behrendt, R., White, P., and Offer, J. (2016). Advances in Fmoc solid-phase peptide synthesis.
Journal of Peptide Science 22 (1): 4–27.
90 Weidmann, J., Dimitrijević, E., Hoheisel, J.D., and Dawson, P.E. (2016). Boc-SPPS: compatible
linker for the synthesis of peptide o-aminoanilides. Organic Letters 18 (2): 164–167.
91 Wołczański, G. and Lisowski, M. (2018). A general method for preparation of N-Boc-protected or
N-Fmoc-protected α, β-didehydropeptide building blocks and their use in the solid-phase peptide
synthesis. Journal of Peptide Science 24 (8–9): e3091.
92 Puentes, A.R., Morejón, M.C., Rivera, D.G., and Wessjohann, L.A. (2017). Peptide macrocyclization
assisted by traceless turn inducers derived from Ugi peptide ligation with cleavable and resin-
linked amines. Organic Letters 19 (15): 4022–4025.
93 Coin, I., Beyermann, M., and Bienert, M. (2007). Solid-phase peptide synthesis: from standard
procedures to the synthesis of difficult sequences. Nature Protocols 2 (12): 3247–3256.
94 García-Martín, F., Quintanar-Audelo, M., García-Ramos, Y. et al. (2006). ChemMatrix, a poly
(ethylene glycol)-based support for the solid-phase synthesis of complex peptides. Journal of
Combinatorial Chemistry 8 (2): 213–220.
95 Pedersen, S.L., Tofteng, A.P., Malik, L., and Jensen, K.J. (2012). Microwave heating in solid-phase
peptide synthesis. Chemical Society Reviews 41 (5): 1826–1844.
96 Paradís-Bas, M., Tulla-Puche, J., and Albericio, F. (2016). The road to the synthesis of “difficult
peptides”. Chemical Society Reviews 45 (3): 631–654.
97 Palasek, S.A., Cox, Z.J., and Collins, J.M. (2007). Limiting racemization and aspartimide formation
in microwave-enhanced Fmoc solid phase peptide synthesis. Journal of Peptide Science 13:
143–148.
98 Cardona, V., Eberle, I., Barthélémy, S. et al. (2008). Application of Dmb-dipeptides in the Fmoc
SPPS of difficult and aspartimide-prone sequences. International Journal of Peptide Research and
Therapeutics 14: 285–292.

356
99 Michels, T., Dölling, R., Haberkorn, U., and Mier, W. (2012). Acid-mediated prevention of
aspartimide formation in solid phase peptide synthesis. Organic Letters 14 (20): 5218–5221.
100 Subirós Funosas, R., El Faham, A., and Albericio, F. (2012). Use of Oxyma as pH modulatory agent
to be used in the prevention of base driven side reactions and its effect on 2-chlorotrityl chloride
resin. Peptide Science 98 (2): 89–97.
101 Hojo, H. and Aimoto, S. (1991). Polypeptide synthesis using the S-alkyl thioester of a partially
protected peptide segment. Synthesis of the DNA-binding domain of c-Myb protein
(142–193)-NH2. Bulletin of the Chemical Society of Japan 64 (1): 111–117.
102 Aimoto, S. (2001). Contemporary methods for peptide and protein synthesis. Current Organic
Chemistry 5 (1): 45–87.
103 Schnölzer, M. and Kent, S.B. (1992). Constructing proteins by dove-tailing unprotected synthetic
peptides: backbone-engineered HIV protease. Science 256 (5054): 221–225.
104 Rose, K. (1994). Facile synthesis of homogeneous artificial proteins. Journal of the American
Chemical Society 116 (1): 30–33.
105 Fisch, I., Kunzi, G., Rose, K., and Offord, R.E. (1992). Site-specific modification of a fragment of a
chimeric monoclonal antibody using reverse proteolysis. Bioconjugate Chemistry 3 (2): 147–153.
106 Liu, C.F. and Tam, J.P. (1994). Chemical ligation approach to form a peptide bond between
unprotected peptide segments. Concept and model study. Journal of the American Chemical
Society 116 (10): 4149–4153.
107 Dawson, P.E., Muir, T.W., Clark-Lewis, I., and Kent, S.B. (1994). Synthesis of proteins by native
chemical ligation. Science 266 (5186): 776–779.
108 Liu, C.F., Rao, C., and Tam, J.P. (1996). Acyl disulfide-mediated intramolecular acylation for
orthogonal coupling between unprotected peptide segments. Mechanism and application.
Tetrahedron Letters 37 (7): 933–936.
109 Muir, T.W., Sondhi, D., and Cole, P.A. (1998). Expressed protein ligation: a general method for
protein engineering. National Academy of Sciences of the United States of America 95 (12): 6705–6710.
110 Shigenaga, A., Sumikawa, Y., Tsuda, S. et al. (2010). Sequential native chemical ligation utilizing
peptide thioacids derived from newly developed Fmoc-based synthetic method. Tetrahedron
66 (18): 3290–3296.
111 Varela, Y.F., Vanegas Murcia, M., and Patarroyo, M.E. (2018). Synthetic evaluation of standard
and microwave-assisted solid phase peptide synthesis of a long chimeric peptide derived from
four plasmodium falciparum proteins. Molecules 23 (11): 2877.
112 Hansen, A.M., Bonke, G., Hogendorf, W.F. etal. (2019). Microwave-assisted solid-phase synthesis
of antisense acpP peptide nucleic acid-peptide conjugates active against colistin- and tigecycline
resistant E. coli and K. pneumoniae. European Journal of Medicinal Chemistry 168: 134–145.
113 Goodwin, D., Simerska, P., and Toth, I. (2012). Peptides as therapeutics with enhanced bioactivity.
Current Medicinal Chemistry 19: 4451–4461.
114 Jamieson, A.G., Boutard, N., Sabatino, D., and Lubell, W.D. (2013). Peptide scanning for studying
structure-activity relationships in drug discovery. Chemical Biology & Drug Design 81: 148–165.
115 Eustache, S., Leprince, J., and Tuffery, P. (2016). Progress with peptide scanning to study
structure-activity relationships: the implications for drug discovery. Expert Opinion on Drug
Discovery 11: 771–784.
116 Pelay-Gimeno, M., Glas, A., Koch, O., and Grossmann, T.N. (2015). Structure-based design of
inhibitors of protein–protein interactions: mimicking peptide binding epitopes. Angewandte
Chemie, International Edition 54 (31): 8896–8927.
117 Noisier, A.F. and Brimble, M.A. (2014). C–H functionalization in the synthesis of amino acids
and peptides. Chemical Reviews 114 (18): 8775–8806.

References 357
118 Boto, A., González, C.C., Hernández, D. et al. (2021). Site-selective modification of peptide
backbones. Organic Chemistry Frontiers 8: 6720–6759.
119 Zuo, C., Tang, S., Si, Y.Y. et al. (2016). Efficient synthesis of longer Aβ peptides via removable
backbone modification. Organic & Biomolecular Chemistry 14 (22): 5012–5018.
120 Zheng, J.S., Yu, M., Qi, Y.K. et al. (2014). Expedient total synthesis of small to medium-sized
membrane proteins via Fmoc chemistry. Journal of the American Chemical Society 136 (9): 3695–
3704.
121 Weinstock, M.T., Francis, J.N., Redman, J.S., and Kay, M.S. (2012). Protease-resistant peptide
design—empowering nature’s fragile warriors against HIV. Peptide Science 98 (5): 431–442.
122 Glas, A., Bier, D., Hahne, G. et al. (2014). Constrained peptides with target-adapted cross-links as
inhibitors of a pathogenic protein–protein interaction. Angewandte Chemie, International Edition
53 (9): 2489–2493.
123 Cheloha, R.W., Maeda, A., Dean, T. et al. (2014). Backbone modification of a polypeptide drug
alters duration of action in vivo. Nature Biotechnology 32 (7): 653–655.
124 Werner, H.M., Cabalteja, C.C., and Horne, W.S. (2016). Peptide backbone composition and
protease susceptibility: impact of modification type, position, and tandem substitution.
ChemBioChem 17 (8): 712–718.
125 Urban, J., Vaisar, T., Shen, R., and Lee, M.S. (1996). Lability of N-alkylated peptides towards TFA
cleavage. International Journal of Peptide and Protein Research 47:182–189.
126 Maybauer, M.O., Maybauer, D.M., Enkhbaatar, P. et al. (2014). The selective vasopressin type 1a
receptor agonist selepressin (FE 202158) blocks vascular leak in ovine severe sepsis. Critical Care
Medicine 42: e525–e533.
127 Räder, A.F.B., Reichart, F., Weinmüller, M., and Kessler, H. (2018). Improving oral bioavailability
of cyclic peptides by N-methylation. Bioorganic & Medicinal Chemistry 26 (10): 2766–2773.
128 Henninot, A., Collins, J.C., and Nuss, J.M. (2018). The current state of peptide drug discovery:
back to the future? Journal of Medicinal Chemistry 61 (4): 1382–1414.
129 Hone, A.J., Fisher, F., Christensen, S. et al. (2019). PeIA-5466: a novel peptide antagonist
containing non-natural amino acids that selectively targets alpha 3 beta 2 nicotinic acetylcholine
receptors. Journal of Medicinal Chemistry 62: 6262–6275.
130 Masri, E., Ahsanullah, Accorsi, M., and Rademann, J. (2020). Side-chain modification of peptides
using a phosphoranylidene amino acid. Organic Letters 22: 2976–2980.
131 Del Olmo-Garcia, M.I. and Merino-Torres, J.F. (2018). GLP-1 receptor agonists and cardiovascular
disease in patients with type 2 diabetes. Journal Diabetes Research 2018: 4020492.
132 Wang, L., Wang, N., Zhang, W. et al. (2022). Therapeutic peptides: current applications and future
directions. Signal Transduction and Targeted Therapy 7 (1): 48.
133 Hayes, H.C., Luk, L.Y.P., and Tsai, Y.H. (2021). Approaches for peptide and protein cyclisation.
Organic & Biomolecular Chemistry 19 (18): 3983–4001. https://doi.org/10.1039/d1ob00411e.
PMID: 33978044; PMCID: PMC8114279.
134 Jin, K., Sam, I.H., Po, K.H. et al. (2016). Total synthesis of teixobactin. Nature Communications
7 (1): 12394.
135 Ollivier, N., Toupy, T., Hartkoorn, R.C. et al. (2018). Accelerated microfluidic native chemical
ligation at difficult amino acids toward cyclic peptides. Nature Communications 9 (1): 2847.
136 Lam, H.Y., Zhang, Y., Liu, H. et al. (2013). Total synthesis of daptomycin by cyclization via a
chemoselective serine ligation. Journal of the American Chemical Society 135 (16): 6272–6279.
137 Wong, C.T., Lam, H.Y., and Li, X. (2013). Effective synthesis of kynurenine-containing peptides
via on-resin ozonolysis of tryptophan residues: synthesis of cyclomontanin B. Organic &
Biomolecular Chemistry 11 (43): 7616–7620.

358
138 Liu, H. and Li, X. (2018). Serine/threonine ligation: origin, mechanistic aspects, and applications.
Accounts of Chemical Research 51 (7): 1643–1655.
139 Adebomi, V., Cohen, R.D., Wills, R. et al. (2019). CyClick chemistry for the synthesis of cyclic
peptides. Angewandte Chemie 131 (52): 19249–19256.
140 Soellner, M.B., Tam, A., and Raines, R.T. (2006). Staudinger ligation of peptides at non-glycyl
residues. The Journal of Organic Chemistry 71 (26): 9824–9830.
141 Bode, J.W., Fox, R.M., and Baucom, K.D. (2006). Chemoselective amide ligations by
decarboxylative condensations of N-alkylhydroxylamines and α-ketoacids. Angewandte Chemie
International Edition 45 (8): 1248–1252.
142 Zheng, X., Li, Z., Gao, W. et al. (2020). Condensation of 2-((alkylthio)(aryl) methylene)
malononitrile with 1,2-aminothiol as a novel bioorthogonal reaction for site-specific protein
modification and peptide cyclization. Journal of the American Chemical Society 142 (11): 5097–
5103.
143 Bottecchia, C. and Noël, T. (2019). Photocatalytic modification of amino acids, peptides, and
proteins. Chemistry – A European Journal 25 (1): 26–42.
144 Patel, S.G., Sayers, E.J., He, L. etal. (2019). Cell-penetrating peptide sequence and modification
dependent uptake and subcellular distribution of green florescent protein in different cell lines.
Scientific Reports 9 (1): 6298.
145 Li, X., Chen, S., Zhang, W.D., and Hu, H.G. (2020). Stapled helical peptides bearing different
anchoring residues. Chemical Reviews 120 (18): 10079–10144.
146 Weeks, A.M. and Wells, J.A. (2019). Subtiligase-catalyzed peptide ligation. Chemical Reviews
120 (6): 3127–3160.
147 Dai, X., Böker, A., and Glebe, U. (2019). Broadening the scope of sortagging. RSC Advances 9 (9):
4700–4721.
148 Touati, J., Angelini, A., Hinner, M.J., and Heinis, C. (2011). Enzymatic cyclisation of peptides
with a transglutaminase. ChemBioChem 12 (1): 38–42.
149 Shah, N.H., Dann, G.P., Vila-Perelló, M. et al. (2012). Ultrafast protein splicing is common among
cyanobacterial split inteins: implications for protein engineering. Journal of the American
Chemical Society 134 (28): 11338–11341.
150 Zakeri, B., Fierer, J.O., Celik, E. et al. (2012). Peptide tag forming a rapid covalent bond to a
protein, through engineering a bacterial adhesin. National Academy of Sciences of the United
States of America 109 (12): E690–E697.
151 Veggiani, G., Nakamura, T., Brenner, M.D. et al. (2016). Programmable polyproteams built using
twin peptide superglues. National Academy of Sciences of the United States of America 113 (5):
1202–1207.
152 Jochim, A.L. and Arora, P.S. (2010). Systematic analysis of helical protein interfaces reveals
targets for synthetic inhibitors. ACS Chemical Biology 5: 919–923.
153 Sawyer, N., Watkins, A.M., and Arora, P.S. (2017). Protein domain mimics as modulators of
protein–protein interactions. Accounts of Chemical Research 50: 1313–1322.
154 Khoo, K.K. et al. (2011). Lactam-stabilized helical analogues of the analgesic muconotoxin
KIIIA. Journal of Medicinal Chemistry 54: 7558–7566.
155 Galande, A.K. et al. (2004). Thioether side chain cyclization for helical peptide formation: inhibitors
of estrogen receptor–coactivator interactions. The Journal of Peptide Research 63: 297–302.
156 Muppidi, A., Zhang, H., Curreli, F. et al. (2014). Design of antiviral stapled peptides containing a
biphenyl cross-linker. Bioorganic & Medicinal Chemistry Letters 24: 1748–1751.
157 Reguera, L. and Rivera, D.G. (2019). Multicomponent reaction toolbox for peptide
macrocyclization and stapling. Chemical Reviews 119 (17): 9836–9860.

References 359
158 Cheng, P.N., Liu, C., Zhao, M. et al. (2012). Amyloid beta-sheet mimics that antagonize protein
aggregation and reduce amyloid toxicity. Nature Chemistry 4: 927–933.
159 Banerjee, V., Shani, T., Katzman, B. et al. (2016). Superoxide dismutase 1 (SOD1)-derived peptide
inhibits amyloid aggregation of familial amyotrophic lateral sclerosis SOD1 mutants. ACS
Chemical Neuroscience 7 (11): 1595–1606.
160 Ryan, P., Patel, B., Makwana, V. et al. (2018). Peptides, peptidomimetics, and carbohydrate–
peptide conjugates as amyloidogenic aggregation inhibitors for Alzheimer’s disease. ACS
Chemical Neuroscience 9 (7): 1530–1551.
161 Mishra, N.K., Joshi, K.B., and Verma, S. (2013). Inhibition of human and bovine insulin fibril
formation by designed peptide conjugates. Molecular Pharmaceutics 10 (10): 3903–3912.
162 Hanold, L.E., Oruganty, K., Ton, N.T. et al. (2015). Inhibiting EGFR dimerization using triazolyl
bridged dimerization arm mimics. PLoS One 10 (3): e0118796.
163 Hopping, G., Kellock, J., Caughey, B., and Daggett, V. (2013). Designed Trpzip-3 β-hairpin inhibits
amyloid formation in two different amyloid systems. ACS Medicinal Chemistry Letters 4 (9):
824–828.
164 Dufau, L., Marques Ressurreição, A.S., Fanelli, R. et al. (2012). Carbonylhydrazide-based
molecular tongs inhibit wild-type and mutated HIV-1 protease dimerization. Journal of Medicinal
Chemistry 55 (15): 6762–6775.
165 Mirecka, E.A., Shaykhalishahi, H., Gauhar, A. et al. (2014). Sequestration of a β-hairpin for
control of α-synuclein aggregation. Angewandte Chemie International Edition 53 (16): 4227–4230.
166 Wibowo, D. and Zhao, C. (2019). Recent achievements and perspectives for large-scale
recombinant production of antimicrobial peptides. Applied Microbiology and Biotechnology 103:
659–671.
167 Rosano, G.L., Morales, E.S., and Ceccarelli, E.A. (2019). New tools for recombinant protein
production in Escherichia coli: a 5-year update. Protein Science 28 (8): 1412–1422.
168 Itakura, K. et al. (1977). Expression in Escherichia coli of a chemically synthesized gene for the
hormone somatostatin. Science 198: 1056–1063.
169 Johnson, I.S. (1983). Human insulin from recombinant DNA technology. Science 219: 632–637.
170 Wang, L. (2017). Engineering the genetic code in cells and animals: biological considerations and
impacts. Accounts of Chemical Research 50: 2767–2775.
171 Oller-Salvia, B. and Chin, J.W. (2019). Efficient phage display with multiple distinct noncanonical
amino acids using orthogonal ribosome-mediated genetic code expansion. Angewandte Chemie
(International Ed. in English) 58: 10844–10848.
172 Valentini, T.D., Lucas, S.K., Binder, K.A. et al. (2020). Bioorthogonal non-canonical amino acid
tagging reveals translationally active subpopulations of the cystic fibrosis lung microbiota. Nature
Communications 11: 2287.
173 Liu, J., Hemphill, J., Samanta, S. et al. (2017). Genetic code expansion in zebrafish embryos and
its application to optical control of cell signaling. Journal of the American Chemical Society
139: 9100–9103.
174 Brown, W., Liu, J., and Deiters, A. (2018). Genetic code expansion in animals. ACS Chemical
Biology 13: 2375–2386.
175 Liu, S., Fan, L., Sun, J. et al. (2017). Computational resources and tools for antimicrobial peptides.
Journal of Peptide Science 23 (1): 4–12.
176 Fok, J.A. and Mayer, C. (2020). Genetic-code-expansion strategies for vaccine development.
ChemBioChem 21: 3291–3300.
177 Veronese, F.M. and Mero, A. (2008). The impact of PEGylation on biological therapies. BioDrugs
22: 315–329.

360
178 Swierczewska, M., Han, H.S., Kim, K. et al. (2016). Polysaccharide-based nanoparticles for
theranostic nanomedicine. Advanced Drug Delivery Reviews 99: 70–84.
179 Santos, J.H., Carretero, G., Ventura, S.P. et al. (2019). PEGylation as an efficient tool to enhance
cytochrome c thermostability: a kinetic and thermodynamic study. Journal of Materials Chemistry
B 7 (28): 4432–4439.
180 Gupta, V., Bhavanasi, S., Quadir, M. et al. (2019). Protein PEGylation for cancer therapy: bench to
bedside. Journal of Cell Communication and Signaling 13: 319–330.
181 Zhang, C., Yang, X.L., Yuan, Y.H. et al. (2012). Site-specific PEGylation of therapeutic proteins via
optimization of both accessible reactive amino acid residues and PEG derivatives. BioDrugs
26: 209–215.
182 Alconcel, S.N., Baas, A.S., and Maynard, H.D. (2011). FDA-approved poly(ethylene glycol)–protein
conjugate drugs. Polymer Chemistry 2 (7): 1442–1448.
183 Schiavon, O., Caliceti, P., Ferruti, P., and Veronese, F.M. (2000). Therapeutic proteins: a
comparison of chemical and biological properties of uricase conjugated to linear or branched
poly(ethylene glycol) and poly(N-acryloylmorpholine). Il Farmaco 55 (4): 264–269.
184 Xiaojiao, S., Corbett, B., Macdonald, B. et al. (2016). Modeling and optimization of protein
pegylation. Industrial and Engineering Chemistry Research 55 (45): 11785–11794.
185 Chiu, K., Agoubi, L.L., Lee, I. et al. (2010). Effects of polymer molecular weight on the size,
activity, and stability of PEG-functionalized trypsin. Biomacromolecules 11 (12): 3688–3692.
186 Roberts, M.J., Bentley, M.D., and Harris, J.M. (2012). Chemistry for peptide and protein
PEGylation. Advanced Drug Delivery Reviews 54 (4): 459–476. https://doi.org/10.1016/j.addr.
2012.09.025.
187 Belén, L.H., Rangel-Yagui, C.D., Beltrán Lissabet, J.F. et al. (2019). From synthesis to
characterization of site-selective PEGylated proteins. Frontiers in Pharmacology 10: 1450.
188 Ameringer, T., Ercole, F., Tsang, K.M. et al. (2013). Surface grafting of electrospun fibers using
ATRP and RAFT for the control of biointerfacial interactions. Biointerphases 8 (1). 189
Wallat, J.D., Rose, K.A., and Pokorski, J.K. (2014). Proteins as substrates for controlled radical
polymerization. Polymer Chemistry 5 (5): 1545–1558.
190 Tucker, B.S., Coughlin, M.L., Figg, C.A., and Sumerlin, B.S. (2017). Grafting-from proteins using
metal-free PET–RAFT polymerizations under mild visible-light irradiation. ACS Macro Letters
6 (4): 452–457.
191 Zhang, B., Xu, H., Chen, J. et al. (2015). Development of next generation of therapeutic IFN-α2b
via genetic code expansion. Acta Biomaterialia 19: 100–111.
192 Wu, L., Chen, J., Wu, Y. et al. (2017). Precise and combinatorial PEGylation generates a low
immunogenic and stable form of human growth hormone. Journal of Controlled Release 249: 84–93.
193 Fu, C., Chen, Q., Zheng, F. et al. (2019). Genetically encoding a lipidated amino acid for extension
of protein half-life in vivo. Angewandte Chemie 131 (5): 1406–1410.
194 Wang, H.H., Altun, B., Nwe, K., and Tsourkas, A. (2017). Proximity-based sortasemediated
ligation. Angewandte Chemie, International Edition 56 (19): 5349–5352. https://doi.org/10.1002/
anie.201701419.
195 Jaroszewicz, W., Morcinek-Orłowska, J., Pierzynowska, K. et al. (2022). Phage display and other
peptide display technologies. FEMS Microbiology Reviews 46 (2): fuab052.
196 Marintcheva, B. (2017). Harnessing the Power of Viruses. London: Academic Press. 197
Bábíčková, J., Tóthová, Ľ., Boor, P., and Celec, P. (2013). In vivo phage display: a discovery tool in
molecular biomedicine. Biotechnology Advances 31 (8): 1247–1259.
198 Norman, A., Franck, C., Christie, M. et al. (2021). Discovery of cyclic peptide ligands to the
SARS-CoV-2 spike protein using mRNA display. ACS Central Science 7: 1001–1008.

References 361
199 Yamagishi, Y., Shoji, I., Miyagawa, S. et al. (2011). Natural product-like macrocyclic N-methyl-
peptide inhibitors against a ubiquitin ligase uncovered from a ribosome-expressed de novo library.
Chemistry & Biology 18: 1562–1570.
200 Saito, M., Itoh, Y., Yasui, F. et al. (2021). Macrocyclic peptides exhibit antiviral effects against
influenza virus HA and prevent pneumonia in animal models. Nature Communications 12: 2654.
201 Zimmermann, G. and Neri, D. (2016). DNA-encoded chemical libraries: foundations and
applications in lead discovery. Drug Discovery Today 21 (11): 1828–1834.
202 Zhou, Y., Li, C., Peng, J. et al. (2018). DNA-encoded dynamic chemical library and its applications
in ligand discovery. Journal of the American Chemical Society 140: 15859–15867.
203 Zhu, Z., Shaginian, A., Grady, L.C. et al. (2018). Design and application of a DNA-encoded
macrocyclic peptide library. ACS Chemical Biology 13 (1): 53–59.
204 Shin, M.H., Lee, K.J., and Lim, H.S. (2019). DNA-encoded combinatorial library of macrocyclic
peptoids. Bioconjugate Chemistry 30 (11): 2931–2938.
205 Habault, J. and Poyet, J.L. (2019). Recent advances in cell penetrating peptide-based anticancer
therapies. Molecules 24: 927. https://doi.org/10.3390/MOLECULES24050927.
206 Hyun, S., Lee, Y., Jin, S.M. et al. (2018). Oligomer formation propensities of dimeric bundle
peptides correlate with cell penetration abilities. ACS Central Science 4: 885–893.
207 Peacock, H. and Suga, H. (2021). Discovery of de novo macrocyclic peptides by messenger RNA
display. Trends in Pharmacological Sciences 42 (5): 385–397.
208 Vinogradov, A.A. et al. (2019). Macrocyclic peptides as drug candidates: recent progress and
remaining challenges. Journal of the American Chemical Society 141: 4167–4181.
209 Pandya, A.K. and Patravale, V.B. (2021). Computational avenues in oral protein and peptide
therapeutics. Drug Discovery Today 26 (6): 1510–1520.
210 Wu, S.J., Luo, J., O’Neil, K.T. et al. (2010). Structure-based engineering of a monoclonal antibody
for improved solubility. Protein Engineering, Design & Selection 23: 643–651.
211 Agostini, F., Cirillo, D., Livi, C.M. et al. (2014). cc SOL omics: a webserver for solubility prediction
of endogenous and heterologous expression in Escherichia coli. Bioinformatics 30: 2975–2977.
212 Smialowski, P., Doose, G., Torkler, P. et al. (2012). PROSO II – a new method for protein solubility
prediction. The FEBS Journal 279: 2192–2200.
213 Qian, Z., LaRochelle, J.R., Jiang, B. et al. (2014). Early endosomal escape of a cyclic cell-
penetrating peptide allows effective cytosolic cargo delivery. Biochemistry 53: 4034–4046.
214 Donsky, E. and Wolfson, H.J. (2011). PepCrawler: a fast RRT-based algorithm for high-resolution
refinement and binding affinity estimation of peptide inhibitors. Bioinformatics 27: 2836–2842.
215 Raveh, B., London, N., and Schueler-Furman, O. (2010). Sub-angstrom modeling of complexes
between flexible peptides and globular proteins. Proteins 78: 2029–2040.
216 Trellet, M., Melquiond, A.S., and Bonvin, A.M. (2013). A unified conformational selection and
induced fit approach to protein-peptide docking. PLoS One 8: e58769.
217 Antes, I. (2010). DynaDock: a new molecular dynamics-based algorithm for protein-peptide
docking including receptor flexibility. Proteins 78: 1084–1104.
218 Trabuco, L.G., Lise, S., Petsalaki, E., and Russell, R.B. (2012). PepSite: prediction of peptide-
binding sites from protein surfaces. Nucleic Acids Research 40: 423–427.
219 Porter, K.A., Xia, B., Beglov, D. et al. (2017). ClusPro PeptiDock: efficient global docking of
peptide recognition motifs using FFT. Bioinformatics 33: 3299–3301.
220 De Vries, S.J., Rey, J., Schindler, C.E.M. et al. (2017). The pepATTRACT web server for blind,
large-scale peptide–protein docking. Nucleic Acids Research 45: 361–364.
221 Zhou, P., Jin, B., Li, H., and Huang, S.Y. (2018). HPEPDOCK: a web server for blind peptide-
protein docking based on a hierarchical algorithm. Nucleic Acids Research 46: 443–450.

362
222 Lee, H., Heo, L., Lee, M.S., and Seok, C. (2015). GalaxyPepDock: a protein–peptide docking tool
based on interaction similarity and energy optimization. Nucleic Acids Research 43: 431–435.
223 Taherzadeh, G., Zhou, Y., Liew, A.W., and Yang, Y. (2018). Structure-based prediction of protein
peptide binding regions using random forest. Bioinformatics 34: 477–484.
224 Iqbal, S. and Hoque, M.T. (2018). PBRpredict-Suite: a suite of models to predict peptide
recognition domain residues from protein sequence. Bioinformatics 34: 3289–3299.
225 Obarska-Kosinska, A., Iacoangeli, A., Lepore, R., and Tramontano, A. (2016). PepComposer:
computational design of peptides binding to a given protein surface. Nucleic Acids Research 44:
522–528.
226 Yagi, Y., Terada, K., Noma, T. et al. (2007). In silico panning for a non-competitive peptide
inhibitor. BMC Bioinformatics 8: 1–1.
227 Tabassum, S., Al-Asbahy, W.M., Afzal, M., and Arjmand, F. (2012). Synthesis, characterization
and interaction studies of copper based drug with human serum albumin (HSA): spectroscopic
and molecular docking investigations. Journal of Photochemistry and Photobiology B: Biology 114:
132–139.
228 Zhang, Z.H., Abbad, S., Pan, R.R. et al. (2013). N-octyl-N-arginine chitosan micelles as an oral
delivery system of insulin. Journal of Biomedical Nanotechnology 9 (4): 601–609.
229 Moradi, S.V., Hussein, W.M., Varamini, P. et al. (2016). Glycosylation, an effective synthetic
strategy to improve the bioavailability of therapeutic peptides. Chemical Science 7 (4): 2492–2500.
230 Yang, C., Lu, D., and Liu, Z. (2011). How PEGylation enhances the stability and potency of
insulin: a molecular dynamics simulation. Biochemistry 50 (13): 2585–2593.
231 Clardy-James, S., Allis, D.G., Fairchild, T.J., and Doyle, R.P. (2012). Examining the effects of
vitamin B 12 conjugation on the biological activity of insulin: a molecular dynamic and in vivo
oral uptake investigation. MedChemComm 3 (9): 1054–1058.
232 Sabu, C., Raghav, D., Jijith, U.S. et al. (2019). Bioinspired oral insulin delivery system using yeast
microcapsules. Materials Science and Engineering: C 103: 109753.
233 Mishra, B. and Wang, G. (2012). Ab initio design of potent anti-MRSA peptides based on database
filtering technology. Journal of the American Chemical Society 134: 12426–12429. https://doi.org/
10.1021/ja305644e.
234 Freire, J.M., Almeida, D.S., Flores, L. et al. (2015). Mining viral proteins for antimicrobial and
cell-penetrating drug delivery peptides. Bioinformatics 31: 2252–2256. https://doi.org/10.1093/
bioinformatics/btv131.
Соседние файлы в папке Библиотека им академика М.И. Перельмана
