Computational Vaccine Design

Preface
Contents
Contributors
Chapter 1: Vaccine Design: An Introduction
1 Introduction
2 Basic Immunology Principles: Innate and Adaptive Immunity
3 What Is a Vaccine and How Vaccines Work?
4 Rational Vaccine Design and Vaccine Platforms
5 Concluding Remarks: Future Vaccines
References
Part I: Immunomics and System Immunology
Chapter 2: Epitope Binning of Monoclonal and Polyclonal Antibodies by Biolayer Interferometry
1 Introduction
2 Materials
2.1 Equipment/Software
2.2 Plates
2.3 Buffers/Antigens/Samples
3 Methods
3.1 Kinetics Measurement of Antibody Binding
3.1.1 Kinetics Experiment Assay Definition
3.1.2 Kinetics Experiment Plate Setup
3.1.3 Transfer of Assay Plates to the BLI Instrument and Assay Start
3.1.4 Data Analysis/Processing
3.2 Epitope Binning of mAbs
3.2.1 mAb Epitope Binning Assay Definition
3.2.2 mAb Epitope Binning Assay Plates Setup
3.2.3 Transfer of Assay Plates to the BLI Instrument and Assay Start
3.2.4 Data Analysis/Processing
3.3 Epitope Binning of Serum pAbs
3.3.1 Serum Epitope Binning Assay Plate Definition
3.3.2 Serum Epitope Binning Assay Plates Setup
3.3.3 Transfer of Assay Plates to the BLI Instrument and Assay Start
3.3.4 Data Analysis/Processing
4 Notes
References
Chapter 3: Clustering and Annotation of T Cell Receptor Repertoires
1 Introduction
2 Materials
2.1 TCR Data Files
2.2 ClusTCR Tool
2.3 TCRex Tool
2.4 Code Repository and Tutorials
3 Methods
3.1 Clustering Repertoires with ClusTCR
3.2 Annotating the Clusters with TCRex
3.2.1 Adjust the Size of TCR Repertoire Files
3.2.2 Send Files Through TCRex
3.2.3 Download TCRex Results
3.2.4 Concatenate the Results from the Original Files
3.2.5 Calculate Identification Metrics
3.2.6 Examine the Epitope-Specific Clusters
4 Notes
References
Chapter 4: Protocol for Classification Single-Cell PBMC Types from Pathological Samples Using Supervised Machine Learning
1 Introduction
2 Workflow
3 The Protocol
3.1 Data Preprocessing
3.1.1 Basic Statistic
3.1.2 Metadata Checking
3.1.3 Standardization
3.1.4 Quality Control
3.1.5 Documenting Statistics and Metadata
3.2 Cell-Type Labeling and Supervised Machine Learning
3.2.1 Protein Marker-Based Prediction and Hierarchical Clustering
3.2.2 Other Supervised Machine Learning Methods
3.2.3 Supervised ML Models
3.3 Prediction of Cell Types by Supervised Machine Learning
3.4 Discussion
4 Notes
References
Chapter 5: Unbiased, High-Throughput Identification of T Cell Epitopes by ELISPOT
1 Introduction
2 Materials
3 Methods
3.1 Thaw Cryopreserved PBMC (Sterile Conditions)
3.2 Plate PBMC (Sterile Conditions)
3.3 Plate Antigens/Peptides (Sterile Conditions)
3.4 Develop the ELISPOT Plate (Non-sterile Conditions)
4 Notes
References
Chapter 6: CD4+ T Cell Epitope Identification from Complex Parasite Antigen Mixtures
1 Introduction
2 Materials
2.1 Critical Consumables and Equipment
2.2 Antigen Characterization
3 Methods
3.1 Antigen Characterization: Input Antigen for MS Database
3.2 Reconstituted In Vitro Antigen Processing System
3.3 Prioritizing Candidate Epitopes
3.3.1 Retrieving Scores from the Experimental Information
3.3.2 Refine Selection Based on Available Information
3.3.3 Combining Experimental, Available, and In Silico-Related Information
3.4 Evaluating CD4+ T Cell Reactivity
3.4.1 Expanding Human Antigen-Specific T Cells
3.4.2 Quantification and Phenotypic Characterization of Peptide-Reactive T Cells
3.4.3 Tetramer Staining of ES-Expanded T Cells
4 Notes
References
Chapter 7: Computational Grafting of Epitopes
1 Introduction
2 Materials
2.1 Software Packages
2.2 Install RAMP
3 Methods
3.1 Prepare Epitope Sequence and Sequence db File
3.2 Prepare the PDB File of Carrier Protein/Scaffold
3.3 Prepare Loop Files
3.4 Graft the Epitope Using mcgen_semfold_loop
3.5 Score the Chimeric Grafted Models Using RAPDF Scoring Function
3.6 Extract the Scores from fileslist.rapdf_score Text Files
3.7 Expected Outcome
4 Notes
References
Chapter 8: Manufacture of Mesoporous Silicon Microparticles (MSMPs) as Adjuvants for Vaccine Delivery
1 Introduction
2 Materials
2.1 Mesoporous Silicon Microparticles Production
2.2 Mesoporous Silicon Microparticles Functionalization
2.3 Load of Peptides and Carbohydrates into Mesoporous Silicon Microparticles
3 Methods
3.1 Mesoporous Silicon Production
3.2 Obtention of Microparticles of the Desired Size
3.3 Microparticles Functionalization
3.4 Weighing Particles
3.5 Washing Particles
3.6 Characterization of MSMPs
3.7 Fluorescent Labeling of MSMP with FITC
3.8 Load Viral Peptides to MSMPs-NH3
4 Notes
References
Part II: Databases
Chapter 9: IEDB and CEDAR: Two Sibling Databases to Serve the Global Scientific Community
1 Introduction
2 IEDB Data Curation
2.1 Criteria for Epitope Inclusion
2.2 Consistent Data Entry and Literature Curation
3 Querying the IEDB Data
4 Navigating the Results of IEDB Queries
5 IEDB Community Outreach
6 Adapting IEDB Processes to CEDAR
7 Searching the CEDAR Database
8 Two Sibling Resources
9 Notes
References
Chapter 10: Updates on Databases of Allergens and Allergen-Epitopes
1 Introduction
2 Materials and Methods
2.1 WHO/IUIS Allergen Sub-Committee Database
2.1.1 Description
2.1.2 Usage
2.1.3 Query Result
2.2 AllFam-The Database of Allergen Family
2.2.1 Description
2.2.2 Usage
2.2.3 Query Result
2.3 Structural Database of Allergenic Proteins Database (SDAP)
2.3.1 Description
2.3.2 Usage
2.3.3 SDAP Database Query Result
2.4 AllergenOnline Database
2.4.1 Description
2.4.2 Usage
2.4.3 AllergenOnline Database Query Result
2.5 AllerBase
2.5.1 Description of AllerBase
2.5.2 Usage of AllerBase
2.5.3 AllerBase Query Result
3 Notes
References
Chapter 11: TSNAD and TSNAdb: The Useful Toolkit for Clinical Application of Tumor-Specific Neoantigens
1 Introduction
2 Materials
3 Methods
3.1 Docker of TSNAD
3.2 Web-Server of TSNAD
3.3 Architecture of TSNAdb
3.4 Web Interface of TSNAdb
4 Notes
References
Chapter 12: EPIPOX: A Resource Facilitating Epitope-Vaccine Design Against Human Pathogenic Orthopoxviruses
1 Introduction
2 Materials
2.1 The EPIPOX Resource
2.1.1 Description of EPIPOX Data: Predicted T Cell Epitopes
2.1.2 Experimental T Cell Epitopes
2.2 Description of Web Interface
2.2.1 Description of Search Options
2.2.2 Description of Search Filters
2.2.3 Description of Output Page
3 Methods
3.1 EPIPOX Search Example Case
3.2 Selection of Restriction Elements and Proteins
3.3 Selection of Search Filters
3.4 Getting Search Results
4 Notes
References
Part III: Prediction of Antigenicity and Immunogenicity: Tools and Protocols
Chapter 13: Prediction of Linear B Cell Epitopes in Proteins
1 Introduction
2 Materials
3 Methods
4 Notes
References
Chapter 14: Design of Linear B Cell Epitopes and Evaluation of Their Antigenicity, Allergenicity, and Toxicity: An Immunoinfor...
1 Introduction
2 Materials
3 Methods
3.1 Identification of Consensus Regions
3.2 Prediction of B Cell Epitopes
3.3 Prediction of Antigenicity, Allergenicity, and Toxicity
3.4 Deduction of Final B Cell Epitope(s)
4 Notes
References
Chapter 15: NetCleave: An Open-Source Algorithm for Predicting C-Terminal Antigen Processing for MHC-I and MHC-II
1 Introduction
1.1 C-Terminal Antigen Processing as a Predictor of T Cell Epitope Immunogenicity
1.2 NetCleave Advantages
1.3 Program
1.3.1 Installation
2 Using the NetCleave Algorithm
2.1 C-Terminal Processing Prediction
2.1.1 C-Terminal Prediction from a Protein Sequence in FASTA File (Type 1)
2.1.2 C-Terminal Prediction from Peptides and Corresponding UniProt IDs in CSV File (Type 2)
2.1.3 C-Terminal Prediction from Peptides and Corresponding Parental Protein Sequences in a CSV File (Type 3)
2.1.4 Epitope Prioritization and Filtering According to Their C-Terminal Processing Likelihood
2.2 Model Retraining
2.2.1 Retraining NetCleave Using an Updated IEDB Dataset (Type 1)
2.2.2 Retraining NetCleave Using IEDB and a Target Dataset (Type 2)
2.2.3 Retraining NetCleave Using a Target Dataset (Type 3)
3 Notes
References
Chapter 16: Prediction of TAP Transport of Peptides with Variable Length Using TAPREG
1 Introduction
1.1 Classical Class I Antigen Presentation Pathway
1.2 TAP Transport of Peptides
1.3 Predicting Peptide Binding to TAP
2 Materials
2.1 Sequence Collection
2.2 TAPREG Tool
3 Methods
3.1 Predicting TAP Binding Affinity Using Peptides as Input
3.2 Predicting TAP Binding Affinity Using as Input a Protein Sequence
4 Notes
References
Chapter 17: Docking-Based Prediction of Peptide Binding to MHC Proteins
1 Introduction
2 Materials
2.1 Structures
2.2 Software
3 Methods
3.1 Docking Options
3.2 Pre-docking Data Preparation
3.3 Molecular Docking
3.4 Construction of the Docking-Based Quantitative Matrix (QM)
4 Notes
References
Chapter 18: The PANDORA Software for Anchor-Restrained Peptide:MHC Modeling
1 Introduction
1.1 3D Modeling of Peptide:MHC
1.2 PANDORA
2 Useful Information
2.1 Code-Block Colors
2.2 Chain ID and Numbering Conventions
2.3 Database Location
2.4 Database Structure
2.5 Exploring the Database
3 PANDORA Protocols
3.1 Installation
3.2 Download the Template Database or Build the Database Locally
3.3 Protocol 1-Model a Peptide:MHC-I Complex, a Simple Scenario
3.4 Protocol 2-Model a Peptide:MHC-I Complex, a Comprehensive Python Scenario
3.5 Protocol 3-Model a Peptide:MHC-II Complex
3.6 Protocol 4-Run PANDORA Wrapper on Multiple Cases
3.7 Model Quality Evaluations
3.8 Anticipated Results
4 Limitations of PANDORA
5 Notes
References
Chapter 19: Prediction of Peptide and TCR CDR3 Loops in Formation of Class I MHC-Peptide-TCR Complexes Using Molecular Models ...
1 Introduction
2 Software
3 Methods
3.1 Model Building for Peptides
3.2 Prediction for Peptides
3.3 Model Building for TCR CDR3 Loops
3.4 Prediction for TCR CDR3 Loops
4 Notes
References
Chapter 20: Prediction of Bacterial Immunogenicity by Machine Learning Methods
1 Introduction
2 Materials
2.1 Papers
2.2 Sequences
2.2.1 Dataset of Immunogenic Proteins
2.2.2 Dataset of Non-immunogenic Proteins
2.3 Software
3 Methods
3.1 Splitting the Datasets
3.2 Transformation of the Protein Sequences into Numerical Vectors
3.3 Auto- and Cross-Covariance Transformation
3.4 Data Preparation
3.5 Training a Classification Model
3.6 Assessment of the Model Performance
3.7 Validation of ML Models with the Test Set
3.8 Machine Learning Methods
3.8.1 Partial Least Squares-Based Discriminant Analysis (PLS-DA)
3.8.2 k Nearest Neighbor (kNN)
3.8.3 Support Vector Machine (SVM)
3.8.4 Random Forest (RF)
3.8.5 Random Subspace Method (RSM) with kNN Estimator
3.8.6 Extreme Gradient Boosting (Xgboost)
3.9 ML Model Assessment
4 Notes
References
Chapter 21: Vaxi-DL: An Artificial Intelligence-Enabled Platform for Vaccine Development
1 Introduction
2 Materials
2.1 Sequences
2.2 Software
3 Methods
3.1 Construction of a Deep Learning Model
3.1.1 Data Curation
3.1.2 Annotating Protein Sequences
3.1.3 Selection of Features
3.1.4 Data Preprocessing
3.1.5 Hyperparameter Tuning
3.1.6 Construction of DL Models
3.1.7 Training, Validation, and Testing of DL Model
3.1.8 Evaluating Performance
3.2 Testing the Models
3.2.1 Benchmarking with Independent Datasets
3.2.2 Comparison with Known Vaccines
Known Vaccines from Protegen
Known Vaccines from the Vaxgen Database
Licensed Vaccine from the Violin Database
Potential Vaccines Candidate from Known Pathogens
4 Notes
References
Chapter 22: A Web-Based Method for the Identification of IL6-Based Immunotoxicity in Vaccine Candidates
1 Introduction
2 Materials
3 Methods
3.1 Brief Description of IL6pred
3.2 Identification of IL6-Inducing Peptides
3.3 Designing of Non-IL6-Inducing Peptides
3.4 Identification of IL6-Inducing Peptides in Antigen
3.5 Scanning of IL6-Specific Motifs
3.6 BLAST-Based Similarity Search
3.7 Standalone Package
4 Notes
References
Chapter 23: In Silico Tool for Identification, Designing, and Searching of IL13-Inducing Peptides in Antigens
1 Introduction
2 Materials
3 Methods
3.1 Description of IL13pred Tool
3.2 Prediction of IL13-Inducing Peptides
3.3 Designing of IL13-Inducing Peptides
3.4 Scanning of IL13-Inducing Regions
3.5 Similarity Search Based on BLAST
3.6 Standalone Version of IL13pred
4 Notes
References
Part IV: Computational Vaccinology Applications and Protocols
Chapter 24: A Lean Reverse Vaccinology Pipeline with Publicly Available Bioinformatic Tools
1 Introduction
2 Materials
3 Methods
3.1 Obtain Core Proteome of the Target Bacterium
3.1.1 Find Reference Assemblies and Download Their Proteomes
3.1.2 Generate Clustered Panproteome
3.1.3 Generate Core Proteome
3.2 Prediction of Protein Subcellular Localization
3.3 Estimation of Protein Expression/Abundance
3.3.1 Download Proteomic Dataset
3.3.2 Process Proteomics Data in MaxQuant
3.4 Prediction of T and B Cell Linear Epitopes
3.4.1 Selecting Protein of Interest from fasta File
3.4.2 T Cell Epitope Prediction with MixMHC2Pred
3.4.3 B Cell Epitope Prediction with EpitopeVec
4 Notes
References
Chapter 25: Immunoinformatics Protocol to Design Multi-Epitope Subunit Vaccines
1 Introduction
2 Materials
2.1 Computational Workstation
2.2 Software for Structure Visualization and Data Analysis
3 Methods
3.1 Sequence Retrieval
3.2 Antigenicity Prediction
3.3 HTL Epitope Prediction
3.4 IFN-γ-Inducing Epitopes Prediction
3.5 CTL Epitope Prediction
3.6 BCL Epitope Prediction
3.7 Toxicity Prediction
3.8 Multi-Epitope Subunit Vaccine Design
3.9 Adjuvant Selection
3.10 Immunogenicity Prediction
3.11 Allergenicity Prediction
3.12 Determination of Physiochemical Properties
3.13 Structure Prediction and Validation
3.14 Disulfide Engineering
3.15 Molecular Docking with Receptor
3.16 Molecular Dynamics Simulations
3.17 MD Simulation-Based Analyses
3.18 Codon Adaptation and in Silico Cloning
4 Notes
References
Chapter 26: In Silico Structure-Based Vaccine Design
1 Introduction
1.1 Rational Structure-Based Vaccine Design
1.1.1 Choice of the Vaccine Target and Structure Determination
1.1.2 X-Ray Crystallography and NMR
1.1.3 Homology Modeling
1.1.4 AI-Based Ab Initio Modeling
1.1.5 Identification of Binding Site
1.1.6 Sequence-Based Methods
1.1.7 Structure-Based Methods
1.1.8 Energy-Based Methods
1.1.9 Consensus Method
1.1.10 Molecular Docking-Based Virtual Screening
1.1.11 Target Preparation
1.1.12 Molecular Docking
1.1.13 Search Algorithm
1.1.14 Scoring Functions
1.1.15 Post-Docking Evaluation and Analysis
1.1.16 Molecular Dynamics and Binding Free Energy Calculation
1.1.17 Limitations of Molecular Docking
1.1.18 COVID-19 Vaccine Design
2 Materials
2.1 Software and Servers
2.2 Vaccine Design and Generation
2.3 Mouse Vaccination
2.4 ELISA
2.5 pVNT Assay
3 Methods for COVID-19 Vaccine Design and Simulation
3.1 Structural Modeling of SARS-CoV-2 Spike Protein
3.2 Structural Modeling of ACE2 Receptor
3.3 Docking of SARS-CoV-2 S Protein with ACE2 Proteins
3.4 MD Simulation of Docked SARS-CoV-2 Spike/ACE2 Protein Complexes
3.5 Calculation of Binding Free Energies of Spike-ACE2 Complexes
3.6 Molecular Dynamics Simulation of SARS-CoV-2 Spike Extracellular Domain (ECD) Trimer Structure
3.7 Spike Protein Vaccine Design and Generation
3.8 In Vivo Immunogenicity Testing
3.9 Immunogenicity Assessment by Spike Protein Binding Immunoglobulin ELISA Assay
3.10 Assessment of SARS-CoV-2 Neutralizing Antibody Using Lentivirus Pseudotype Assay
4 Notes
References
Chapter 27: Reverse Vaccinology for Influenza A Virus: From Genome Sequencing to Vaccine Design
1 Introduction
1.1 Reverse Vaccinology Approach Overview
1.2 A Case Study: Application of RV Methodology on the Design of the Influenza A Vaccine
2 Materials
2.1 Sequences
2.2 Software
3 Methods
3.1 General Workflow
3.2 Collection of Influenza A Protein Sequences
3.3 Multi-Sequence Alignment
3.4 Epitopes Prediction
3.5 Antigenicity and Allergenicity Evaluation
3.6 Simulation on UISS-FLU
3.7 Results
4 Notes
References
Chapter 28: Immunoinformatics Vaccine Design for Zika Virus
1 Introduction
2 Methods
2.1 Consensus Sequence
2.2 CD8 Epitope Selection
2.2.1 IEDB MHC-I Binding
2.2.2 IEDB Class I Immunogenicity
2.3 CD4 Epitope Selection
2.3.1 IEDB MHC-II Binding
2.3.2 IEDB CD4+ T Cell Immunogenicity Prediction
2.4 IFN-γ-Inducing Epitopes on IFNepitope Server
2.5 Other Cytokines Induction Evaluation
2.6 IEDB Population Coverage
2.7 B Cell Linear Epitope Prediction
2.8 Vaccine Structure Design
2.8.1 Adjuvants
2.8.2 Linkers
2.9 Vaccine Safety
2.9.1 Vaccine Potential Allergenicity
2.9.2 Evaluation of Autoimmune Induction
2.10 Vaccine Antigenicity
2.11 Physical and Chemical Properties
2.12 Secondary Structure Prediction
2.13 Tertiary Structure, Refinement, and Validation
2.14 B Cell Epitope Prediction from Protein Tertiary Structure
3 Notes
References
Chapter 29: Immunoinformatics Approaches in Designing Vaccines Against COVID-19
1 Introduction
2 Materials
2.1 Software, Servers, and Databases
3 Methods
3.1 Data Mining and Selection of Antigenic Protein
3.2 Screening of B and T Cell Epitopes and Prediction of Antigenicity
3.3 Construction of the Vaccine Architecture and Physicochemical Characterization
3.4 Determination of Vaccine Efficacy in Triggering Innate and Adaptive Immunity
3.5 Determination of Stability of Binding of Vaccine to the Target Proteins
3.6 Immune Simulation with the Vaccine
3.7 Cloning of the Vaccine Construct
4 Notes
References
Chapter 30: A Sample Guideline for Reverse Vaccinology Approach for the Development of Subunit Vaccine Using Varicella Zoster ...
1 Introduction
2 Materials
3 Methods
3.1 Retrieval of VZV Glycoprotein B Sequences
3.2 Multiple Sequence Alignment and Identification of Conserved Regions
3.3 Linear B Cell Epitope Analysis
3.4 Prediction of MHC Class I Epitopes
3.5 MHC-II Binding Prediction
3.6 Population Coverage Analysis
4 Notes
References
Chapter 31: Computational Vaccine Design for Poxviridae Family Viruses
1 Introduction
2 Materials
3 Methods
3.1 Proteome Retrieval and Scanning
3.2 Prediction of CTL, HTL, and B Cell Epitopes
3.3 Construction of Multi-Epitopes Subunit Vaccine
3.4 Physicochemical Properties Analysis and 3D Structure Modeling
3.5 Molecular Docking with Human TLRs
3.6 Codon Optimization and In Silico Cloning
3.7 Immune Simulation Analysis
3.8 Molecular Dynamics Simulation
4 Notes
References
Chapter 32: Computational Prediction of Trypanosoma cruzi Epitopes Toward the Generation of an Epitope-Based Vaccine Against C...
1 Introduction
2 Materials
2.1 Sequences
2.2 Protein Structures
2.3 Software
3 Methods
3.1 Sequence Processing
3.2 Clusterization and Filtering
3.3 Alignment and Conservation Analysis
3.4 CD8+ T Cell Epitope Prediction
3.5 CD4+ T Cell Epitope Prediction
3.6 B Cell Epitope Prediction
3.7 Homology Analysis
4 Notes
References
Chapter 33: Computational Vaccine Design for Common Allergens
1 Introduction
2 Materials and Methods
2.1 In Silico Designing of Carrier-Bound Hypoallergenic B Cell Epitope Vaccine
2.2 In Silico Design of Multi-Epitope Vaccine
2.3 Assessment of Physicochemical Properties of the Synthetic Candidate Vaccine Using Computational Tools
3 Notes
References
Index