Book Club System: How It Works & How You Win – Bio Informatics Gate

Course Content

Before You Start: Book Club Orientation

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Book Club System: How It Works & How You Win

Computational Vaccine Design | Book-Club |

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Computational Vaccine Design

Chapter 1

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2. Basic Immunology Principles

3 What Is a Vaccine and How Vaccines Work

5 Concluding Remarks Future Vaccines

Chapter 2

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2. 1 Materials Equipment Software

3 .1 Methods Kinetics

3 .2 Epitope Binning of mAbs

3 3 Epitope Binning of Serum pAbs

Chapter 3

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2. 2 ClusTCR Tool

2. 3 ,2 .4 TCRex Tool

2. 4 TCRex Tool Code Repository and Tutorials

3 1 Clustering Repertoires with ClusTCR

3. 2 Clustering and Annotation of T Cell Receptor Repertoires

Chapter 4

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Chapter 4 Protocol for Classification Single Cell PBMC Types from Pathological Samples Usin

3 .3 .1 The Protocol Data

3. 2 Cell Type Labeling and Supervised Machine Learning

3. 3 Prediction of Cell Types by Supervised Machine Learning

3 .4 Discussion

END CH4 Protocol for Classification Single Cell PBMC Types from Pathological Samples Using Sup

Chapter 5

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3. 1 Methods Thaw Cryopreserved PBMC

3. 2 Plate PBMC Sterile Conditions

Chapter 6

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3 .3. 1 Methods Antigen

3. 2 Reconstituted In Vitro Antigen

3 .3 Prioritizing Candidate Epitopes 3 3 1

3 .4 Evaluating CD4+ T Cell Reactivity

Chapter7

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2 . 2. 1 Materials Software

3 . 3 .1 Methods

Chapter 8

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Chapter 8 Manufacture of Mesoporous Silicon Microparticles MSMPs as Adjuvants for Vacc

2 2 1 Materials

3 .3. 1 Methods Mesoporous Silicon Production

Chapter9

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2 2 1 IEDB Data 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

END Chapter 9 IEDB and CEDAR Two Sibling Databases to Serve the Global Scientific Community

Chapter 10

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2 2 1 Materials and Methods WHOIUIS Allergen Sub Committee Database

2 2 AllFam—The Database of Allergen Family

2 3 Databases of Allergens and their Epitopes

2 4 AllergenOnline Database

2. 5 .1 AllerBase Description of AllerBase

Chapter 11

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Chapter 11 TSNAD and TSNAdb The Useful Toolkit for Clinical Application of Tumor Speci

3 . 3. 1 Methods

Chapter 12

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Chapter 12 EPIPOX A Resource Facilitating Epitope Vaccine Design Against Human Pathogenic Ort

2 . 2. 1 Materials The EPIPOX Resource

2. 2 Description of Web Interface

3 .4 Getting Search Results

Chapter 13

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1. Introduction

Chapter 14

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Chapter 14Design of Linear B Cell Epitopes and Evaluation of Their Antigenicity, Allergenicity

3. 3 .1 Methods

Chapter 15

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Chapter 15 NetCleave An Open Source Algorithm for Predicting C Terminal Antigen Processing for

1 .2 NetCleave Advantages

2. 2. 1 Using the NetCleave Algorithm

2 2 1 Model Retraining Retraining

Chapter 16

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1. 1. 1 Introduction

1. 2 TAP Transport of Peptides

1. 3 Predicting Peptide Binding to TAP

2 . 2. 1 Materials Sequence Collection

2 2 TAPREG Tool

3. 3. 1 Methods Predicting TAP Binding Affinity Using Peptides as Input

3. 2 Predicting TAP Binding Affinity Using as Input a Protein Sequence

Chapter 17

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2 . 2. 1 Materials Structures Software

3 . 3 .1 Methods

3 .2 Pre docking Data Preparation

3. 3 Molecular Docking

3. 4 Construction of the Docking Based Quantitative Matrix QM

Chapter 18

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2 .2. 1 Useful Information Code Block Colors

3. 3 .1 PANDORA Protocols Installation

3 .2 Download the Template Database or Build the Database

3. 3 Protocol 1— Model a PeptideMHC I Complex, a Simple

3. 4 Protocol 2— Model a PeptideMHC I Complex, a Comprehensive Python Scenario

3 .5 Protocol 3— Model a PeptideMHC II Complex

3. 6 Protocol 4—Run PANDORA Wrapper on Multiple Cases

3 .7 Model Quality Evaluations

3 .8 Anticipated

4 Limitations of PANDORA

Chapter 19

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Chapter 19 Prediction of Peptide and TCR CDR3 Loops in Formation of Class I MHC Peptide TCR

3 1 Model Building for Peptides

3 3 Model Building for TCR CDR3 Loops

Chapter 20

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Chapter 20 Prediction of Bacterial Immunogenicity by Machine Learning Methods Abstract

2 . 2. 1 Materials

2 2 1 Sequences Dataset of Immunogenic Proteins

3 1 Methods 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 8 Machine Learning Methods

3 9 ML Model Assessment

Chapter 21

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2 2 1 Materials Sequences

3 3 1 Methods Construction of a Deep Learning Model

3 2 Testing the Models

Chapter 22

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Chapter 22 A Web Based Method for the Identification of IL6 Based Immunotoxicity in Vaccine

3 3 1 Methods Brief Description of IL6pred

3 2 Identification of IL6 Inducing Peptide

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

Chapter 23

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Chapter 23 In Silico Tool for Identification, Designing, and Searching of IL13 Inducing Pept

3 3 1 Methods Description of IL13pred Tool

3 3 Predicting IL13 Inducer Peptides

3 4 Scanning of IL13 Inducing Regions

3 5 Similarity Search Based on BLAST

3 6 Standalone Version of IL13pred

Chapter 24

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Chapter 24 A Lean Reverse Vaccinology Pipeline with Publicly Available Bioinformatic Tools Ab

3 3 1 Methods Obtain Core Proteome of the Target Bacterium

3 3 Estimation of Protein Expression Abundance

3 4 Prediction of T and B Cell Linear Epitopes

Chapter 25

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Chapter 25 Immunoinformatics Protocol to Design Multi Epitope Subunit Vaccines Abstract

2 2 1 Materials Computational Workstation

3 3 1 Methods Sequence Retrieval

3 1 2 Determination of Physiochemical Properties

3 1 6 Molecular Dynamics Simulations

Chapter 26

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Chapter 26 In Silico Structure Based Vaccine Design Abstract

1 1 Rational Structure Based Vaccine Design

2 2 1 Materials Software and Servers

3 3 1 Methods for COVID 19 Vaccine Design and Simulation Structural

3 2 Structural Modeling of ACE2 Receptor

3 3 In Silico Vaccine Design

3 4 MD Simulation of Docked SARS CoV 2 SpikeACE2 Protein Complexes

3 5 Calculation of Binding Free Energies of Spike ACE2 Complexes

3 6 Molecular Dynamics Simulation

3 7 Spike Protein Vaccine Design and Generation

3 8 In Vivo Immunogenicity Testing

3 9 Immunogenicity Assessment by Spike Protein Binding

3 10 Assessment of SARS CoV 2 Neutralizing Antibody Using Lentivirus Pseudotype Assay

Chapter 27

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Chapter 27 Reverse Vaccinology for Influenza A Virus From Genome Sequencing to Vaccine Design

1. 1 Reverse Vaccinology Approach

3 2 Collection of Identification of New Vaccine Candidates for Influenza A Virus

Chapter 28

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Chapter 28 Immunoinformatics Vaccine Design for Zika Virus Abstract

2 2 CD8 Epitope Selection 2 2 1 Binding IEDB MHC I control

2. 3 CD4 Epitope Selection

2 4 IFN γ Inducing Epitopes on IFNepitope Server

2 6 IEDB Population Coverage

2 8 Vaccine Structure Design

2. 9 , 1 Linkers Vaccine Safety

2 10 Vaccine Antigenicity

2 14 B Cell Epitope

Chapter 29

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Chapter 29 Immunoinformatics Approaches in Designing Vaccines Against COVID 19 Abst

2 1 Software, Servers, and Databases

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

Chapter 30

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Chapter 30 A Sample Guideline for Reverse Vaccinology Approach for the Development of Subun

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

3 6 Population Coverage Analysis

Chapter 31

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Chapter 31 Computational Vaccine Design for Poxviridae Family Viruses Abstract

3 3 1 Methods Proteome

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

Chapter 32

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Chapter 32 Computational Prediction of Trypanosoma cruzi Epitopes Toward the Generation

2 2 1 Materials Sequences Protein Structures

3 3 1 Methods 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

Chapter 33

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Chapter 33 Computational Vaccine Design for Common Allergens Abstract

2 2 1 Materials and Methods In Silico

2 2 In Silico Design of Multi Epitope Vaccine

2 3 Assessment of Physicochemical Properties

Computational Vaccine Design | Book-Club |

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