Computational Vaccine Design | Book-Club |

Categories: Book-Club, Computational Vaccine Design

About Course

Description

This book provides a comprehensive guide to computational methods used in vaccine design. It covers topics such as epitope prediction, antigen selection, and immunoinformatics, offering practical protocols and tools for researchers. The book is a valuable resource for understanding how computational approaches are revolutionizing vaccine development and immunology.

Implementation Plan for the Book Club Over Two Months

1. Book Selection

Book: Computational Vaccine Design.
Level: Intermediate to Advanced.
Total Chapters: 12 (approximate).

2. Chapter Division

The book will be divided into 8 parts (one part per week).
Each week, members will read 1-2 chapters depending on the length and complexity.

3. Weekly Schedule

Week 1: Chapter 1 (Introduction to Computational Vaccine Design) + Chapter 2 (Basics of Immunology).
Week 2: Chapter 3 (Epitope Prediction Methods) + Chapter 4 (Antigen Selection Strategies).
Week 3: Chapter 5 (Immunoinformatics Tools) + Chapter 6 (Vaccine Target Identification).
Week 4: Chapter 7 (Computational Models for Vaccine Efficacy) + Chapter 8 (Case Studies in Vaccine Design).
Week 5: Chapter 9 (Challenges in Computational Vaccine Design) + Chapter 10 (Future Directions).
Week 6: Chapter 11 (Ethical Considerations) + Chapter 12 (Conclusion and Summary).
Week 7: Review and Recap of Key Concepts.
Week 8: Final Discussion and Evaluation.

4. Weekly Meetings

Duration: 1-2 hours per meeting.
Agenda:
- Discuss the assigned chapters.
- Explain complex concepts with the help of an instructor.
- Answer members’ questions.
- Open discussion on ideas presented in the chapters.
Use interactive tools like presentations or videos to enhance understanding.

5. Interactive Activities

Workshops: Organize practical workshops on using immunoinformatics tools (e.g., epitope prediction software).
Side Discussions: Create a Facebook or WhatsApp group for discussions outside meetings.
Weekly Challenges: For example, writing a summary of the week’s chapters or analyzing a small dataset.

6. Final Evaluation

At the end of the two months, conduct a final evaluation:
- Survey to assess the reading and meeting experience.
- General discussion session about the book as a whole.
- Members share their personal evaluation of the book and what they learned.

Course Content

Before You Start: Book Club Orientation

Promo

00:00
Book Club System: How It Works & How You Win

00:00

Computational Vaccine Design | Book-Club |

Chapter 1

Chapter 2

Chapter 3

Abstract

00:00
1 Introduction

00:00
2 1 Materials

00:00
2. 2 ClusTCR Tool

00:00
2. 3 ,2 .4 TCRex Tool

00:00
2. 4 TCRex Tool Code Repository and Tutorials

00:00
3 1 Clustering Repertoires with ClusTCR

00:00
3. 2 Clustering and Annotation of T Cell Receptor Repertoires

00:00
4 Notes

00:00

Chapter 4

Chapter 5

Chapter 6 Chapter7

Chapter 8 Chapter9

Abstract

00:00
1 Introduction

00:00
2 2 1 IEDB Data Curation

00:00
3 Querying the IEDB Data

00:00
4 Navigating the Results of IEDB Queries

00:00
5. IEDB Community Outreach

00:00
6 Adapting IEDB Processes to CEDAR

00:00
7 Searching the CEDAR Database

00:00
8 Two Sibling Resources

00:00
END Chapter 9 IEDB and CEDAR Two Sibling Databases to Serve the Global Scientific Community

00:00

Chapter 10

Chapter 11

Chapter 12

Chapter 13

Chapter 14

Chapter 15

Chapter 16

Abstract

00:00
1. 1. 1 Introduction

00:00
1. 2 TAP Transport of Peptides

00:00
1. 3 Predicting Peptide Binding to TAP

00:00
2 . 2. 1 Materials Sequence Collection

00:00
2 2 TAPREG Tool

00:00
3. 3. 1 Methods Predicting TAP Binding Affinity Using Peptides as Input

00:00
3. 2 Predicting TAP Binding Affinity Using as Input a Protein Sequence

00:00
4 Notes

00:00

Chapter 17

Chapter 18

Abstract

00:00
1 Introduction

00:00
2 .2. 1 Useful Information Code Block Colors

00:00
3. 3 .1 PANDORA Protocols Installation

00:00
3 .2 Download the Template Database or Build the Database

00:00
3. 3 Protocol 1— Model a PeptideMHC I Complex, a Simple

00:00
3. 4 Protocol 2— Model a PeptideMHC I Complex, a Comprehensive Python Scenario

00:00
3 .5 Protocol 3— Model a PeptideMHC II Complex

00:00
3. 6 Protocol 4—Run PANDORA Wrapper on Multiple Cases

00:00
3 .7 Model Quality Evaluations

00:00
3 .8 Anticipated

00:00
4 Limitations of PANDORA

00:00
5 Notes

00:00

Chapter 19

Chapter 20

Chapter 20 Prediction of Bacterial Immunogenicity by Machine Learning Methods Abstract

00:00
1 Introduction

00:00
2 . 2. 1 Materials

00:00
2 2 1 Sequences Dataset of Immunogenic Proteins

00:00
3 1 Methods Splitting the Datasets

00:00
3 2 Transformation of the Protein Sequences into Numerical Vectors

00:00
3 .3 Auto and Cross Covariance Transformation

00:00
3 4 Data Preparation

00:00
3 5 Training a Classification Model

00:00
3. 6 Assessment of the Model Performance

00:00
3 8 Machine Learning Methods

00:00
3 9 ML Model Assessment

00:00
4 Notes

00:00

Chapter 21

Chapter 22

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

00:00
1 Introduction

00:00
2 Materials

00:00
3 3 1 Methods Brief Description of IL6pred

00:00
3 2 Identification of IL6 Inducing Peptide

00:00
3 3 Designing of Non IL6 Inducing Peptides

00:00
3 4 Identification of IL6 Inducing Peptides in Antigen

00:00
3 5 Scanning of IL6 Specific Motifs

00:00
3 6 BLAST Based Similarity Search

00:00
3 .7 Standalone Package

00:00
4 Notes

00:00

Chapter 23

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

00:00
1 Introduction

00:00
2 Materials

00:00
3 3 1 Methods Description of IL13pred Tool

00:00
3 3 Predicting IL13 Inducer Peptides

00:00
3 4 Scanning of IL13 Inducing Regions

00:00
3 5 Similarity Search Based on BLAST

00:00
3 6 Standalone Version of IL13pred

00:00
4 Notes

00:00

Chapter 24

Chapter 25

Chapter 26

Chapter 26 In Silico Structure Based Vaccine Design Abstract

00:00
1 Introduction

00:00
1 1 Rational Structure Based Vaccine Design

00:00
2 2 1 Materials Software and Servers

00:00
3 3 1 Methods for COVID 19 Vaccine Design and Simulation Structural

00:00
3 2 Structural Modeling of ACE2 Receptor

00:00
3 3 In Silico Vaccine Design

00:00
3 4 MD Simulation of Docked SARS CoV 2 SpikeACE2 Protein Complexes

00:00
3 5 Calculation of Binding Free Energies of Spike ACE2 Complexes

00:00
3 6 Molecular Dynamics Simulation

00:00
3 7 Spike Protein Vaccine Design and Generation

00:00
3 8 In Vivo Immunogenicity Testing

00:00
3 9 Immunogenicity Assessment by Spike Protein Binding

00:00
3 10 Assessment of SARS CoV 2 Neutralizing Antibody Using Lentivirus Pseudotype Assay

00:00
4 Notes

00:00

Chapter 27

Chapter 28

Chapter 28 Immunoinformatics Vaccine Design for Zika Virus Abstract

00:00
1 Introduction

00:00
2 2 1 Methods

00:00
2 2 CD8 Epitope Selection 2 2 1 Binding IEDB MHC I control

00:00
2. 3 CD4 Epitope Selection

00:00
2 4 IFN γ Inducing Epitopes on IFNepitope Server

00:00
2 6 IEDB Population Coverage

00:00
2 8 Vaccine Structure Design

00:00
2. 9 , 1 Linkers Vaccine Safety

00:00
2 10 Vaccine Antigenicity

00:00
2 14 B Cell Epitope

00:00
3 Notes

00:00

Chapter 29

Chapter 29 Immunoinformatics Approaches in Designing Vaccines Against COVID 19 Abst

00:00
1 Introduction

00:00
2 Materials

00:00
2 1 Software, Servers, and Databases

00:00
3 3 1 Methods

00:00
3 2 Screening of B and T Cell Epitopes and Prediction of Antigenicity

00:00
3 3 Construction of the Vaccine Architecture and Physicochemical Characterization

00:00
3 4 Determination of Vaccine Efficacy in Triggering Innate and Adaptive Immunity

00:00
3 5 Determination of Stability of Binding of Vaccine to the Target Proteins

00:00
3 6 Immune Simulation with the Vaccine

00:00
3 7 Cloning of the Vaccine Construct

00:00
4 Notes

00:00

Chapter 30

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

00:00
1 Introduction

00:00
2 Materials

00:00
3 3 1 Methods

00:00
3 2 Multiple Sequence Alignment and Identification of Conserved Regions

00:00
3 3 Linear B Cell Epitope Analysis

00:00
3 4 Prediction of MHC Class I Epitopes 3 5

00:00
3 5 MHC

00:00
3 6 Population Coverage Analysis

00:00
4 Notes

00:00

Chapter 31

Chapter 31 Computational Vaccine Design for Poxviridae Family Viruses Abstract

00:00
1 Introduction

00:00
2 Materials

00:00
3 3 1 Methods Proteome

00:00
3 2 Prediction of CTL, HTL, and B Cell Epitopes

00:00
3 3 Construction of Multi Epitopes Subunit Vaccine

00:00
3 4 Physicochemical Properties Analysis and 3D Structure Modeling

00:00
3 5 Molecular Docking with Human TLRs

00:00
4 Notes

00:00

Chapter 32

Chapter 32 Computational Prediction of Trypanosoma cruzi Epitopes Toward the Generation

00:00
1 Introduction

00:00
2 2 1 Materials Sequences Protein Structures

00:00
3 3 1 Methods Sequence Processing

00:00
3 2 Clusterization and Filtering

00:00
3 3 Alignment and Conservation Analysis

00:00
3 4 CD8+ T Cell Epitope Prediction

00:00
3 5 CD4+ T Cell Epitope Prediction

00:00
3 6 B Cell Epitope Prediction

00:00
3 7 Homology Analysis

00:00
4 Notes

00:00

Chapter 33

Bioinformatics Gate: Accredited in Egypt & UK. Certificates registered with Company House UK & UKRLP. Join us

Student Ratings & Reviews

No Review Yet

Intermediate
20 Total Enrolled
February 1, 2026 Last Updated
Enrollment validity: 345 days
Certificate of completion

A course by

Israa M Shamkh

About Course

What Will You Learn?

Course Content

Before You Start: Book Club Orientation

Promo

Book Club System: How It Works & How You Win

Computational Vaccine Design | Book-Club |

Computational Vaccine Design

Chapter 1

Abstract

1 Introduction

2. Basic Immunology Principles

3 What Is a Vaccine and How Vaccines Work

5 Concluding Remarks Future Vaccines

Chapter 2

Abstract

1 Introduction

2. 1 Materials Equipment Software

3 .1 Methods Kinetics

3 .2 Epitope Binning of mAbs

3 3 Epitope Binning of Serum pAbs

4 Notes

Chapter 3

Abstract

1 Introduction

2 1 Materials

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

4 Notes

Chapter 4

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

1 Introduction

2 Workflow

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

Abstract

1 Introduction

2 Materials

3. 1 Methods Thaw Cryopreserved PBMC

3. 2 Plate PBMC Sterile Conditions

4 Notes

Chapter 6

Abstract

1 Introduction

2. 1 Materials

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

4 NotS

Chapter7

Abstract

1 Introduction

2 . 2. 1 Materials Software

3 . 3 .1 Methods

4 Notes

Chapter 8

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

1 Introduction

2 2 1 Materials

3 .3. 1 Methods Mesoporous Silicon Production

4 Notes

Chapter9

Abstract

1 Introduction

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