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Molecular Bioinformatics & In Silico Genetic Engineering

Molecular Bioinformatics & In Silico Genetic Engineering

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BATCH #24
03Seats Left

92% Booked

01
Days
:
03
Hrs
:
11
Min
:
40
Sec

Course Overview


02/01/2026

Registration closes 02/01/2026

Program Syllabus

Module 1

About This Course

The advent of computational vaccine design has revolutionized the field of immunology, enabling the development of protein and mRNA vaccines at an accelerated pace. With the success of COVID-19 mRNA vaccines, there is a growing need for computational tools that can predict, optimize, and validate the efficacy of novel vaccine candidates. This workshop introduces participants to in silico methods used to design vaccine candidates, including the selection of appropriate antigens, epitopes, adjuvants, and delivery systems.

The workshop covers the design of protein and mRNA vaccines, focusing on techniques like epitope prediction, protein folding (using tools such as MODELLER), codon optimization for mRNA, and mRNA vaccine construct design. Participants will also learn how to simulate vaccine immunogenicity, assess immune response using immune system modeling, and explore delivery methods for optimal efficacy. The program combines theory with dry-lab practical sessions on popular computational tools, enabling participants to create design-ready vaccine candidates for preclinical testing.

Module 2

Aim

This workshop aims to provide participants with the skills and knowledge to design protein and mRNA vaccines using computational tools. It will cover the complete workflow from sequence analysis, antigen selection, and vaccine construct design to mRNA synthesis and delivery system modeling. Participants will also learn how in silico approaches can accelerate vaccine development by identifying potential epitopes, improving immune response, and reducing experimental costs.

Module 3

Workshop Objectives

Participants will learn to:

  1. Identify and select candidate antigens for vaccine development using sequence analysis tools.
  2. Predict B-cell and T-cell epitopes using computational algorithms.
  3. Design mRNA constructs and optimize codon usage for efficient translation.
  4. Model vaccine immune responses using simulation tools.
  5. Create in silico protein and mRNA vaccine designs ready for experimental validation.
Module 4

Day 1: Introduction to In Silico Vaccine Design & Antigen Selection

  • Overview of Vaccine Types: Protein-based vs. mRNA-based vaccines
  • Antigen Discovery: Identifying and selecting potential antigens for vaccine development
  • Tools for Antigen Prediction: Epitope prediction tools, sequence alignment, and structure-based antigen design
  • Key Concepts: Immune system interactions, B-cell and T-cell epitopes
  • Tools: IEDB, NetMHC, HLA class I/II prediction tools, Vaxign
  • Mini task: Identify potential antigens for a virus (e.g., SARS-CoV-2) using IEDB and NetMHC tools
Module 5

Day 2: Protein Structure Prediction and Vaccine Design

  • Protein Structure Prediction: Techniques for predicting the 3D structure of antigens (homology modeling, ab initio, and threading)
  • Vaccine Design: Designing a stable, immunogenic protein vaccine candidate
  • mRNA Vaccine Design: Optimizing mRNA constructs for efficient translation, codon optimization, and delivery mechanisms
  • Tools: PyMOL, SWISS-MODEL, Rosetta, mRNA design tools (RNAfold, mfold)
  • Mini task: Design a protein-based vaccine candidate and optimize its mRNA sequence for codon usage
Module 6

Day 3: Molecular Docking, Immunogenicity Evaluation, and Research-Grade Reporting

  • Molecular Docking: Docking the antigen with immune receptors (e.g., TCR, BCR) to predict immunogenicity
  • Immunogenicity Evaluation: Evaluating the immune response through computational methods (e.g., MHC binding, T-cell receptor interactions)
  • Vaccine Optimization: Refining the vaccine candidate to improve stability, efficacy, and immune response
  • Reproducibility and Reporting: Best practices for reporting vaccine design results, ensuring reproducibility, and avoiding overclaims
  • Tools: AutoDock, Chimera, MDAnalysis, VMD, and optional PLUMED overview
  • Mini task: Create a 1-page in silico vaccine design report (including antigen structure, mRNA optimization, and docking results)
Module 7

Who Should Enrol?

  • Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
  • Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
  • University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
  • Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
  • Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Module 8

Important Dates

Module 9

Registration Ends

02/01/2026
IST 07:00 PM

Module 10

Workshop Dates

02/01/2026 – 02/03/2026
IST 08:00 PM

Module 11

Workshop Outcomes

Participants will be able to:

  • Design protein and mRNA vaccine candidates using in silico methods.
  • Predict potential T-cell and B-cell epitopes for optimal immune response.
  • Optimize mRNA constructs for enhanced translation and stability.
  • Apply immune system modeling to predict vaccine efficacy.
  • Generate vaccine constructs ready for preclinical validation.
Module 12

Meet Your Mentor(s)

Prof. Kumud Malhotra

Professor & Dean

Prof. Kumud Malhotra, Dean of the University Institute of Physical and Life Sciences with 30 years of experience is an academician and administrator and has attained the highest echelons in the educational sector by managing senior positions, like Director, Dean, Managing Editor, or Editor-in-Chief . . . for content development and journal publication. She completed her Ph. D on “Genetic Epidemiology of Malaria in Some Population Groups of Delhi and Tribal Groups of Tarai Region of Nainital District” and Post Doctorate from Delhi University as an ICMR and UGC fellow.
Her expertise domains are Biotechnology, Genetics, Bioinformatics, Forensic  Science, Molecular Biology, and its allied areas. She has obtained Certifications in the areas of Genetic Engineering, Molecular Biology, Microarrays, Proteomics, and Parasitology from IGIB, NICD, MRC, and Delhi University. Down the lane, her achievements and accolades are the best employee and faculty while working at Bioinformatics Institute of India and RNB GLOBAL University as a Dean and other prestigious awards. Her contribution to the establishment of the School of Basic and Applied Sciences and Agricultural Sciences along with the approval from ICAR at the new University RNBGLOBAL
University was acknowledged by the Management.
Apart from teaching and academics her experience in content development and e-learning for various renowned universities and organizations like Banasthali Vidyapith, GOLS Academy, Biionline, etc. is an added credential of hers. Dr. Kumud Malhotra has been a Resource Person in Course Development Committees (for graduate and post-graduate programs) for Indira Gandhi National Open University (IGNOU), Jamia Hamdard along with other credits like Member of various committees like Academic Council, BOS, Research Committee, etc. for the IGNOU, IAMR. She has demonstrated caliber as a resourceful Technical Collaborator for the conduction of the Workshop on Bioinformatics at
Bundelkhand University and in Engineering College in Durg, Chhattisgarh with an appreciation letter from the university.
She is a member of the editorial board of various journals and has presented Papers at International Conferences on Genetics, Parasitology, Clinical Trials, Pharmacovigilance, Biotechnology, etc. Dr. Malhotra has delivered keynote addresses at CII, International Conferences, and member of various professional bodies Genetics Society, Indian Science Congress, International Society for Malariology and Parasitology, IAA, SBTI, etc. She has guided many students for Ph.D. and internships and got the projects from ICMR.

Module 13

Fee Structure

Module 14

Student Fee

₹1799 | $70

Module 15

Ph.D. Scholar / Researcher Fee

₹2799 | $80

Module 16

Academician / Faculty Fee

₹3799 | $95

Module 17

Industry Professional Fee

₹4799 | $110

Module 18

What You’ll Gain

  • Live & recorded sessions
  • e-Certificate upon completion
  • Post-workshop query support
  • Hands-on learning experience
Module 19

Join Our Hall of Fame!

Take your research to the next level with NanoSchool.

Module 20

Publication Opportunity

Get published in a prestigious open-access journal.

Module 21

Centre of Excellence

Become part of an elite research community.

Module 22

Networking & Learning

Connect with global researchers and mentors.

Module 23

Global Recognition

Worth ₹20,000 / $1,000 in academic value.

Module 24

Need Help?

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Instructor

Lead Instructor

Dr. Sarah Chen

PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.

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Molecular Bioinformatics & In Silico Genetic Engineering

Professional Certification Program

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FormatLive + Recorded
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Duration8 Weeks
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CertificationVerified
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