Programme Parameters
Foundation
1 Day (1.5 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This intensive one-day hands-on course introduces participants to the core concepts and methodologies for predicting protein and nucleic acid structures. Participants will delve into advanced computational techniques such as homology modeling, ab initio prediction, and molecular dynamics simulations, gaining insights into how these approaches are used in drug discovery and structural genomics. Through practical exercises, attendees will learn to use cutting-edge tools and algorithms for structure determination and visualization. Across 4 Weeks, you will work hands-on with homology modeling, ab initio prediction, and structural genomics, then consolidate everything in a capstone project. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This intensive one-day hands-on course introduces participants to the core concepts and methodologies for predicting protein and nucleic acid structures. Participants will delve into advanced computational techniques such as homology modeling, ab initio prediction, and molecular dynamics simulations, gaining insights into how these approaches are used in drug discovery and structural genomics. Through practical exercises, attendees will learn to use cutting-edge tools and algorithms for structure determination and visualization.
📋 Course Objectives
1. Get comfortable working with homology modeling.
2. Build practical fluency in ab initio prediction.
3. Gain working command of structural genomics.
4. Put biotechnology techniques to work on real datasets and case studies.
5. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
• Data and computational scientists moving into homology modeling
🚀 Key Learning Outcomes
• Confidence to implement homology modeling in real projects.
• Confidence to reason about ab initio prediction in real projects.
• Confidence to apply structural genomics in real projects.
• A demonstrable biotechnology project for your research or industry portfolio.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Why Structure Can Be Predicted
• Sequence determines structure, and the limits of that statement
• Experimental methods and the coverage gaps prediction is filling
• CASP and CASP-RNA as the only unbiased assessments of accuracy
Comparative and Deep Learning Methods
• Homology modelling with SWISS-MODEL and MODELLER, and template selection
• AlphaFold and ESMFold, and the dependence on MSA depth
• Model quality assessment: pLDDT, PAE, Ramachandran and MolProbity
A Harder and Less Solved Problem
• RNA secondary structure with RNAfold and the accuracy actually achieved
• Tertiary prediction with SimRNA or RoseTTAFoldNA and its much lower reliability
• SHAPE and chemical probing data as restraints on prediction
Beyond a Single Static Model
• Molecular dynamics with GROMACS to test model stability
• Force field choice, solvation and the timescale a simulation can reach
• Conformational ensembles and why one predicted structure misleads
Applying Predicted Structures
• Docking and virtual screening into predicted models, with caveats
• Interpreting mutations and disease variants structurally
• Visualisation and honest figure preparation in PyMOL or ChimeraX
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Microsoft Excel |
| Covered Tool / Platform | Relevant Online Databases |
Frequently Asked Questions
Scholar Feedback & Reviews
Based on 0 scholar submissions
Rating Breakdown
No verified reviews published yet. Be the first to share your academic experience.
Leave Scholar Feedback
Your rating will help prospective scholars. Ratings below 3 stars are routed privately to the faculty mentor for immediate response.
