Programme Parameters
Foundation
3 Days (4.5 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
Proteins are important gears of life and in order to understand the functions of proteins at a molecular level, it is necessary to determine its 3D structure which enables researchers to get an insight into its function and their role, or more, specific spatial conformations to perform its biological function, driven by a number of noncovalent interactions. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Proteins are important gears of life and in order to understand the functions of proteins at a molecular level, it is necessary to determine its 3D structure which enables researchers to get an insight into its function and their role, or more, specific spatial conformations to perform its biological function, driven by a number of noncovalent interactions.
📋 Course Objectives
1. Apply biotechnology methods to authentic research and industry problems.
2. Assemble a documented case study that evidences your applied capability.
👥 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
🚀 Key Learning Outcomes
• Tangible, reproducible biotechnology work to show supervisors or employers.
• 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
The Structure Prediction Problem
• Levinthal's paradox, folding principles and why prediction was hard
• Experimental methods and where they leave gaps
• CASP and the evidence behind claimed accuracy
How AlphaFold Works
• Multiple sequence alignments and co-evolutionary signal
• Evoformer and structure module at a conceptual level
• Why MSA depth largely determines prediction quality
Practical Prediction
• AlphaFold, ColabFold and the AlphaFold Database compared
• Compute, memory and runtime expectations
• Predicting complexes and the additional uncertainty this introduces
Reading a Prediction Correctly
• pLDDT and PAE: what each measures and how to use them together
• Low-confidence regions and their frequent correspondence to disorder
• A single predicted conformation is not a description of dynamics
Using Predicted Structures
• Docking and virtual screening against predicted models, and the caveats
• Mutation analysis and interpreting effects on stability
• When experimental structure determination remains necessary
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
Frequently Asked Questions
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