About :
Master AI-driven protein modeling with AlphaFold2 and beyond.
Aim :
This 3-month course equips researchers with expertise in AI-powered structural biology, focusing on AlphaFold2’s latest advancements (2024). Participants will progress from basic protein structure prediction to advanced applications like drug discovery and multi-scale modeling. Through hands-on sessions, they’ll learn to integrate AF2 with experimental data (Cryo-EM, MD), address limitations (disorder, PTMs), and explore cutting-edge tools (RFdiffusion, AlphaFold-3). The course emphasizes real-world applications, from antibody design to nucleic acid modeling, while fostering critical discussions on AI’s role in structural biology. By the end, attendees will be prepared to leverage AF2 in academia or industry.
Job Opportunity :
Computational Structural Biologist (Biotech/Pharma: Genentech, Relay Tx)AI Drug Design Scientist (Absci, Recursion Pharmaceuticals)Protein Engineer (Enzyme/antibody design startups)Cryo-EM Data Analyst (Core labs, academia)Scientific Software Developer (Building next-gen AF2 tools)
Objective :
Run and interpret AlphaFold2 predictions (single/multi-chain)
Validate AF2 models with MD simulations (GROMACS)
Predict protein-protein interactions using AF2-Multimer
Apply AF2 to drug discovery (docking, de novo design)
Integrate AF2 with experimental data (Cryo-EM, X-ray)
Navigate limitations (disorder, small molecules, RNA)
Deploy AF2 in industry-relevant workflows (antibody design)
Your Mentor Details
Duration : 3 Months
Start Date : -
End Date :
Product Structure :
Month 1: Foundations of Structural Biology & AI Week 1:
Introduction to structural biology techniques (Cryo-EM, X-ray crystallography)
Evolution of computational methods: From homology modeling to deep learning
Hands-on: Setting up AlphaFold2 (Colab/Local)
Week 2:
AlphaFold2 architecture: Evoformer, recycling, and confidence metrics (pLDDT, PAE)
Latest: AlphaFold2 updates (2024) for multi-chain complexesHands-on: Running AlphaFold2 on a single protein
Week 3:
Limitations of AlphaFold2: Disorder prediction, PTMs, and small molecules
Emerging: AlphaFold2 for RNA structures (RoseTTAFold-NA)Hands-on: Visualizing results in PyMOL/ChimeraX
Week 4:
Experimental validation: Integrating MD simulations (GROMACS) with AF2 predictions
Case Study: COVID-19 spike protein dynamics
Month 2: Advanced Applications & Multi-Scale Modeling Week 5:
Protein-Protein Interactions (PPIs):
AF2-Multimer for complex prediction
Latest: AI-driven interface scoring (DiffDock, AF2-Cluster)
Week 6:
Drug Discovery:
Virtual screening with AlphaFold2 (docking using AutoDock Vina)
Hot Topic: Generative AI for de novo protein design (RFdiffusion)
Week 7:
Membrane Proteins:
Challenges and AF2 adaptations (Alphafold-multimer for GPCRs)
Research Spotlight: Cryo-EM + AF2 hybrid workflows
Week 8:
Multi-Scale Modeling:
Integrating AF2 with coarse-grained methods (MARTINI)
Hands-on: Building a multi-resolution model
Month 3: Cutting-Edge Research & Capstone Week 9:
Beyond Proteins:
AF2 for nucleic acids, glycans, and ligands (AlphaFold-3)
2024 Breakthrough: Small molecule binding site prediction
Week 10:
Ethics & Limitations:
Over-reliance on AI predictions?
Debate: Should AF2 models replace experimental structures?
Week 11:
Industry Applications:
Biotech case studies (e.g., antibody design at Absci)
Guest Lecture: Structural biologist from industry
Week 12:
Capstone Project:
Solve a real-world structural biology problem using AF2
Present findings in a mock “journal club”
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Benefits to the participants :
Lead the AI-structural biology revolution with 2024’s most in-demand skillsBridge computation & experiment via hybrid modeling approachesAccess curated AF2 pipelines for proteins, RNA, and complexesLearn from industry experts (biotech/pharma applications)Build a capstone project for your portfolio
Outcomes :
Portfolio of AF2 models (proteins, complexes, nucleic acids)
MD-validated structure with functional annotations
Virtual screening pipeline for drug discovery
Critical analysis of AF2’s strengths/limitations
Capstone report (publishable format)
