About Workshop
This workshop provides an in-depth exploration of computational structural biology, emphasizing AlphaFold 3 and molecular dynamics (MD) simulations. Participants will learn to generate high-confidence 3D macromolecular structures, perform virtual screening and docking workflows, and analyze dynamic trajectories for structural stability. The course integrates hands-on training with cutting-edge tools such as AlphaFold 3, UCSF ChimeraX, DiffDock, AutoDock Vina, GROMACS, and PyMOL. Attendees will acquire practical skills to validate structural models, predict binding interactions, and produce publication-ready visualizations, making it ideal for applications in drug discovery, structural bioinformatics, and molecular research.
Aim
The aim of this workshop is to equip participants with advanced skills in computational structural biology, including 3D structure prediction, virtual screening, docking, and molecular dynamics analysis. The workshop focuses on interpreting deep-learning metrics, validating macromolecular structures, and applying computational tools to real-world biomedical research problems. Participants will gain the ability to integrate AI-driven predictions into experimental workflows and generate actionable insights for research and drug discovery.
What Participants Will Learn
- Understand macromolecular modeling principles and structural integrity assessment.
- Learn to preprocess PDB files and handle missing loops or structural gaps.
- Build and validate multi-entity complexes, including proteins, nucleic acids, and post-translational modifications.
- Set up high-throughput virtual screening workflows and apply ADMET filters.
- Analyze MD trajectories and visualize structural features using PyMOL and UCSF ChimeraX.
Structure
📅 Day 1: Macro-Molecular Modeling & Structural Integrity
- Core Objective: Generate high-confidence 3D macro-molecular structures and interpret deep-learning confidence metrics for structural validation.
- Curation of raw PDB files and fixing missing structural loops or gaps
- Running multi-entity complex predictions including Protein-DNA, Protein-RNA, and post-translational modifications
- Interpreting pLDDT, PAE, and ipTM matrices for confidence, domain positioning, and interface quality
- Quantitative structural validation and identifying structural strain before docking
🛠️ Hands-on:
- Hands-on Lab: Submit sequences to the AlphaFold 3 engine for multi-entity complex prediction and import the model into UCSF ChimeraX to highlight high-confidence and flexible regions
📅 Day 2: Virtual Screening & Target Docking Workflows
- Core Objective: Screen chemical libraries, apply drug-likeness filters, and map target-ligand binding interactions using docking workflows.
- Setting up high-throughput virtual screening workflows for large chemical datasets
- Applying ADMET filters and Lipinski’s Rule of 5 to remove unsuitable drug candidates
- Using DiffDock for global blind docking and AutoDock Vina for targeted active-site docking
- Understanding grid box parameterization and binding free energy calculation
🛠️ Hands-on:
- Hands-on Lab: Screen a target compound library against the validated Day 1 structural model and rank high-affinity lead molecules based on binding scores and orientation
📅 Day 3: Molecular Dynamics Trajectories & Manuscript Graphics
- Core Objective: Analyze molecular stability over time and generate clean, publication-ready structural biology visuals.
- Understanding system solvation, ion neutralization, and force-field assignment steps
- Reading and interpreting RMSD, RMSF, and hydrogen bond stability over time
- Plotting 2D and 3D non-bonded interaction networks
- Creating surface hydrophobicity meshes and structural interaction graphics
- Reporting AI-generated data in manuscripts and addressing reviewer comments
🛠️ Hands-on:
- Hands-on Lab: Analyze MD trajectory outputs and prepare publication-ready molecular interaction graphics
Important Dates
Workshop Dates
2026-07-11
8:00 PM IST
8:00 PM IST
What You Will Gain
Outcomes
- Generate high-confidence 3D structures of proteins, protein-DNA, and protein-RNA complexes using AlphaFold 3.
- Perform virtual screening of chemical libraries and evaluate binding interactions using DiffDock and AutoDock Vina.
- Interpret deep-learning confidence metrics such as pLDDT, PAE, and ipTM to validate structural models.
- Conduct molecular dynamics simulations to assess structural stability, RMSD, RMSF, and hydrogen bond networks.
- Create publication-ready 2D and 3D visualizations using PyMOL and UCSF ChimeraX.
- Integrate computational predictions into experimental pipelines for drug discovery and structural bioinformatics research.
Who Should Attend
- Researchers and PhD scholars in structural biology, computational biology, biochemistry, and bioinformatics.
- Professionals in drug discovery, pharmaceutical research, and biotech industries.
- Students or early-career scientists seeking expertise in AlphaFold 3, docking workflows, and molecular dynamics simulations.
- Data scientists and computational researchers interested in AI-driven protein modeling and structural analysis.