2026 Amber Workshop Tutorials

Welcome to the 2026 Amber Workshop Tutorials!

This workshop is taking place in Donostia, Basque Country, Spain from July 13th to July 17th, 2026.

For details and registration, see the workshop web page: 2026 Amber Workshop.

Warning

These tutorials are currently under development! Please check back closer to the workshop date for the finalized versions.

Day 1: Introduction to force fields, visualization, and Amber

• Hands-On Session 1: Use VMD and CPPTRAJ to visualize and analyze AMBER simulations
  • Learning Objectives
  • Relevant literature
  • Tutorial
    • 1.1 VMD Overview
    • 1.2 How to Access VMD
    • 1.3 VMD Basics
    • 1.4 Customizing Representations
    • 1.5 Measuring Geometrical Properties
    • 1.6 Keywords and Aligning
    • 1.7 Calculating Root-Mean-Square Deviation (RMSD)
    • 1.8 Image Rendering
    • 2.1 Introduction to CPPTRAJ
    • 2.2 Wrapping Trajectories
    • 2.3 Trajectory Time Series and Distribution Analysis
    • 2.4 Computing Average Structures and Fluctuations
• Hands-On Session 2: Introduction to Molecular Dynamics Simulations with Amber
  • Learning Objectives
  • Relevant Literature
  • Tutorial
    • Starting structure and input files
    • Accessing the tutorial files
    • Equilibration and Production Workflow
      • 1. Equilibrating the Box
      • Equilibration input files
        • Running the equilibration
        • Wrapping and inspection
      • 1. Running Production Simulations
        • The production SLURM script
        • Preparing the production input files
        • Running the production simulation
        • Wrapping and visualization of the production trajectory
      • 1. (Optional) Additional Exercise: Performing a brief energy minimization

Day 2: Generating non-standard force-field parameters in Amber

• Hands-On Session 3: Build an RNA enzyme (Ribozyme) containing 12-6-4 divalent ions and non-standard residues
  • Learning Objectives
  • Relevant literature
  • Tutorial
• Hands-On Session 4: Calculating Free Energy Surfaces Using QM/MM-Δ Machine Learning Potentials
  • Learning Objectives
  • Relevant literature
  • Tutorial
    • Build the QM system - 1D methyl transfer in MTR1
    • Preparing the inputs
    • Equilibrating the umbrella windows
    • Running umbrella sampling
    • Applying a Δ machine learning potential
    • Using an ab initio DFT potential with QUICK
    • Generating free energy profiles with NDFES
    • Comparison with DFT
• Hands-On Session 5: Perform SASM calculations to study catalytic mechanisms of a ribozyme
  • Learning Objectives
  • Relevant literature
  • Tutorial

Day 3: QM/MM and Delta Machine Learning Potential Models and Free Energy Surfaces

• Hands-On Session 6: Generate Protein forcefield parameters for drug-like molecules utilizing charge fitting procedures and the general AMBER force field
  • Learning Objectives
  • Tutorial
    • Overview of Ligand Parameterization and Terminology
    • Parameterizing with AM1-BCC Charges
    • Parameterizing with RESP Charges
    • Parameterizing with LigandParam
  • Relevant literature
• Hands-On Session 7: Using QM and Machine Learning Potentials to Parameterize Torsion Terms for Flexible Molecules
  • Learning objectives
  • Relevant literature
  • Tutorial
    • Linear Dihedral Scans
    • WaveFront Dihedral Scans
      • Introduction to WaveFront Scans
      • Scans on the mmm_NN Model System
      • Another Example: Linear Scans Overestimate Barriers
    • Dihedral Fitting

Day 4: Alchemical Free Energy Methods

• Hands-On Session 8: Analyzing Alchemical Free Energy Results Using FE-Toolkit
  • Learning Objectives
  • Relevant literature
  • Tutorial
• Hands-On Session 9: Perform AFE simulations for Relative Binding, using free energy workflows
  • Learning Objectives
  • Relevant literature
  • Tutorial
    • Getting Started
    • System Information
    • Getting Structures and Parameters
    • Preparing the Simulation Directory
    • Running FE-WORKFLOW
    • Examining the Outputs of the Build
    • Modifying Atom Mappings
    • Submitting Jobs to the Cluster
    • Analyzing Results
• Hands-On Session 10: Perform AFE simulations for Absolute Binding, using free energy workflows
  • Learning objectives
  • Relevant literature
  • Outline
  • Tutorial
    • Building Simulation Boxes for ABFE Calculations
      • Building the Ligand in the Aqueous Phase
      • Calculating the Number of Ions to Add
      • Building the Bound Protein-Ligand Complex
      • Optional: Applying Hydrogen Mass Reparationing (HMR)
    • Setting Up and Running End-State Equilibration
      • Equilibrating the Aqueous Endstates
      • Equilibrating the Complex Endstates
    • Determining Boresch Restraints
      • Theory of Boresch Restraints
      • How to Determine Boresch Restraints
    • Setting Up and Running Annihilation Simulations
      • Choosing A Lambda Schedule
      • Annihilation Simulation Setup
    • Equilibrating the Lambda Windows
      • Binder Equilibration
      • Complex Equilibration
      • Running Production Simulations
      • Binder Production
      • Complex Production
    • Getting Started
    • Overview of the Outputs from An ABFE calculation
      • Extracting Data using Amber2Dats
      • Running Edgembar
      • Creating Free Energy HTML Reports
      • Understanding the Results
    • Running ABFE Calculations with AmberFlow
      • Parameterizing the Ligands
      • Building the Systems
      • Running End State Equilibrations
      • Determining Boresch Restraints Using AmberFlow
      • Running the ABFE Flow within AmberFlow
      • Analyzing the Results
      • Further Reading

Day 5: Machine Learning Models for Accurate Free Energy Predictions

• Hands-On Session 11: Transfer learning: active learning to fine-tune a foundational machine learning potential to a specific target system for improved accuracy
  • Learning Objectives
  • Relevant literature
  • Tutorial
    • Generate training data from AMBER QM/MM simulaitons
    • Generating input files for a DeePMD-Kit
    • Training a model
    • Model validation
    • Apply transfer learning
    • Model deviation for data-efficient active learning
    • Addtional actiivity: on-the-fly active learning
• Hands-On Session 12: Indirect free energy “book-ending”: efficiently correct low-level MM force field results to high-level QDπ
  • Learning Objectives
  • Relevant literature
  • Tutorial
• Hands-On Session 13: Training AI-FEP Models for High-Throughput Virtual Screening
  • Learning Objectives
  • Relevant literature
  • Tutorial