2024 Amber Workshop Activities

Welcome to the 2024 Amber Workshop Activities!

These activities were initially created for the 2024 Amber Workshop held at the San Diego Supercomputer Center (SDSC). They are designed to help you understand and utilize Amber for state-of-the-art free energy and machine learning calculations.

As these activities were designed to be used in a specific workshop setting, there may be some assumptions about the environment and tools available. However, they are structured to be self-contained, allowing you to follow along independently on your own system. Unless otherwise noted, these activities require some combination of a slurm-based HPC system, Ambertools, Amber, and Gaussian.

Warning

These activities were created prior to the transition to the new york group activities page, and thus some links and references may be out of date. We are working to update these activities to be fully compatible with the new site.

System Preparation, Visualization, and Ligand Parameterization

• VMD and CPPTRAJ Basics for Visualization and Analysis of MD Trajectories
  • Learning objectives
  • Activities
    • 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
• Simulating the MTR1 ribozyme
  • Learning objectives
  • Relevant literature
  • Activities
    • 1.1 Cleaning the PDB file
    • 1.2 Building the Simulation Box
    • 1.3 Equilibrating the Box
    • 1.4 Running Production Simulations
    • 1.5 (Optional) Additional Exercise: Performing a brief energy minimization
  • References
• Preparing a Small Molecule from PDB to Simulation
  • Learning Objectives
  • Introduction
  • Initial Setup
    • Antechamber
    • parmchk2
    • tleap
  • References
• Parameterizing a non-standard residue for the MTR1 ribozyme
  • Learning objectives
  • Relevant literature
  • Outline
    • 1.1 Preparing a Gaussian input file
    • 1.2 Structure optimization and ESP charges with Gaussian
    • 1.2 Atom types and partial charges using antechamber and RESP procedure
    • 1.3 Building parameter files
  • References
• Building and Simulating TYK2 Protein Ligand System
  • Learning Objectives
  • Introduction
  • Initial Setup
    • pdb4amber
    • rdkit
    • antechamber
    • parmchk2
    • tleap
  • References

Free Energy Activities

• Reaction Free Energy Profiles from QM/MM simulations: Methyl Transfer reaction in MTR1
  • Learning objectives
  • Relevant literature
  • Outline
  • 1. 1-dimensional methyl transfer
    • 1.1 Preparing the inputs
    • 1.2 Generating the umbrella windows
    • 1.3 Running the umbrella sampling
    • 1.4 Generating the PMF
    • 1.5 Generating the combined PMF
    • Additional exercise
    • 1.6 Demonstrating the impact of umbrella window spacing
  • 2. 2-dimensional strings
    • 2.1 Introduction to running SASM simulations
    • 2.2 Analyzing a set of strings
    • Linear pathway
    • Stepwise (PT) pathway
    • Stepwise (MT) pathway
    • Additional exercises
    • 2.3 Visualizing the path progression
    • 2.4 Comparing the PMF at the semi-empirical and ab initio level
  • References
• Absolute Solvation Free Energy using ACES
  • Learning Objectives
  • Introduction
  • Initial Setup
  • Prepare for Simulations
  • Launch Alchemical Simulations
  • Perform Analysis using FE-Workflow
  • Perform Analysis using Edgembar
  • View Data
  • Optimizing λ Schedule
  • Final Remarks
  • References
• Relative Binding Free Energy using ACES
  • Learning Objectives
  • Introduction
  • Prepare for Simulations
  • Launch Alchemical Simulations
  • Download Data
  • Analyze Simulations
  • References

Machine Learning Potential Activities

• QM/MM+ΔMLP simulations of the MTR1 ribozyme
  • Learning objectives
  • Relevant literature
  • Outline
    • 1.1 Performing a timing test
    • 1.2 Generating QM/MM+ΔMLP data
    • 1.3 Performing gwTP
    • 1.4 Comparing the PMF at the semi-empirical and ab initio level
  • References
• MM-to-QM/MM Free Energy “Bookend” Corrections using GFN2-xTB and QDπ2 machine learning potential.
  • Learning objectives
  • Relevant literature
  • Introduction
  • Running the simulations
  • Example
  • Analysis