Table 1:

List of workshop topics and learning objectives in Part I.

Current Topics	Students Will Be Able To
1.00 How to Get Started 1.01 PyRosetta Google Drive Setup 1.02 PyRosetta Google Drive Usage Example 1.03 How to Install Local PyRosetta	• Set up PyRosetta in Google Colab. • Set up PyRosetta on local computer (optional).
2.00 Intro to PyRosetta 2.01 Pose Basics 2.02 Working with Pose Residues 2.03 Accessing PyRosetta Documentation 2.04 Getting Spatial Features from Pose 2.05 Protein Geometry 2.06 Visualization and PyMOL Mover 2.07 RosettaScripts in PyRosetta 2.08 Visualization and pyrosetta.distributed.viewer	• Load a PDB structure. • Measure and alter protein structure (in internal or Cartesian coordinates). • Visualize macromolecules and PyRosetta ResidueSelectors within Jupyter Notebooks and through the PyMol-PyRosetta interface. • Run a RosettaScript from Python. • Instantiate and use individual configured components (objects) from a RosettaScript
3.00 Rosetta Energy Score Functions 3.01 Score Function Basics 3.02 Analyzing Energy Between Residues 3.03 Energies and the PyMOL Mover	• Test different score function components or weighted combinations.
4.00 Intro to Folding 4.01 Basic Folding Algorithm 4.02 Low-Res Folding and Fragments	• Explain the fundamental challenges of protein structure prediction. • Describe the use of protein fragments for building protein backbones. • Implement a Metropolis Monte Carlo search strategy. • Use standard PyRosetta protocols to optimize protein structure.
5.00 Structure Refinement 5.01 High-Res Movers 5.02 Refinement Protocol	• Implement a Monte Carlo-plus-minimization algorithm. • Use various standard PyRosetta Movers to manipulate protein structure.
6.00 Intro to Packing and Design 6.01 Side-Chain Conformations and Dunbrack Energies 6.02 Packing Design Regional Relax 6.03 Design with a Resfile and Relax 6.04 Protein Design 2 6.05 HBnet Before Design 6.06 Intro to Parametric Backbone Design 6.07 Intro to de novo Protein Design 6.08 Point Mutation Scan	• Optimize side-chain conformations for a set of specified residues using PyRosetta. • Write custom PyRosetta protocols to simultaneously optimize protein structure and sequence. • Integrate sidechain packing with small and shear moves and minimization in PyRosetta refinement protocols. • Precede sidechain packing with hydrogen bond network design • Design proteins using custom scorefunctions with non-pairwise decomposable scoreterms in PyRosetta. • Design symmetrical proteins using Parametric backbone design • Design families of proteins with regular arrangements of secondary-structure elements • Generate mutagenesis library for antigen-antibody binding with PyRosetta • Compare mutant and wild-type binding energy • Visualize mutagenesis results using a Python heatmap
7.00 Protein Docking 7.01 Fast Fourier Transform Docking 7.02 Docking Moves in Rosetta	• Describe the major approaches to docking (grid based, FFT, Monte Carlo) and their advantages and disadvantages. • Use the PyJobDistributor for job distribution
8.00 Ligand Docking PyRosetta 8.01 Ligand Docking XMLObjects 8.02 Ligand Docking pyrosetta.distributed	• Perform high-resolution protein-ligand refinement using the DockMCMProtocol mover. • Perform global ligand docking using XMLObjects. • Perform ligand docking with a genetic algorithm using pyrosetta.distributed.
9.00 Loop Modeling 9.01 Using Gen KIC	• Describe the loop modeling and loop closure problems. • Describe the cyclic coordinate descent and kinematic closure approach and identify their advantages and limitations. • Close loops using Gen KIC protocol.