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Update and expand RFdiffusion3 documentation
Added introductory and reference documentation files, improved the index structure with general information and examples, and enhanced the PPI design tutorial with additional notes, figures, and clarifications. Also fixed a typo in a PDB filename.
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models/rfd3/docs/index.rst

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RFdiffusion3 Documentation
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==========================
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RFdiffusion3 is a powerful protein design tool that operate on the atomic level to
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study ligand-protein interactions, create nucleic acid-protein interfaces, and
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design enzymes with specific catalytic activities. It is designed to be highly flexible and
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user-friendly, making it suitable for a wide range of applications in computational biology and biochemistry.
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General Information
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-------------------
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.. toctree::
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:maxdepth: 1
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:caption: General Information
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readme.md
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Tutorials
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---------
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.. toctree::
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:maxdepth: 1
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:caption: Contents
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ppi_design_tutorial.md
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Examples
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--------
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.. toctree::
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:maxdepth: 1
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:caption: Examples
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na_binder_design.md
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protein_binder_design.md
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symmetry.md
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enzyme_design.md
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# Understanding the RFdiffusion3 Inputs

models/rfd3/docs/ppi_design_tutorial.md

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## Before We Get Started...
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This tutorial does not cover installing RFD3, before continuing you should make sure that RFdiffusion3 (RFD3) is installed and able to be run on your system. See the [README](../README.md) for how to install RFD3. You will need to remember the path to the directory where you stored your checkpoint files.
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> [!NOTE]
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> The instructions below assume that you have installed RFD3 via the pip commands.
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> You may need to slightly modify how you run the calculations based on your setup.
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```{note}
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The instructions below assume that you have installed RFD3 via the pip commands.
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You may need to slightly modify how you run the calculations based on your setup.
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```
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Make sure you have activated any environments you used to install RFD3.
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```
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If you would like to compare your outputs against those generated by the authors of this tutorial, you can download the example output files here. <!-- TO DO: Create example output files.-->
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The 'basic' folder has neither of the settings in the [Other Useful Settings](#other-useful-settings) section. The 'redesign' and 'fixed' directories have the `redesign_motif_sidechains` and `select_fixed_atoms` options, respectively. The 'all' directory includes both of the optional settings.
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There is also an already made YAML file available here. <!-- TO DO: Add example YAML file --> We recommend following the tutorial to create this file yourself to better understand the RFD3 options that are relevant to PPI design.
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input: /path/to/rfd3_ppi_tutorial/4zxb_cropped.pdb
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```
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This file was directly cropped from the 4zxb structure that can be found in the [RSCB PDB library](https://www.rcsb.org/). *If you visualize the cropped structure against the full one from the RSCB library, they may not appear to be exactly the same structure. However, if you align the two you will get an RMSD of 0.0.*
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<!--![Image of the cropped and full 4ZXB structure, RMSD of 0.0.](.assets/ppi_tutorial/cropped_vs_full.png)-->
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<!--<img src=".assets/ppi_tutorial/cropped_vs_full.png" alt="Image of the cropped and full 4ZXB structure, RMSD of 0.0." width="60%">-->
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```{figure} .assets/ppi_tutorial/cropped_vs_full.png
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:width: 60%
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E88: CG,CZ
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E96: CD1,CZ
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```
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```{figure} .assets/ppi_tutorial/hotspots.png
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:width: 60%
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The hotspot residues along with the specific target atoms circled in yellow.
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```
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1. Next we need to add information about our ORI token, this token specifies where we want the center of mass of our designed protein to be. Unless you know where you want to place the ORI token for your specific design needs, it is often easiest to have RFD3 infer the ORI placement based on the chosen `hotspots`:
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```yaml
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infer_ori_strategy: hotspots
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```
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Your output files will be placed in a new directory `ppi_tutorial_outputs/0`. If you run the tutorial again, change the `0` to another number to not overwrite your outputs. Your output files will be named `ppi_tutorial_insulinr_0_model_n.cif.gz` where `n` is the number of the design. `ppi_tutorial` comes from the name of the YAML file and `insulinr` comes from the name you gave your calculation in the YAML file.
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> [!NOTE]
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> You may see several warning messages when you run RFD3, these should not interfere with your calculation.
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```{note}
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You may see several warning messages when you run RFD3, these should not interfere with the calculation.
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```
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## Analyzing the Outputs
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You should end up with 8 designs, numbered 0-7, each with its own `.cif.gz` and `.json` file. If you want to adjust the number, add the configuration option `diffusion_batch_size` to your `rfd3 design` command.
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The JSON file has many details about your diffusion run, including the options in your YAML file. The compressed CIF file that you can easily visualize with tools like PyMOL.
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Your results should look something like this:
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```{figure} .assets/ppi_tutorial/example_output_w_hotspots.png
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:width: 60%
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Green is the original input structure while blue is the designed binder. The hotspot residues are purple and represented as ball and sticks.
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```
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You'll notice that the binders are always on the side of the input structure closest to the hotspots. The lengths of the designed binders are all also between 40 and 120 amino acids long.
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## References and Further Reading
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- input.md
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- intro_inference_calculations.md
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-
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- <!-- Ask Rafi for recommendations, I'd like to at least include a resource saying where the hotspots came from-->
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- [input.md](input.md)
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<!-- - intro_inference_calculations.md need to make this file-->
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<!-- Ask Rafi for recommendations, I'd like to at least include a resource saying where the hotspots came from-->
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models/rfd3/docs/readme.md

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../README.md

models/rfd3/docs/readmelink.md

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README
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======
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.. include:: ../README.md
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:parser: myst_parser.sphinx_

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