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A GUI for visualizing and interacting with score vs RMSD plots.

Project description

The purpose of this program is to make it easier to judge forward-folded candidates in computational protein design pipelines.

Most computational design pipelines have one step that searches for sequences which satisfy the design goals and another that simulates some of those sequences to see which really do satisfy those goals. This second step is usually called forward-folding or computational validation. For each design chosen to be validated, hundreds of forward-folding simulations may be run. Each of these produces a single model which can be characterized by metrics expressing how realistic it is and how well it satisfies the design goals. These metrics include force-field scores, RMSDs to target structures, buried unsatisfied H-bond counts, and possibly other things like that. A design is promising if the forward-folded models that best satisfy the design goals are also the most realistic.

This program provides a number of utilities and features to make it easier to find promising designs:

  1. Extract quality metrics from forward-folded models and plot them against each other in any combination.
  2. Easily visualize specific models by right-clicking on plotted points.
  3. Plot multiple designs at once, for comparison purposes.
  4. Keep notes on each design, and search your notes to find the designs you want to visualize.


The most difficult part of installing show_my_designs is making sure all the required dependencies are present:

  • python 2.7
  • pygtk
  • numpy
  • scipy
  • pandas
  • numexpr

On linux, these most of these should already be installed, and all of these should be available through whatever package manager your distribution uses. On mac, homebrew seems to be the most promising route, but I haven’t tried it.

Bugs and New Features

If you find a bug, open an issue through the github interface:

If you’d like to fix a bug or make an improvement to the code, fork the project and make a pull request:


Use the -h flag to get help on using show_my_designs:

$ ./ -h Usage: …

Generally, the only arguments you need are the names of one or more directories containing the forward-folded models in the PDB format. For example:

$ ls

$ ls design_1

$ ./ design_*

This last command will launch the GUI. If you specified more than one design on the command line, the GUI will have a panel on the left listing all the designs being compared. You can control what is plotted by selecting one or more designs from this list. The search bar at the top of this panel can be used to filter the list for designs that have the search term in their descriptions. The buttons at the bottom can be used to save information about whatever designs are selected. The “Save selected paths” button will save a text file listing the path to the lowest scoring model for each selected design. The “Save selected funnels” button will save a PDF with the plot for each selected design on a separate page.

The upper right area of the GUI will contain a plot with different metrics on the two axes where each point represents a single model. You can right-click on any point to take an action on the model represented by that point. Usually this means visualizing the model in an external program, like pymol or chimera. You can also run your own custom scripts; see the “customization” section below for more information.

The tool bar below the plot can be used to pan around, zoom in or out, save an image of the plot, or change the axes. If the mouse is over the plot, its coordinates will be shown just to the right of these controls. Below the plot is a text form which can be used to enter a description of the design. These descriptions can be searched. I like using the ‘+’, ‘++’, … convention to rank designs so I can easily search for increasingly good designs.


Because every protein design pipeline is different, show_my_designs was written to be flexible. Providing a new way to visualize specific models is trivial. You just need to write a script with the extension *.sho that takes the path of a model as its only argument. show_my_designs will search for scripts with this extension in every directory starting with the directory containing the model in question and going down all the way to the root of the file system. Any scripts that are found are added to the menu you get by right-clicking on a point, using simple rules (the first letter is capitalized and underscores are converted to spaces) to convert the file name into a menu item name.

Another common modification is to change what metrics are extracted for each model. By default, only the metrics that outputted by rosetta’s loop modeling framework are extracted. The metrics include: the rosetta fullatom score, the RMSD to the native backbone, and the number of buried unsatisfied H-bonds. To add new metrics, you have to monkey-patch the show_my_designs module and call show_my_designs.main() from your own script.

This is more clear with an example. Say your forward-folding simulation outputs an auxiliary file for each model containing all sorts of metrics relevant to your particular system. You can add support for these metrics by reimplementing show_my_designs.parse_records_from_pdbs(). This function takes a list of paths to PDB files that haven’t been cached yet and returns a list containing {'metric_name': metric_value} dictionaries for each one. The information in this list is cached so that it doesn’t have to be regenerated unless necessary.

If your custom metrics are encoded in the PDB file itself, you can reimplement show_my_designs.parse_record_from_pdb() instead. This function is called by show_my_designs.parse_records_from_pdbs() and with a list of the lines in a specific PDB file. It is expected to return the {'metric_name': metric_value} dictionary for that model.


  • j,f,down: Select the next design, if there is one.
  • k,d,up: Select the previous design, if there is one.
  • i,a: Focus on the description form.
  • z: Use the mouse to zoom on a rectangle.
  • x: Use the mouse to pan (left-click) or zoom (right-click).
  • c: Return to the original plot view.
  • slash: Focus on the search bar.
  • tab: Change the y-axis metric.
  • space: Change the x-axis metric.
  • escape: Unfocus the search and description forms.

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