RosettaPy 0.2.11

A Python utility for wrapping Rosetta command line tools.

RosettaPy

A Python Utility for Wrapping Rosetta Macromolecural Modeling Suite.

[!CAUTION] RosettaPy requires Rosetta compiled and installed. Before running RosettaPy, please DO make sure that you have obtained the correct license from Rosetta Commons. For more details, please see this page.

[!IMPORTANT] RosettaPy is NOT PyRosetta. You probably don't need to install this package if you are looking for PyRosetta. Please see this page.

License

CI Status

Quality

Release

Overview

RosettaPy is designed to handle the complexities of locating, composing and running Rosetta binaries within Python. The module includes:

Key Component	Description
Rosetta	A command-line wrapper for Rosetta that simplifies the setting up and running commands.
RosettaScriptsVariableGroup	Represents variables used in Rosetta scripts, facilitating their management and use.
RosettaEnergyUnitAnalyser	Analyzes and interprets Rosetta output score files, providing a simplified interface for result analysis.
Example Applications	Demos of specific Rosetta applications like PROSS, FastRelax, RosettaLigand, Supercharge, MutateRelax, and Cartesian ddG for different computational biology tasks.

Installation

One can install RosettaPy directly from PyPI:

pip install RosettaPy -U

Getting Started

Building Rosetta Workflow from scratch

1. Import necessary modules

   from RosettaPy import Rosetta, RosettaScriptsVariableGroup, RosettaEnergyUnitAnalyser
   from RosettaPy.node import RosettaContainer, MpiNode, Native
   

2. Create a Rosetta proxy with parameters

   rosetta = Rosetta(
       # a binary name for locating the real binary path
       bin="rosetta_scripts",
   
       # flag file paths (please do not use `@` prefix here)
       flags=[...],
   
       # command-line options
       opts=[
           "-in:file:s", os.path.abspath(pdb),
           "-parser:protocol", "/path/to/my_rosetta_scripts.xml",
       ],
   
       # output directory
       output_dir=...,
   
       # save pdb and scorefile together
       save_all_together=True,
   
       # a job identifier
       job_id=...,
   
       # silent the rosetta logs from stdout
       verbose = False,
   )
   

   RosettaPy uses Native node by default.

   It has to be noted that Native and MpiNode are only available on Linux and macOS. For Windows users, please refer to the Full Operating System Compatibility Table and Getting Windows Ready for Rosetta Runs sections below.

3. Compose rosetta tasks matrix as inputs

   tasks: list[Dict[str, Any]] = [ # Create tasks for each variant
       {
           "rsv": RosettaScriptsVariableGroup.from_dict(
               {
                   "var1": ...,
                   "var2": ...,
                   "var3": ...,
               }
           ),
           "-out:file:scorefile": f"{variant}.sc",
           "-out:prefix": f"{variant}.",
       }
       for variant in variants
   ]
   
   # pass task matrix to rosetta.run as `inputs`
   rosetta.run(inputs=tasks)
   

4. Using structure labels (-nstruct)

   Create distributed runs with structure labels (-nstruct <int>) is feasible. For local runs without MPI or container, RosettaPy implemented this feature by ignoring the build-in job distributer of Rosetta, canceling the default output structure label, attaching external structural label as unique job identifier to each other, then run these tasks only once for each. This enables massive parallalism.

   options=[...] # Passing an optional list of options that will be used to all structure models
   rosetta.run(nstruct=nstruct, inputs=options) # input options will be passed to all runs equally
   

5. Call Analyzer to check the results

   analyser = RosettaEnergyUnitAnalyser(score_file=rosetta.output_scorefile_dir)
   best_hit = analyser.best_decoy
   pdb_path = os.path.join(rosetta.output_pdb_dir, f'{best_hit["decoy"]}.pdb')
   
   # Ta-da !!!
   print("Analysis of the best decoy:")
   print("-" * 79)
   print(analyser.df.sort_values(by=analyser.score_term))
   
   print("-" * 79)
   
   print(f'Best Hit on this run: {best_hit["decoy"]} - {best_hit["score"]}: {pdb_path}')
   

   One can also build a customized analyser by re-using the analyser.df Dataframe.

Being Like a Pro

Here are some tips for advanced usages to adjust the workflow in respect to behaviors of some Rosetta workflows and applications.

Isolation Mode

Some Rosetta Apps (Superchange, Cartesian ddG, etc.) may produce files at their working directory, and this may not threadsafe if one runs multiple jobs in parallel in the same directory. In this case, the isolation flag can be used to create a temporary directory for each run.

Rosetta(
    ...
+   isolation=True,
)

Running Node Configurations

There are various node configurations that can be used to run Rosetta jobs.

Full Operating System Compatibility Table

Node	Linux	macOS	Windows	Architectures	Prerequisite
Native	✅[^1]	✅	❌	x86_64, aarch64	Rosetta compiled.
MpiNode	✅[^1]	✅	❌	x86_64, aarch64	Rosetta compiled with extras=mpi flag; MPI installed
RosettaContainer	✅	✅[^3]	✅	x86_64	Docker or Docker Desktop installed and launched.
WslWrapper[^2]	❌	❌	✅[^1]	x86_64, aarch64[^4]	WSL2, with Rosetta built and installed on.

[^1]: For building Rosetta upon aarch64, please refer to this example. [^2]: Windows Subsystem for Linux(WSL) installed and switched to WSL2, with Rosetta built and installed on. [^3]: Translated with Rosetta2 framework if runs on Apple Silicon Mac, which may cause worthy slow performance. [^4]: It's theoretically possible yet no testing is done at all.

MPI, or not MPI?

This Flow chart shows how RosettaPy decides whether to use MPI or not.

Before RosettaContainer or WslWrapper instance is passed as run_node, the node itself checks if MPI-related environment is ready. Also, the flag prohibit_mpi will force to disable MPI if it is True.

Reallocating CPU Cores

By default, RosettaPy uses Native node with 4 CPU cores, representing the local machine with Rosetta installed. To specify the number of cores, use the nproc parameter.

Rosetta(
    ...
+   run_node=Native(nproc=8)
)

Native MPI Support for local builds of Rosetta

Requires MPI(mpich, openmpi, etc.) installed.

If one wish to run with Rosetta that was installed on local and built with extra=mpi flag via MPI, consider using MpiNode instance as run_node instead. This enables native parallelism feature with MPI.

Rosetta(
    ...
+   run_node=MpiNode(nproc=10),
)

Also, if one wishes to use MpiNode with Slurm task manager, specifying run_node to MpiNode.from_slurm() may help with fetching the node info from the environment.

This is an experimental feature that has not been seriously tested in production.

Rosetta(
    ...
+   run_node=MpiNode.from_slurm(),
)

Rosetta Container

If one wishes to use the Rosetta container as the task worker, (WSL + Docker Desktop, for example) setting a run_node option as RosettaContainer class would tell the proxy to use it. This image names can be found at https://hub.docker.com/r/rosettacommons/rosetta Note that the paths of each task will be mounted into the container and rewritten to the container's path. This rewriting feature may fail if the path is mixed with complicated expressions as options.

Non-mpi image:

Rosetta(
    ...
+   run_node=RosettaContainer(image="rosettacommons/rosetta:latest"),
)

or MPI image:

Rosetta(
    ...
+   run_node=RosettaContainer(image="rosettacommons/rosetta:mpi"),
+   use_mpi=True, # one still needs to enable MPI by this flag if MPI is required.
)

or non-MPI runs with MPI image:

Rosetta(
    ...
+   run_node=RosettaContainer(image="rosettacommons/rosetta:mpi", prohibit_mpi=True), # override MPI setting
)

During the workflow processing, one will see some active containers at Containers tab of Docker Desktop, if Docker Desktop is installed. Also, typing docker ps in the terminal will show them too. Each of these containers will be destructed immediately after its task finished or stopped.

Picking Node Accordingly

One can still pick the desire node quickly by calling node_picker method.

from RosettaPy.node import node_picker, NodeHintT

node_hint: NodeHintT = 'docker_mpi'

Rosetta(
    ...
+   run_node=node_picker(node_type=node_hint),
+   use_mpi=...,
)

Where node_hint is one of ["docker", "docker_mpi", "mpi", "wsl", "wsl_mpi", "native"]

Fetching additional scripts/database files from the Rosetta GitHub repository

[!CAUTION] AGAIN, before using this tool, please DO make sure that you have licensed by Rosetta Commons. For more details of licensing, please check this page.

This tool is helpful for fetching additional scripts/database files/directories from the Rosetta GitHub repository.

For example, if one's local machine does not have Rosetta built and installed, and wishes to check some files from $ROSETTA3_DB or use some helper scripts at $ROSETTA_PYTHON_SCRIPTS before run Rosetta tasks within Rosetta Container, one can use this tool to fetch them into the local harddrive by doing a minimum cloning.

The partial_clone function do will do the following steps:

1. Check if Git is installed and versioned with >=2.34.1. If not satisfied, raise an error to notify the user to upgrade git.
2. Check if the target directory is empty or not and the repository is not cloned yet.
3. Setup partial clone and sparse checkout stuffs.
4. Clone the repository and subdirectory to the target directory.
5. Setup the environment variable with the target directory.

import os
from RosettaPy.utils import partial_clone

def clone_db_relax_script():
    """
    A example for cloning the relax scripts from the Rosetta database.

    This function uses the `partial_clone` function to clone specific relax scripts from the RosettaCommons GitHub repository.
    It sets an environment variable to specify the location of the cloned subdirectory and prints the value of the environment variable after cloning.
    """
    # Clone the relax scripts from the Rosetta repository to a specified directory
    partial_clone(
        repo_url="https://github.com/RosettaCommons/rosetta",
        target_dir="rosetta_db_clone_relax_script",
        subdirectory_as_env="database",
        subdirectory_to_clone="database/sampling/relax_scripts",
        env_variable="ROSETTA3_DB",
    )

    # Print the value of the environment variable after cloning
    print(f'ROSETTA3_DB={os.environ.get("ROSETTA3_DB")}')

Getting Windows Ready for Rosetta Runs

Thanks for Windows Subsystem for Linux(WSL), we provide two simple ways to run Rosetta on Windows.

One must enable Windows Subsystem for Linux, then switch to WSL2 following the instructions on this page.

Docker Desktop

1. Install Docker Desktop and enable WSL2 docker engine.

2. Search for the Image rosettacommons/rosetta:<label> where <label> is the version of Rosetta build one want to use.

   • Note: network proxies or docker registry mirror setting may be required for users behind the GFW.

3. Use RosettaContainer class as the run node, with the image name one just pulled.

4. Make sure all the input files are using LF ending instead of CRLF. This is fatal for Rosetta to parse input files.

   • Note: this issue now can be done by using a context manager convert_crlf_to_lf from RosettaPy.utils.tools. Example:

   from RosettaPy.utils.tools import convert_crlf_to_lf
   
   with convert_crlf_to_lf(input_file) as output_file:
       """Use `output_file` to replace `input_file`."""
   

5. Build Rosetta workflow with RosettaPy and run it.

WSL Wrapper

1. Install any recent release of Linux Distribution (e.g., Ubuntu-22.04) and setup for the account.
2. Install build essential tools: apt-get update && apt-get install build-essential git -y
   • Note: network proxies or apt repostory mirror setting may be required for users behind the GFW.
3. Install MPI: apt-get install mpich -y. MPICH is sufficient for Rosetta.
4. Install Python: apt-get install python-is-python3 -y
5. Fetch the source code of Rosetta and un-tar it to anywhere convenient. e.g. /opt/rosetta
6. Go to the source code directory and build it according to the Official Rosetta Build Documentation.
7. Environment variables required by RosettaPy are:
   1. ROSETTA_BIN: path to the Rosetta executables
   2. ROSETTA3_DB: path to the Rosetta database
   3. ROSETTA_PYTHON_SCRIPTS: path to the Rosetta scripts
8. Use WslWrapper class as the run node. Parameters:
   1. rosetta_bin: RosettaBinary with in-wsl dirname(the absolute path of Rosetta binary directory in WSL distro).
   2. distro: the name of the Linux Distribution. for example, 'Ubuntu-22.04'
   3. user: the name one just setup as the user in the Linux Distribution.
   4. nproc: number of CPU cores to be used.
   5. prohibit_mpi: whether to prohibit MPI.

Environment Variables

The RosettaFinder searches the following directories by default:

0. PATH, which is commonly used in dockerized Rosetta image.
1. The path specified in the ROSETTA_BIN environment variable.
2. ROSETTA3/bin
3. ROSETTA/main/source/bin/
4. A custom search path provided during initialization.

Naming Convention

The Rosetta binaries are expected to follow this naming pattern:

rosetta_scripts[[.mode].oscompilerrelease]

• Binary Name: rosetta_scripts (default) or specified.
• Mode (optional): default, mpi, or static.
• OS (optional): linux or macos.
• Compiler (optional): gcc or clang.
• Release (optional): release or debug.

For reg expression to match the basenames:

^(?P<binary_name>[\w]+)((\.(?P<mode>static|mpi|default|cxx11threadserialization|cxx11threadmpiserialization))?(\.(?P<os>linux|macos)(?P<compiler>gcc|clang)(?P<release>release|debug)))?$

See this regex101 page for more details.

Examples of valid binary filenames:

• rosetta_scripts (dockerized Rosetta)
• rosetta_scripts.linuxgccrelease
• rosetta_scripts.mpi.macosclangdebug
• rosetta_scripts.static.linuxgccrelease

Running Tests

The project includes unit tests using Python's pytest framework.

1. Clone the repository (if not already done):

   git clone https://github.com/YaoYinYing/RosettaPy.git
   

2. Navigate to the project directory and install the required dependencies:

   cd RosettaPy
   pip install '.[test]'
   

3. Run the tests:

   # quick test cases
   pytest ./tests -m 'not integration'
   
   # test integration cases
   pytest ./tests -m 'integration'
   
   # run integration tests with both docker and local
   export GITHUB_CONTAINER_ROSETTA_TEST=YES
   pytest ./tests -m 'integration'
   

Contributing

Contributions are welcome! Please submit a pull request or open an issue for bug reports and feature requests.

Acknowledgements

• Rosetta Commons: The Rosetta software suite for the computational modeling and analysis of protein structures.
• CIs, formatters, checkers and hooks that save my life and make this tool improved.
• ChatGPT, Tongyi Lingma, DeepSource Autofix™ AI and CodeRabbit for the documentation, code improvements, test cases generations and code revisions.

Contact

For questions or support, please contact:

• Name: Yinying Yao
• Email:yaoyy.hi(a)gmail.com

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