af-analysis 0.1.1

`AF analysis` is a python library allowing analysis of Alphafold results.

[](https://af2-analysis.readthedocs.io/en/latest/?badge=latest) [](https://codecov.io/gh/samuelmurail/af_analysis) [](https://dev.azure.com/samuelmurailRPBS/af_analysis/_build/latest?definitionId=2&branchName=main) [](https://pypi.org/project/af-analysis/) [](https://pepy.tech/project/af2-analysis)

# About Alphafold Analysis

<img src=”https://raw.githubusercontent.com/samuelmurail/af_analysis/master/docs/source/logo.jpeg” alt=”AF Analysis Logo” width=”200” style=”display: block; margin: auto;”/>

af-analysis is a python package for the analysis of AlphaFold protein structure predictions. This package is designed to simplify and streamline the process of working with protein structures generated by [AlphaFold 2][AF2], [AlphaFold 3][AF3] and its derivatives like [ColabFold][ColabFold], [AlphaFold-Multimer][AF2-M] and [AlphaPulldown][AlphaPulldown].

• Source code repository:

  [https://github.com/samuelmurail/af_analysis](https://github.com/samuelmurail/af_analysis)

## Statement of Need

AlphaFold 2 and its derivatives have revolutionized protein structure prediction, achieving remarkable accuracy. Analyzing the abundance of resulting structural models can be challenging and time-consuming. Existing tools often require separate scripts for calculating various quality metrics (pDockQ, pDockQ2, LIS score) and assessing model diversity. af-analysis addresses these challenges by providing a unified and user-friendly framework for in-depth analysis of AlphaFold 2 results.

## Main features:

• Import AlphaFold or ColabFold prediction directories as pandas DataFrames for efficient data handling.

• Calculate and add additional structural quality metrics to the DataFrame, including:

  • pDockQ

  • pDockQ2

  • LIS score

• Visualize predicted protein models.

• Cluster generated models to identify diverse conformations.

• Select the best models based on defined criteria.

• Add your custom metrics to the DataFrame for further analysis.

## Installation

• af-analysis is available on PyPI and can be installed using pip:

`bash pip install af_analysis `

• You can install last version from the github repo:

`bash pip install git+https://github.com/samuelmurail/af_analysis.git@main `

• AF-Analysis can also be installed easily through github:

`bash git clone https://github.com/samuelmurail/af_analysis cd af_analysis pip install . `

## Documentation

The full documentation is available at [ReadTheDocs](https://af-analysis.readthedocs.io/en/latest/).

## Usage

### Importing data

Create the Data object, giving the path of the directory containing the results of the alphafold2/colabfold run.

`python import af_analysis my_data = af_analysis.Data('MY_AF_RESULTS_DIR') `

Extracted data are available in the df attribute of the Data object.

`python my_data.df `

### Analysis

• The analysis package contains several function to add metrics like [pdockQ][pdockq] and [pdockQ2][pdockq2]:

`python from af_analysis import analysis analysis.pdockq(my_data) analysis.pdockq2(my_data) `

### Docking Analysis

• The docking package contains several function to add metrics like [LIS Score][LIS]:

`python from af_analysis import docking docking.LIS_pep(my_data) `

### Plots

• At first approach the user can visualize the pLDDT, PAE matrix and the model scores. The show_info() function displays the scores of the models, as well as the pLDDT plot and PAE matrix in a interactive way.

<img src=”https://raw.githubusercontent.com/samuelmurail/af_analysis/master/docs/source/_static/show_info.gif” alt=”Interactive Visualization” width=”100%” style=”display: block; margin: auto;”/>

• plot msa, plddt and PAE:

`python my_data.plot_msa() my_data.plot_plddt([0,1]) best_model_index = my_data.df['ranking_confidence'].idxmax() my_data.plot_pae(best_model_index) `

• show 3D structure (nglview package required):

`python my_data.show_3d(my_data.df['ranking_confidence'].idxmax()) `

## Dependencies

af_analysis requires the following dependencies:

• pdb_numpy

• pandas

• numpy

• tqdm

• seaborn

• cmcrameri

• nglview

• ipywidgets

• mdanalysis

## Contributing

af-analysis is an open-source project and contributions are welcome. If you find a bug or have a feature request, please open an issue on the GitHub repository at https://github.com/samuelmurail/af_analysis. If you would like to contribute code, please fork the repository and submit a pull request.

## Authors

• Alaa Regei, Graduate Student - [Université Paris Cité](https://u-paris.fr).

• [Samuel Murail](https://samuelmurail.github.io/PersonalPage/>), Associate Professor - [Université Paris Cité](https://u-paris.fr), [CMPLI](http://bfa.univ-paris-diderot.fr/equipe-8/>).

See also the list of [contributors](https://github.com/samuelmurail/af_analysis/contributors) who participated in this project.

## License

This project is licensed under the GNU General Public License version 2 - see the LICENSE file for details.

# References

• Jumper et al. Nature (2021) doi: [10.1038/s41586-021-03819-2][AF2]

• Abramson et al. Nature (2024) doi: [10.1038/s41586-024-07487-w][AF3]

• Mirdita et al. Nature Methods (2022) doi: [10.1038/s41592-022-01488-1][ColabFold]

• Evans et al. bioRxiv (2021) doi: [10.1101/2021.10.04.463034][AF2-M]

• Bryant et al. Nat. Commun. (2022) doi: [10.1038/s41467-022-28865-w][pdockq]

• Zhu et al. Bioinformatics (2023) doi: [10.1093/bioinformatics/btad424][pdockq2]

• Kim et al. bioRxiv (2024) doi: [10.1101/2024.02.19.580970][LIS]

• Yu et al. Bioinformatics (2023) doi: [10.1093/bioinformatics/btac749][AlphaPulldown]

[AF2]: https://www.nature.com/articles/s41586-021-03819-2 “Jumper et al. Nature (2021) doi: 10.1038/s41586-021-03819-2” [AF3]: https://www.nature.com/articles/s41586-024-07487-w “Abramson et al. Nature (2024) doi: 10.1038/s41586-024-07487-w” [ColabFold]: https://www.nature.com/articles/s41592-022-01488-1 “Mirdita et al. Nat Methods (2022) doi: 10.1038/s41592-022-01488-1” [AF2-M]: https://www.biorxiv.org/content/10.1101/2021.10.04.463034v2 “Evans et al. bioRxiv (2021) doi: 10.1101/2021.10.04.463034” [pdockq]: https://www.nature.com/articles/s41467-022-28865-w “Bryant et al. Nat Commun (2022) doi: 10.1038/s41467-022-28865-w” [pdockq2]: https://academic.oup.com/bioinformatics/article/39/7/btad424/7219714 “Zhu et al. Bioinformatics (2023) doi: 10.1093/bioinformatics/btad424” [LIS]: https://www.biorxiv.org/content/10.1101/2024.02.19.580970v1 “Kim et al. bioRxiv (2024) doi: 10.1101/2024.02.19.580970 ” [AlphaPulldown]: https://doi.org/10.1093/bioinformatics/btac749 “Yu et al. Bioinformatics (2023) doi: 10.1093/bioinformatics/btac749”