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# Af-analysis: a Python package for Alphafold analysis

Alaa Reguei, Samuel Murail

► To cite this version:

Alaa Reguei, Samuel Murail. Af-analysis: a Python package for Alphafold analysis. Journal of Open Source Software, 2025, 10 (107), pp.7577. 10.21105/joss.07577. hal-05415824

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Submitted on 15 Dec 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Af-analysis: a Python package for AlphaFold analysis

Alaa Reguei¹ and Samuel Murail ¹,²¶

1 Université Paris Cité, Inserm, CNRS, BFA, F-75013 Paris, France 2 Ressource Parisienne en Bioinformatique Structurale (RPBS), F-75013 Paris, France ¶ Corresponding author

DOI: 10.21105/joss.07577

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Submitted: 12 October 2024

Published: 14 March 2025

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## Summary

The publication of AlphaFold 2 (Jumper et al., 2021) has significantly advanced the field of protein structure prediction. The prediction of protein structures has long been a central challenge in the field of structural bioinformatics, with the ultimate goal of elucidating the relationship between protein structure and function (Baker & Sali, 2001; Pearce & Zhang, 2021). Accurate prediction of protein structure is essential for a number of applications, including drug discovery, protein engineering, and the study of protein-protein interactions. AlphaFold, which employs a deep learning-based approach, has demonstrated unprecedented accuracy in protein structure prediction, outperforming other contemporary methods. In this paper, we present *af-analysis*, a Python package that provides tools for the analysis of AlphaFold results. The *af-analysis* library has been designed to facilitate the analysis of many different protein structures predicted by AlphaFold and its derivatives. It provides functions for comparing predicted structures with experimental structures, visualising predicted structures, and calculating structural quality metrics.

## Statement of need

With the release of AlphaFold 2 (Jumper et al., 2021) in 2021, the scientific community has achieved an unprecedented level of accuracy in predicting protein structures. Derivatives of AlphaFold 2, namely ColabFold (Mirdita et al., 2022), AlphaFold Multimer (Evans et al., 2022), AlphaFold 3 (Abramson et al., 2024) and its re-implementations such as Boltz-1 (Wohlwend et al., 2024) and Chai-1 (Discovery et al., 2024) have been developed to predict the structure of protein complexes, setting a new standard for protein-protein and protein-peptide docking.

Analysis of AlphaFold results is a crucial step in the process of utilising these predictions for scientific research. The AlphaFold software provides several excellent quality metrics that offer valuable information about the accuracy of the predicted structures. Among these scores, the predicted local distance difference test (pLDDT) is a per-residue measure of local confidence, as the predicted aligned error (PAE) provides confidence over the relative position of two residues within the predicted structure. To analyse these results, the AlphaBridge webserver (Álvarez-Salmoral et al., 2024) and the PICKLUSTER plugin (Genz et al., 2023) for the UCSF ChimeraX visualisation software were developed to characterise the different interfaces within protein complexes, and extract their respective scores.

Although these tools are very practical, Bjorn Wallner has shown that calculating 5 or 25 basic AlphaFold models may not be enough, it is sometimes necessary to generate thousands of models to obtain a few high quality models, leading to the AlphaFold derivative, AFsample (Wallner, 2023). Massive sampling altogether with multiple software usage (AFsample and ColabFold), weights and parameters has been integrated into the MassiveFold software (Raouraoua et al., 2024) and has shown performance approaching the accuracy of AlphaFold 3.

The subsequent analysis of hundreds to thousands of models can prove to be a tedious and
meticulous process, as dealing with thousands of models and different output formats can be time consuming. Furthermore, while the quality metrics produced by AlphaFold are good, additional metrics have been developed to assess the quality of the models. These include pdockq (Bryant et al., 2022), pdockq2 (Zhu et al., 2023), and LIS score (Kim et al., 2024). All of these metrics have to be calculated from different scripts. Another point to consider is the diversity of the models. As shown in AFsample, it is sometimes necessary to compute up to tens of thousands of models and then cluster them in order to select the best ones. The af-analysis library has been developed to facilitate the analysis of sets of model structures and associated metrics. The library is based on the pandas library and is able to import AlphaFold 2 and 3, ColabFold, Boltz-1 and Chai-1 prediction directory as a pandas DataFrame. The library provides a number of functions to add further metrics to the DataFrame, compare models with experimental structures, visualise models, cluster models and select the best models.

## References

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