OpenFold: A Comprehensive Guide
OpenFold is a free and open-source software package that acts as a faithful, yet trainable reproduction of DeepMind's renowned AlphaFold 2. Developed using PyTorch, OpenFold is designed to replicate the functionality of AlphaFold 2 while allowing for additional flexibility in training and model adjustments.
Documentation and Installation
For individuals eager to delve into OpenFold, a dedicated set of documentation is available on the project’s new home at openfold.readthedocs.io. This resource provides comprehensive instructions on how to install the software and proceed with model inference and training. Additionally, much of the foundational content can be accessed via the GitHub repository’s original README file.
Copyright and Licensing
OpenFold and AlphaFold's source codes are open for public use under the Apache License, Version 2.0. However, it is important to note that the pretrained parameters from DeepMind fall under the Creative Commons Attribution 4.0 International License (CC BY 4.0). These parameters are automatically downloaded during the installation of OpenFold, stored in the openfold/resources/params directory, and are crucial for the functionality of the software.
Community and Contributions
OpenFold invites community engagement, welcoming users who encounter any issues to report them through their issue tracking system. Likewise, contributions through pull requests are highly encouraged, promoting a collaborative approach to improving the software continually.
Academic Citation
Researchers and practitioners using OpenFold are requested to appropriately cite their work by referring to the paper published by Gustaf Ahdritz et al. titled "OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization." The citation helps in recognizing the intellectual efforts behind the project and aids in the further dissemination of this tool among the scientific community. Additional citation information is available for those employing OpenProteinSet as part of their projects.
Lastly, users referencing OpenFold should also ensure to cite the original works on AlphaFold and AlphaFold-Multimer to acknowledge their groundbreaking contributions to the field of protein folding and structural biology.
OpenFold not only expands the possibilities for protein structure prediction but also enhances the understanding of the mechanisms behind these sophisticated models, pushing the boundaries of computational biology.