Accelerating Biomolecular Modeling with AtomWorks and RF3

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Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

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RF3: A New Open-Source Contender in the Protein Structure Prediction Arena

This paper introduces RosettaFold-3 (RF3), a new open-source deep learning model for predicting biomolecular structures, and AtomWorks, a framework for developing such models. RF3 shows improved handling of chirality and user-defined constraints, narrowing the gap between open-source and closed-source models like AlphaFold3. RF3 and AtomWorks together facilitate the creation and training of biomolecular modeling tools, emphasizing data quality and reproducibility.

Possible Conflicts of Interest

Some authors are affiliated with the Institute for Protein Design and the Howard Hughes Medical Institute. The authors also acknowledge support from several funding sources, including the Gates Foundation and NIH, although this support doesn't automatically constitute a COI. Additionally, some authors have affiliations with commercial entities such as NVIDIA and Microsoft, who provided substantial computational resources (including Azure compute credits and AI for Good lab support).

Identified Weaknesses

Lack of access to datasets and code for all benchmarked models

RF3 performance is measured against other structure prediction networks using a variety of metrics and datasets. Without access to the full details of these datasets and evaluation methods, it's difficult to fully assess the significance of the improvements or identify potential bias.

Potential training dataset biases

RF3 relies on training datasets derived from the PDB and other distillation sources. Errors and biases within these training datasets could propagate to RF3’s predictions, particularly for the ‘disordered distillation set’ constructed using Rosetta, potentially overfitting to Rosetta’s energy function. The potential bias from AF2-generated data is not discussed or assessed.

Limited exploration of RF3’s effectiveness on real-world use cases

The paper includes the results for a new version of RF3 trained on data deposited through January 2024. However, the impact of this training on real-world use cases isn't fully explored. The improvement in overall median metrics is relatively small, especially compared to AF3, so broader utility is not guaranteed.

Rating Explanation

The development of RF3 and AtomWorks represents a substantial contribution to open-source biomolecular modeling. The focus on data quality, modularity, and reproducibility is commendable, and the improved performance, especially in handling chirality and user-defined constraints, is promising. However, transparency about benchmark details, further investigation of training dataset biases, and more extensive real-world applications are crucial for wider community adoption and building trust in the method. The affiliations with commercial entities warrant disclosure as potential conflicts of interest, though their direct impact on the research direction isn't apparent.

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