Quantifying the Role of OpenFold Components in Protein Structure Prediction
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Abstract (arXiv)
Models such as AlphaFold2 and OpenFold have transformed protein structure prediction, yet their inner workings remain poorly understood. We present a methodology to systematically evaluate the contribution of individual OpenFold components to structure prediction accuracy. We identify several components that are critical for most proteins, while others vary in importance across proteins. We further show that the contribution of several components is correlated with protein length. These findings provide insight into how OpenFold achieves accurate predictions and highlight directions for interpreting protein prediction networks more broadly.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 26 references that are in the map. First posted on arXiv 6 Nov 2025.
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References · 8
- 2023The geometry of hidden representations of large transformer modelscited by 112
- 2020BERTology Meets Biology: Interpreting Attention in Protein Language Modelscited by 85
- 2024What Matters in Transformers? Not All Attention is Neededcited by 84
- 2024InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoderscited by 66
- 2024MLP Can Be A Good Transformer Learnercited by 17
- 2025Interpreting and Steering Protein Language Models through Sparse Autoencoderscited by 17
- 2023ExplainableFold: Understanding AlphaFold Prediction with Explainable AIcited by 4
- 2025Advances in Protein Representation Learning: Methods, Applications, and Future Directionscited by 3
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