- The paper introduces HelixFold3, a model that replicates AlphaFold3’s performance in predicting ligand, nucleic acid, and protein structures using extensive PDB and self-distillation datasets.
- The paper demonstrates that HelixFold3 achieves over 90% success in ligand predictions on PoseBusters benchmarks and competitive scores on RNA and DNA structure evaluations.
- The paper validates HelixFold3’s reliability through strong correlations between confidence metrics (pLDDT, pAE, pTM) and actual structural accuracy, highlighting its practical potential.
Overview of HelixFold3: A Biomolecular Structure Prediction Model
The paper "Technical Report of HelixFold3 for Biomolecular Structure Prediction" discusses the development and capabilities of HelixFold3, a model designed to replicate the performance of AlphaFold3 in predicting the structures of ligands, nucleic acids, and proteins. This research, conducted by the PaddleHelix Team at Baidu Inc., represents a noteworthy attempt to make advanced biomolecular structure prediction accessible to a broader academic audience through open-source development.
Introduction
The AlphaFold series, particularly AlphaFold2, AlphaFold-Multimer, and AlphaFold3, has set a new standard in protein structure prediction, achieving near-experimental accuracy in many cases. However, despite the success and accessibility of AlphaFold2 and AlphaFold-Multimer, AlphaFold3 remains partially accessible, with limited development opportunities due to its closed-source status. The PaddleHelix team aims to mitigate these limitations by developing HelixFold3 based on the insights and datasets leveraged in the AlphaFold series.
Methods and Data
HelixFold3 builds on prior work, including HelixFold, HelixFold-Single, HelixFold-Multimer, and HelixDock. The model was trained using data from the Protein Data Bank (PDB) released before September 30, 2021, and additional self-distillation datasets. HelixFold3's training methodology and model architecture enable it to achieve competitive accuracy in predicting structures for various biomolecular targets.
Results
Ligands
HelixFold3's performance in predicting ligand structures was evaluated using the PoseBusters benchmark. The results indicate that HelixFold3 achieves a high success rate comparable to AlphaFold3, outperforming many baseline methods that rely on predefined protein structures. Specifically, the success rate on PoseBusters V1 and V2 datasets shows that HelixFold3's predictions are both precise and physically plausible, with a quality check pass rate exceeding 90% for most metrics.
Nucleic Acids
The structure prediction of nucleic acids represents a significant challenge due to the limited crystallized structures available. HelixFold3 was tested on RNA targets from the CASP15 benchmark and recent RNA and DNA structures from the PDB. The model demonstrated competitive performance, with accuracy levels comparable to AlphaFold3 in fully automated evaluations. Notably, HelixFold3 outperformed specialized models like RoseTTAFold2NA in predicting RNA and DNA structures.
Proteins
For protein-protein complex structure prediction, HelixFold3 was evaluated against AlphaFold-Multimer and AlphaFold3 using protein complexes released in the PDB. HelixFold3 outperformed AlphaFold-Multimer in interface prediction accuracy, although there remains a gap when compared to AlphaFold3. The team recognizes this and is committed to ongoing improvements in model accuracy and reliability.
Model Confidence
HelixFold3 employs several confidence metrics (pLDDT, pAE, and pTM) to evaluate the quality of its predictions. The analysis indicates a strong correlation between these confidence scores and actual structural accuracy, validating the reliability of these metrics across different datasets, including ligands, protein-protein interfaces, RNA, and DNA.
Conclusion and Future Work
In summary, the development of HelixFold3 represents a significant contribution to the field of biomolecular structure prediction, offering a model that closely rivals the performance of AlphaFold3. The initial open-source release on GitHub ensures that researchers can access and build upon HelixFold3's capabilities. Future work will focus on expanding and refining the model's accuracy across diverse and larger datasets, with a continuous effort to bridge the remaining performance gap with AlphaFold3.
Acknowledgement
The authors acknowledge the support of computing resources from the National SuperComputing Center and Tecorigin, underlining the critical role these resources played in the development of HelixFold3.
For further information regarding HelixFold3 or potential collaborations, researchers can contact the PaddleHelix team at the provided email addresses.