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OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization

Nature Methods (2024)Cite this article

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Abstract

AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (1) tackle new tasks, like protein–ligand complex structure prediction, (2) investigate the process by which the model learns and (3) assess the model’s capacity to generalize to unseen regions of fold space. Here we report OpenFold, a fast, memory efficient and trainable implementation of AlphaFold2. We train OpenFold from scratch, matching the accuracy of AlphaFold2. Having established parity, we find that OpenFold is remarkably robust at generalizing even when the size and diversity of its training set is deliberately limited, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced during training, we also gain insights into the hierarchical manner in which OpenFold learns to fold. In sum, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial resource for the protein modeling community.

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Fig. 1: OpenFold matches the accuracy of AlphaFold2.
Fig. 2: OpenFold generalization capacity on elided training sets.
Fig. 3: Model improvements.
Fig. 4: Secondary structure categories are learned in succession.
Fig. 5: Learning proceeds at multiple scales.

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Data availability

OpenProteinSet and OpenFold model parameters are hosted on the Registry of Open Data on AWS and can be accessed at https://registry.opendata.aws/openfold/. Both are available under the permissive CC BY 4.0 license. Throughout the study, we use validation sets derived from the PDB via CAMEO. We also use CASP evaluation sets. Source data are provided with this paper.

Code availability

OpenFold can be accessed at https://github.com/aqlaboratory/openfold. It is available under the permissive Apache 2 Licence.

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Acknowledgements

We thank the Flatiron Institute, OpenBioML, Stability AI, the Texas Advanced Computing Center and NVIDIA for providing compute for experiments in this paper. Individually, we thank M. Mirdita, M. Steinegger and S. Ovchinnikov for valuable support and expertise. This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the US Department of Energy under contract no. DE-AC02-05CH11231. We acknowledge the Texas Advanced Computing Center at the University of Texas at Austin for providing HPC resources that have contributed to the research results reported within this paper. G.A. is supported by a Simons Investigator Fellowship, NSF grant DMS-2134157, DARPA grant W911NF2010021, DOE grant DE-SC0022199 and a graduate fellowship from the Kempner Institute at Harvard University. N.B. is supported by DARPA Panacea program grant HR0011-19-2-0022 and NCI grant U54-CA225088. C.F. and S.K. are supported by NIH grant R35GM150546. B.Z. and Z.Z. are supported by grants NSF OAC-2112606 and OAC-2106661. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Gustaf Ahdritz, Nazim Bouatta.

Authors and Affiliations

  1. Department of Systems Biology, Columbia University, New York, NY, USA

    Gustaf Ahdritz, Christina Floristean, Sachin Kadyan, Qinghui Xia & Mohammed AlQuraishi

  2. Harvard University, Cambridge, MA, USA

    Gustaf Ahdritz

  3. Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA

    Nazim Bouatta, William Gerecke & Peter K. Sorger

  4. Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Timothy J. O’Donnell

  5. Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA

    Daniel Berenberg

  6. Flatiron Institute, New York, NY, USA

    Ian Fisk

  7. OpenBioML, Cambridge, MA, USA

    Niccolò Zanichelli

  8. Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA

    Bo Zhang

  9. NVIDIA, Santa Clara, CA, USA

    Arkadiusz Nowaczynski, Bei Wang, Marta M. Stepniewska-Dziubinska, Shang Zhang, Adegoke Ojewole, Murat Efe Guney & Pablo Ribalta Lorenzo

  10. EleutherAI, New York, NY, USA

    Stella Biderman

  11. Booz Allen Hamilton, McLean, VA, USA

    Stella Biderman

  12. Prescient Design, Genentech, New York, NY, USA

    Andrew M. Watkins, Stephen Ra & Richard Bonneau

  13. Cyrus Bio, Seattle, WA, USA

    Lucas Nivon

  14. Outpace Bio, Seattle, WA, USA

    Brian Weitzner

  15. Arzeda, Seattle, WA, USA

    Yih-En Andrew Ban

  16. Rutgers University, New Brunswick, NJ, USA

    Shiyang Chen & Zhao Zhang

  17. University of Illinois at Urbana-Champaign, Champaign, IL, USA

    Minjia Zhang

  18. Microsoft, Redmond, WA, USA

    Conglong Li, Shuaiwen Leon Song & Yuxiong He

  19. Stability AI, Los Altos, CA, USA

    Emad Mostaque

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  1. Gustaf Ahdritz
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  2. Nazim Bouatta
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Contributions

G.A. wrote and optimized the OpenFold codebase, generated data, trained the model, performed experiments and maintained the GitHub repository. C.F. wrote and tested code for the OpenFold implementation of AlphaFold-Multimer. S.K. and W.G. wrote data preprocessing code. G.A., N.B. and M.A. conceived of and managed the project, designed experiments, analyzed results and wrote the manuscript. G.A., B.Z., Z.Z., N.Z. and A.N. ran ablations. All authors read and approved the manuscript. The Flatiron Institute (via I.F., A.M.W., S.R. and R.B.) provided compute for ablations, all data generation and our main training experiments. NVIDIA (A.N., B. Wang, M.M.S.-D., S.Z., A.O., M.E.G. and P.R.L.) performed training stability experiments, fixed critical bugs in the codebase, added new model features and provided compute for ablations. Stability AI (via N.Z., S.B. and E.M.) provided compute for ablations. The DeepSpeed team at Microsoft (S.C., M.Z., C.L., S.L.S. and Y.H.) wrote custom optimized attention kernels. Q.X. and T.J.O.’D. debugged code and provided feedback.

Corresponding authors

Correspondence to Nazim Bouatta or Mohammed AlQuraishi.

Ethics declarations

Competing interests

M.A. is a member of the scientific advisory boards of Cyrus Biotechnology, Deep Forest Sciences, Nabla Bio, Oracle Therapeutics and FL2021-002, a Foresite Labs company. P.K.S. is a cofounder and member of the BOD of Glencoe Software, member of the BOD for Applied BioMath and a member of the SAB for RareCyte, NanoString, Reverb Therapeutics and Montai Health; he holds equity in Glencoe, Applied BioMath and RareCyte. L.N. is an employee of Cyrus Biotechnology. The other authors declare no competing interests.

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Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Arunima Singh, in collaboration with the Nature Methods team.

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Extended data

Extended Data Fig. 1 OpenFold matches the accuracy of AlphaFold2 on CASP15 targets.

Scatter plot of GDT-TS values of AlphaFold and OpenFold ‘Model 1’ predictions against all currently available ‘all groups’ CASP15 domains (n = 90). OpenFold’s mean accuracy (95% confidence interval = 68.6-78.8) is on par with AlphaFold’s (95% confidence interval = 69.7-79.2) and OpenFold does at least as well as the latter on exactly 50% of targets. Confidence intervals of each mean are estimated from 10,000 bootstrap samples.

Extended Data Fig. 2 OpenFold learns decoy ranking slowly.

Decoy ranking results (mean Spearman correlation between pLDDT and decoy TM Score) using intermediate checkpoints of OpenFold on 28 randomly chosen proteins from the Rosetta decoy ranking dataset from15. See Supplementary Information section B.1 for more details.

Extended Data Fig. 3 Fine-tuning does not materially improve prediction accuracy on long proteins.

Mean lDDT-Cα over validation proteins with at least 500 residues as a function of fine-tuning step.

Extended Data Fig. 4 The ‘Mostly alpha’ CATH class contains some beta sheets, and vice versa.

Counts for alpha helices and beta sheets in the mostly alpha and mostly beta CATH class-stratified training sets from Fig. 2, based on 1,000 random samples. Counts are binned by size, defined as the number of residues for alpha helices and number of strands for beta sheets.

Extended Data Fig. 5 Reduced dataset diversity disproportionately affects global structure.

Mean GDT-TS and lDDT-Cα of non-overlapping protein fragments from CAMEO validation set as a function of the percentage of CATH clusters in elided training sets. Data for both topology and architecture elisions are included. The fragmenting procedure is the same as that described in Fig. 5a.

Extended Data Fig. 6 Early predictions crudely approximate lower-dimensional PCA projections.

(A) Mean dRMSD, as a function of training step, between low- dimensional PCA projections of predicted structures and the final 3D prediction at step 5,000 (denoted by *). Averages are computed over the CAMEO validation set. Insets show idealized behavior corresponding to unstaggered, simultaneous growth in all dimensions and perfectly staggered growth. Empirical training behavior more closely resembles the staggered scenario. (B) Low-dimensional projections as in (A) compared to projections of the final predicted structures at step 5,000. (C) Mean displacement, as a function of training step, of C? atoms along the directions of their final structure’s PCA eigenvectors. Results are shown for two individual proteins (PDB accession codes 7DQ9_A ref. 66 and 7RDT_A ref. 67). Shaded regions correspond loosely to ‘1D,’ ‘2D,’ and ‘3D’ phases of dimensionality.

Extended Data Fig. 7 Radius of gyration as an order parameter for learning protein phase structure.

Radii of gyration for proteins in the CAMEO validation set (or- ange) as a function of sequence length over training time, plotted on a log-log scale against experimental structures (blue). Legends show equations of best fit curves, computed using non-linear least squares. The training steps chosen correspond loosely to four phases of dimensional growth. See Supplementary Information section B.3 for extended discussion.

Extended Data Fig. 8 Contact prediction for beta sheets at different ranges.

Binned contact F1 scores (8 Å threshold) for beta sheets of various widths as a function of training step at different residue-residue separation ranges (SMLR ≥ 6 residues apart; LR ≥ 24 residues apart, as in8). Sheet widths are weighted averages of sheet thread counts within each bin, as in Fig. 5b.

Extended Data Table 1 Secondary structure recovery by class-stratified models
Full size table

Supplementary information

Supplementary Information

Supplementary Discussion

Reporting Summary

Supplementary Video 1

Folding animation for PDB protein 7B3A, chain A. Predictions are from successive early checkpoints of an OpenFold model (training step is shown at the bottom left).

Supplementary Video 2

Folding animation for PDB protein 7DMF, chain A. Predictions are from successive early checkpoints of an OpenFold model (training step is shown at the bottom left).

Supplementary Video 3

Folding animation for PDB protein 7DQ9, chain A. Predictions are from successive early checkpoints of an OpenFold model (training step is shown at the bottom left).

Supplementary Video 4

Folding animation for PDB protein 7LBU, chain A. Predictions are from successive early checkpoints of an OpenFold model (training step is shown at the bottom left).

Supplementary Video 5

Folding animation for PDB protein 7RDT, chain A. Predictions are from successive early checkpoints of an OpenFold model (training step is shown at the bottom left).

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Source Data Fig. 5

Numerical source data.

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Ahdritz, G., Bouatta, N., Floristean, C. et al. OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization. Nat Methods (2024). https://doi.org/10.1038/s41592-024-02272-z

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