Work overview

Section 15 of 17

Code Availability

Highly Accurate Protein Structure Prediction with AlphaFold

John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · 2021

Contents

Section 15 of 17

  1. 01Abstract
  2. 02Main Results
  3. 03AlphaFold Network
  4. 04Evoformer
  5. 05End-to-End Structure Prediction
  6. 06Training with Labelled and Unlabelled Data
  7. 07Interpreting the Network
  8. 08MSA Depth
  9. 09Related Work
  10. 10Discussion
  11. 11Methods
  12. 12Acknowledgements
  13. 13Author Contributions
  14. 14Data Availability
  15. 15Code Availability
  16. 16Competing Interests
  17. 17References
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Work overview

Section 15 of 17

Code Availability

John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · about 1 minutes

Original source ↗

Code availability

Source code for the AlphaFold model, trained weights and inference script are available under an open-source license at https://github.com/deepmind/alphafold.

Neural networks were developed with TensorFlow v.1 (https://github.com/tensorflow/tensorflow), Sonnet v.1 (https://github.com/deepmind/sonnet), JAX v.0.1.69 (https://github.com/google/jax/) and Haiku v.0.0.4 (https://github.com/deepmind/dm-haiku). The XLA compiler is bundled with JAX and does not have a separate version number.

For MSA search on BFD+Uniclust30, and for template search against PDB70, we used HHBlits and HHSearch from hh-suite v.3.0-beta.3 release 14/07/2017 (https://github.com/soedinglab/hh-suite). For MSA search on UniRef90 and clustered MGnify, we used jackhmmer from HMMER v.3.3 (http://eddylab.org/software/hmmer/). For constrained relaxation of structures, we used OpenMM v.7.3.1 (https://github.com/openmm/openmm) with the Amber99sb force field.

Construction of BFD used MMseqs2 v.925AF (https://github.com/soedinglab/MMseqs2) and FAMSA v.1.2.5 (https://github.com/refresh-bio/FAMSA).

Data analysis used Python v.3.6 (https://www.python.org/), NumPy v.1.16.4 (https://github.com/numpy/numpy), SciPy v.1.2.1 (https://www.scipy.org/), seaborn v.0.11.1 (https://github.com/mwaskom/seaborn), Matplotlib v.3.3.4 (https://github.com/matplotlib/matplotlib), bokeh v.1.4.0 (https://github.com/bokeh/bokeh), pandas v.1.1.5 (https://github.com/pandas-dev/pandas), plotnine v.0.8.0 (https://github.com/has2k1/plotnine), statsmodels v.0.12.2 (https://github.com/statsmodels/statsmodels) and Colab (https://research.google.com/colaboratory). TM-align v.20190822 (https://zhanglab.dcmb.med.umich.edu/TM-align/) was used for computing TM-scores. Structure visualizations were created in Pymol v.2.3.0 (https://github.com/schrodinger/pymol-open-source).