- My Public talk on Alphafold2 Paper Reading By Xingqiang,Chen .Key/.pptx in AF2-PPT file.
- Sergey Ovchinnikov talk on AF2 slides /.pptx in AF2-PPT file.
- DeepMind: AlphaFold-Using-AI-for-scientific-discovery
- DeepMind: alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology
- DeepMind: putting-the-power-of-alphafold-into-the-worlds-hands
- Reference papers list here and you can download them by Baidu Cloud Driver Link with the code 9w2p.
- Reference Papers' Source Codes are included in this repo.
All input data are freely available from public sources.
Structures from the PDB were used for training and as templates (https://www.wwpdb.org/ftp/pdb-ftp-sites; for the associated sequence data and 40% sequence clustering see also https://ftp.wwpdb.org/pub/pdb/derived_data/ and https://cdn.rcsb.org/resources/sequence/clusters/bc-40.out).
Training used a version of the PDB downloaded 28/08/2019, while CASP14 template search used a version downloaded 14/05/2020. Template search also used the PDB70 data- base, downloaded 13/05/2020 (https://wwwuser.gwdg.de/~compbiol/data/hhsuite/databases/hhsuite_dbs/).
We show experimental structures from the PDB with accessions 6Y4F76, 6YJ177, 6VR478, 6SK079, 6FES80, 6W6W81, 6T1Z82, and 7JTL83.
For MSA lookup at both training and prediction time,
we used UniRef90 v2020_01 (https://ftp.ebi.ac.uk/pub/databases/uniprot/previous_releases/release-2020_01/uniref/),
BFD (https://bfd.mmseqs.com), Uniclust30 v2018_08 (https://wwwuser.gwdg.de/~compbiol/uniclust/2018_08/),
and MGnify clusters v2018_12 (https://ftp.ebi.ac.uk/pub/databases/metagenomics/peptide_database/2018_12/). Uniclust30 v2018_08 was further used as input for constructing a distillation structure dataset.
for the AlphaFold model, trained weights, and an inference script is available under an open-source license at https://github.com/deepmind/alphafold.
Neural networks were developed with
- TensorFlow v1 (https://github.com/tensorflow/tensorflow),
- Sonnet v1 (https://github.com/deepmind/sonnet),
- JAX v0.1.69 (https://github.com/google/jax/),
- Haiku v0.0.4 (https://github.com/deepmind/dm-haiku).
For MSA search on
- UniRef90, MGnify clusters, and reduced BFD we used jackhmmer and for template search on the PDB SEQRES we used
- hmmsearch, both from HMMER v3.3 (http://eddylab.org/soft-ware/hmmer/).
For template search against PDB70, we used HHsearch from HH-suite v3.0-beta.3 14/07/2017 (https://github.com/soedinglab/hh-suite). For constrained relaxation of structures, we used OpenMM v7.3.1 (https://github.com/openmm/openmm) with the Amber99sb force field.
Docking analysis on DGAT used
- P2Rank v2.1 (https://github.com/rdk/p2rank),
- MGLTools v1.5.6 (https://ccsb.scripps.edu/mgltools/)
- and AutoDockVina v1.1.2 (http://vina.scripps.edu/download/) on a workstation running Debian GNU/Linux rodete 5.10.40-1rodete1-amd64 x86_64.
Data analysis used
- Python v3.6 (https://www.python.org/),
- NumPy v1.16.4 (https://github.com/numpy/numpy),
- SciPy v1.2.1 (https://www.scipy.org/),
- seaborn v0.11.1 (https://github.com/mwaskom/seaborn),
- scikit-learn v0.24.0 (https://github.com/scikit-learn/),
- Matplotlib v3.3.4 (https://github.com/matplotlib/matplotlib),
- pandas v1.1.5 (https://github.com/pandas-dev/pandas),
- and Colab (https://research.google.com/colaboratory).
- TM-align v20190822 (https://zhanglab.dcmb.med.umich.edu/TM-align) was used for computing TM-scores.
Structure analysis used Pymol v2.3.0 (https://github.com/schrodinger/pymol-open-source).