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Showing content from https://github.com/chenxingqiang/alphafold2-docs below:

chenxingqiang/alphafold2-docs: A C, C++, Python project focusing on Docking analysis, Source code, Blogs, Data availability, References.

• Jumper, J., Evans, R., Pritzel, A. et al. Highly accurate protein structure prediction with AlphaFold. Nature (2021). https://doi.org/10.1038/s41586-021-03819-2

• 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.

• https://github.com/sokrypton/ColabFold.git

• https://github.com/pablo-arantes/Making-it-rain

• 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).

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