A reference of 'AlphaFold2 Codec' include everything of AlphaFold2.

Learning Source Availability

Papers

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

PPT

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.

Learning by Code

Practice on Modeling Test of AF2

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

Blogs

References

reference papers

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.

Data availability

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.

Code and programmings availability

Source code

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

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).

MSA search

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

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

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

Structure analysis used Pymol v2.3.0 (https://github.com/schrodinger/pymol-open-source).

lupengk/Ref-AlphaFold2-Codec