Alphafold¶
Introduction¶
Alphafold
is a protein structure prediction tool developed by DeepMind (Google). It uses a novel machine learning approach to predict 3D protein structures from primary sequences alone. The source code is available on `Github`_. It has been deployed in all RCAC clusters, supporting both CPU and GPU.
It also relies on a huge database. The full database (~2.2TB) has been downloaded and setup for users.
Protein struction prediction by alphafold is performed in the following steps:
Search the amino acid sequence in uniref90 database by jackhmmer (using CPU)
Search the amino acid sequence in mgnify database by jackhmmer (using CPU)
Search the amino acid sequence in pdb70 database (for monomers) or pdb_seqres database (for multimers) by hhsearch (using CPU)
Search the amino acid sequence in bfd database and uniclust30 (updated to uniref30 since v2.3.0) database by hhblits (using CPU)
Search structure templates in pdb_mmcif database (using CPU)
Search the amino acid sequence in uniprot database (for multimers) by jackhmmer (using CPU)
Predict 3D structure by machine learning (using CPU or GPU)
Structure optimisation with OpenMM (using CPU or GPU)