AlphaFold
Description
This package provides an implementation of the inference pipeline of AlphaFold v2.1.1 This is a completely new model that was entered in CASP14 and published in Nature. For simplicity, we refer to this model as AlphaFold throughout the rest of this document. https://github.com/deepmind/alphafold
Licence
To use Alphafold with CUDA, you will also need to accept license for cuDNN library. You will find the link on https://metavo.metacentrum.cz/cs/myaccount/licence.html.
https://github.com/deepmind/alphafold#license-and-disclaimer
Usage
Application is prepared as singularity image and downloaded datasets with example scripts to run with PBS in Metacentrum modules. Location:
- Location:
/storage/brno11-elixir/projects/alphafold
There are four models with two speed/quality tradeoff. All combinations of these parameters are prepared in example scripts in Metacentrum.
Models
You can control which AlphaFold model to run by adding the --model_preset= flag. We provide the following models:
monomer: This is the original model used at CASP14 with no ensembling.
monomer_casp14: This is the original model used at CASP14 with num_ensemble=8, matching our CASP14 configuration. This is largely provided for reproducibility as it is 8x more computationally expensive for limited accuracy gain (+0.1 average GDT gain on CASP14 domains).
monomer_ptm: This is the original CASP14 model fine tuned with the pTM head, providing a pairwise confidence measure. It is slightly less accurate than the normal monomer model.
multimer: This is the AlphaFold-Multimer model. To use this model, provide a multi-sequence FASTA file. In addition, the UniProt database should have been downloaded.
Speed/Quality
You can control MSA speed/quality tradeoff by adding --db_preset=reduced_dbs or --db_preset=full_dbs to the run command. We provide the following presets:
reduced_dbs: This preset is optimized for speed and lower hardware requirements. It runs with a reduced version of the BFD database. It requires 8 CPU cores (vCPUs), 8 GB of RAM, and 600 GB of disk space.
full_dbs: This runs with all genetic databases used at CASP14.
Tips and useful information
Example of run with different models and speed/quality tradeoff with example file seq.fasta, multimer with multi.fasta. This test shows difference of RAM consuming a length of run with the same test. Results form one run, data could be updated.
| model | speed/quality | RAM | Duration | cluster - GPU |
|---|---|---|---|---|
| monomer | full_dbs | 185 GB | 28 min | glados - RTX2080 - 8GB |
| monomer | reduced_dbs | 153 GB | 36 min | glados - RTX2080 - 8GB |
| monomer_casp14 | full_dbs | 197 GB | 35 min | zia - A100 - 40GB |
| monomer_casp14 | reduced_dbs | 38 GB | 36 min | zia - A100 - 40GB |
| monomer_ptm | full_dbs | 190 GB | 36 min | gita - RTX2080 Ti - 11GB |
| monomer_ptm | reduced_dbs | 55 GB | 32 min | gita - RTX2080 Ti - 11GB |
| multimer | full_dbs | 119 GB | 75 min | zia - A100 - 40GB |
| multimer | reduced_dbs | 40 GB | 73 min | zia - A100 - 40GB |
Documentation
URL: https://github.com/deepmind/alphafold