AlphaFold¶

Colabfold¶

Prerequisites

Have followed the container introductions.

Colabfold is an implementation of alphafold that offers more adjustable parameters and in depth MSA control via MMseqs2.

To run colabfold let's first create a colabfold.def singularity container definition file and insert the following:

colabfold.def

Bootstrap: docker
From: nvcr.io/nvidia/jax:24.04-py3

%post
    python3 -m pip install tensorflow jax[cuda12]
    python3 -m pip install --no-warn-conflicts 'colabfold[alphafold] @ git+https://github.com/sokrypton/ColabFold'

%environment
    export PATH=${HOME}/.local/bin:$PATH
    unset XLA_FLAGS

%runscript
    conda deactivate >/dev/null 2>&1 || true
    exec /bin/bash -i

Click here to download the file: colabfold.def

Then build and run the container:

$ singularity pull jax2404py3.sif docker://nvcr.io/nvidia/jax:24.04-py3
$ singularity build --fakeroot colabfold.sif colabfold.def
$ srun -N 1 --gpus 4 --pty singularity run --nv colabfold.sif

Finally test colabfold_batch functionality inside the container on a small P54025.fasta file:

$ Singularity> wget https://github.com/sokrypton/ColabFold/raw/main/test-data/P54025.fasta
$ Singularity> colabfold_batch P54025.fasta output/ # Test colabfold

MMseqs2¶

Prerequisites

Have followed the instructions to install Conda in your user storage space

If you would like to speed up the run time and run MMseqs2 locally on Isambard instead of using the MMseqs2 remote server please follow the installation instructions below. This creates a conda environment named mmseq. You only have to clone the GitHub to get access to the example DB and QUERY for testing.

git clone https://github.com/soedinglab/MMseqs2
cd MMseqs2/
conda create -n mmseq python=3.10
conda activate mmseq
conda install -c conda-forge -c bioconda mmseqs2
mmseqs easy-cluster examples/DB.fasta clusterRes tmp --min-seq-id 0.5 -c 0.8 --cov-mode 1