Alphafold is great, however it’s not suited for large batch predictions for 2 main reasons. Firstly, there is no native functionality for predicting structures off multiple fasta sequences (although a custom batch prediction script can be written pretty easily). Secondly, the multiple sequence alignment (MSA) step is heavy and running MSAs for, say, 10,000 sequences at a tractable speed requires some serious hardware.

Fortunately, an alternative to Alphafold has been released and is now widely used; Colabfold. For many, Colabfold’s primary strength is being cloud-based and that prediction requests can be submitted on Google Colab, thereby being extremely user-friendly by avoiding local installations. However, I would argue the greatest value Colabfold brings is a massive MSA speed up (40-60 fold) by replacing HHBlits and BLAST with MMseq2. This, and the fact batches of sequences can be natively processed facilitates a realistic option for predicting thousands of structures (this could still take days on a pair of v100s depending on sequence length etc, but its workable).

In my opinion the cleanest local installation and simplest usage of Colabfold is via Docker containers, for which both a Dockerfile and pre-built docker image have been released. Unfortunately, the Docker image does not come packaged with the necessary setup_databases.sh script, which is required to build a local sequence database. By default the MSAs are run on the Colabfold public server, which is a shared resource and can only process a total of a few thousand MSAs per day.

The following accordingly outlines preparatory steps for 100% local, batch predictions (setting up the database can in theory be done in 1 line via a mount, but I was getting a weird wget permissions error so have broken it up to first fetch the file on the local):

Pull the relevant colabfold docker image (container registry):

docker pull ghcr.io/sokrypton/colabfold:1.5.5-cuda12.2.2

Create a cache to store weights:

mkdir cache

Download the model weights:

docker run -ti --rm -v path/to/cache:/cache ghcr.io/sokrypton/colabfold:1.5.5-cuda12.2.2 python -m colabfold.download

Fetch the setup_databases.sh script

wget https://github.com/sokrypton/ColabFold/blob/main/setup_databases.sh 

Spin up a container. The container will exit as soon as the first command is run, so we need to be a bit hacky by running an infinite command in the background:

CONTAINER_ID=$(docker run -d ghcr.io/sokrypton/colabfold:1.5.5 cuda12.2.2 /bin/bash -c "tail -f /dev/null")

Copy the setup_databases.sh script to the relevant path in the container and create a databases directory:

docker cp ./setup_databases.sh $CONTAINER_ID:/usr/local/envs/colabfold/bin/ 

docker exec $CONTAINER_ID mkdir /databases

Run the setup script. This will download and prepare the databases (~2TB once extracted):

docker exec $CONTAINER_ID /usr/local/envs/colabfold/bin/setup_databases.sh /databases/ 

Copy the databases back to the host and clean up:

docker cp $CONTAINER_ID:/databases ./ 

docker stop $CONTAINER_ID
docker rm $CONTAINER_ID

You should now be at a stage where batch predictions can be run, for which I have provided a template script (uses a fasta file with multiple sequences) below. It’s worth noting that maximum search speeds can be achieved by loading the database into memory and pre-indexing, but this requires about 1TB of RAM, which I don’t have.

There are 2 key processes that I prefer to log separately, colabfold_search and colabfold_batch:

#!/bin/bash

# Define the paths for database, input FASTA, and outputs

db_path="path/to/database"
input_fasta="path/to/fasta/file.fasta"
output_path="path/to/output/directory"
log_path="path/to/logs/directory"
cache_path="path/to/weights/cache"

# Run Docker container to execute colabfold_search and colabfold_batch 

time docker run --gpus all -v "${db_path}:/database" -v "${input_fasta}:/input.fasta" -v "${output_path}:/predictions" -v "${log_path}:/logs" -v "${cache_path}:/cache"
 ghcr.io/sokrypton/colabfold:1.5.5-cuda12.2.2 /bin/bash -c "colabfold_search --mmseqs /usr/local/envs/colabfold/bin/mmseqs /input.fasta /database msas > /logs/search.log 2>&1 && colabfold_batch msas /predictions > /logs/batch.log 2>&1"