AFProfile

Improved protein complex prediction with AlphaFold-multimer by denoising the MSA profile.

AFProfile learns a bias to the MSA representation that improves the predictions by performing gradient descent through the AlphaFold-multimer network.
We effectively denoise the MSA profile, similar to how a blurry image would be sharpened to become more clear.
This proves to be a highly efficient process, resulting in a 60-fold speedup compared to AFsample and as efficient as AFM v2.3.
Read more about it in the paper here.

AlphaFold2 (including AlphaFold-multimer) is available under the Apache License, Version 2.0 and so is AFProfile, which is a derivative thereof.
The AlphaFold2 parameters are made available under the terms of the CC BY 4.0 license and have not been modified.
You may not use these files except in compliance with the licenses.

Optimisation for 6nnw

• Here is an example trajectory for PDBID 6nnw sorted by confidence.

• The final prediction has an MMscore of 0.96 compared to 0.44 using AF-multimer. The native structure is in grey.

The confidence used to denoise the MSA is defined as:
Confidence = 0.8 iptm + 0.2 ptm
Where iptm is the predicted TM-score in the interface and ptm that of the entire complex.

Setup

Clone this repository

git clone https://github.com/patrickbryant1/AFProfile.git

Get the AlphaFold-multimer parameters

cd AFProfile
mkdir data/params
wget https://storage.googleapis.com/alphafold/alphafold_params_2022-03-02.tar
tar -xvf alphafold_params_2022-03-02.tar
mv params_model_1_multimer_v2.npz data/params/
rm *.npz
rm alphafold_params_2022-03-02.tar

Install the AlphaFold requirements

Install all packages into a conda environment (requires https://docs.conda.io/en/latest/miniconda.html)

conda env create -f afprofile.yml
wait
conda activate afprofile
pip install --upgrade "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
pip install --upgrade numpy

If the conda doesn't work for you - see "pip_pkgs.txt"

Try the test case

Now when you have installed the required packages - you can run a test case on CASP15 target T1123o

cd src
bash AFP.sh

Install the genetic search programs

• We install the genetic search programs from source. This will make the searches faster.

hh-suite

cd src
mkdir hh-suite
cd hh-suite
wget https://github.com/soedinglab/hh-suite/releases/download/v3.3.0/hhsuite-3.3.0-SSE2-Linux.tar.gz
tar xvfz hhsuite-3.3.0-SSE2-Linux.tar.gz
cd ..

hmmer

cd src
wget http://eddylab.org/software/hmmer/hmmer.tar.gz
tar -xvzf hmmer.tar.gz
rm hmmer.tar.gz
cd hmmer-*
./configure
make
cd ..

kalign

wget https://github.com/TimoLassmann/kalign/archive/refs/tags/v3.3.2.tar.gz
tar -zxvf v3.3.2.tar.gz
rm v3.3.2.tar.gz
cd kalign-3.3.2/
./autogen.sh
bash configure
make
make check
make install
cd ..

Download all databases for AlphaFold

• If you have already installed AlphaFold, you don't need to do this. Then you can simply provide the paths for the databases in the runscript.

Small BFD: 17 GB

wget https://storage.googleapis.com/alphafold-databases/reduced_dbs/bfd-first_non_consensus_sequences.fasta.gz
gunzip bfd-first_non_consensus_sequences.fasta.gz
mkdir data/small_bfd
mv bfd-first_non_consensus_sequences.fasta data/small_bfd
rm bfd-first_non_consensus_sequences.fasta.gz

UNIREF90: 67 GB

wget https://ftp.ebi.ac.uk/pub/databases/uniprot/uniref/uniref90/uniref90.fasta.gz
gunzip uniref90.fasta.gz
mkdir data/uniref90
mv uniref90.fasta data/uniref90/
rm uniref90.fasta.gz

UNIPROT: 105 GB

wget https://ftp.ebi.ac.uk/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_trembl.fasta.gz
wget https://ftp.ebi.ac.uk/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.fasta.gz
gunzip uniprot_trembl.fasta.gz
gunzip uniprot_sprot.fasta.gz
mkdir data/uniprot
cat uniprot_sprot.fasta >> uniprot_trembl.fasta
mv uniprot_trembl.fasta data/uniprot/uniprot.fasta
rm *.gz
rm uniprot_sprot.fasta

• The following template databases are not used for the predictions, but needed due to the feature processing.

PDB SEQRES: 0.2 GB

wget https://files.rcsb.org/pub/pdb/derived_data/pdb_seqres.txt.gz
gunzip pdb_seqres.txt.gz
mkdir pdb_seqres
mv pdb_seqres.txt  pdb_seqres/

MGNIFY: 120 GB

wget https://storage.googleapis.com/alphafold-databases/v2.3/mgy_clusters_2022_05.fa.gz
gunzip mgy_clusters_2022_05.fa.gz
mkdir mgnify
mv mgy_clusters_2022_05.fa mgnify/
rm mgy_clusters_2022_05.fa.gz

MMCIF: 238 GB

• This may take a while...

mkdir -p data/pdb_mmcif/raw
mkdir data/pdb_mmcif/mmcif_files
rsync --recursive --links --perms --times --compress --info=progress2 --delete --port=33444 rsync.rcsb.org::ftp_data/structures/divided/mmCIF/ data/pdb_mmcif/raw

find data/pdb_mmcif/raw -type f -iname "*.gz" -exec gunzip
find data/pdb_mmcif/raw -type d -empty -delete  
for subdir in data/pdb_mmcif/raw/*
do
  mv "${subdir}/"*.cif data/pdb_mmcif/mmcif_files/
done
find data/pdb_mmcif/raw -type d -empty -delete

Citation

Bryant P, Noé F. Improved protein complex prediction with AlphaFold-multimer by denoising the MSA profile. PLoS Comput Biol. 2024;20: e1012253.