Awesome

AFsample2

Introducing a way to induce diversity in the AF2 ensemble by spanning the conformational ensemble and identifying possible states.

Introduction

AFsample2 is a generative protein structure prediction system based on AF2 that is able to induce significant conformational diversity for a given protein.

See article preprint: AFsample2: Predicting multiple conformations and ensembles with AlphaFold2

Installation

1. Install Miniconda
2. Setup environment

# Clone this repository
git clone https://github.com/iamysk/AFsample2.git
cd AFsample2/

# install dependencies
conda env create -n <env_name> --file=environment.yaml
conda activate <env_name>
python -m pip install -r requirements.txt

3. Make sure that all sequence databases are available at <data_path>. Follow the official AlphaFold guide here to set up databases.

cd scripts
chmod +x download_all_data.sh
./download_all_data.sh <data_path> reduced_dbs

4. [OPTIONAL] Install Rosetta suite for clustering tasks from here Download page. Make sure that a C++ compiler is installed.

## Optional. Ignore if compilers already installed
$ sudo apt-get install build-essential      # install C++ compilers

## Unzip tarball and compile
tar -xvzf rosetta[releasenumber].tar.gz
cd rosetta*/main/source
./scons.py -j <num_cores> mode=release bin/rosetta_scripts.mpi.linuxgccrelease       # Significiantly fast with multithreading

Refer to this guide for further details.

Usage

Step-by-step instructions to (1) generate model ensembles (2) Analyze diversity and (3) Clustering and downstream analysis

Ensemble generation

Follow the steps to generate a diverse conformational ensemble for a given <fasta_path>.

'''
Inputs: 
<method>: Method to run among afsample2, afsample, speachaf or vanilla af2
<fasta_paths>: path to .fasta file
<flagfile> : AF2 specific parameter file
<nstruct>: Number of structures to generate
<msa_rand_fraction>: % MSA randomization in random msa_perturbation_mode
<models_to_use>: (Optional) AF2 model to use (model_1, model_2 ...)

# Outputs:
# <output_dir>: Path to output directory
'''

# Example usage (AFsample2)
python AF_multitemplate/run_alphafold.py --method afsample2	\
       --fasta_paths examples/P31133/P31133.fasta \
       --flagfile AF_multitemplate/monomer_full_dbs.flag \
       --nstruct 1	\
       --msa_rand_fraction 0.20 \
       --models_to_use model_3_ptm \
       --output_dir examples/								 

Other useful flags (run <AF_multitemplate/run_alphafold.py --help> for more details)

Diversity analysis and state identification

'''
Inputs: 
<afout_path>: Path to generated models
<pdb_state1>: Reference PDB of state1
<pdb_state1>: Reference PDB of state1
<ncpu>: number of cores to use

# Outputs:
# final_df_ref1-ref2.csv file saved at results/
'''

# Example usage (If references available)
python src/analyse_models.py --afout_path examples/8E6Y/ \
	--pdb_state1 examples/8E6Y/referencea/2fs1_A.pdb \
	--pdb_state2 examples/8E6Y/referencea/8e6y_A.pdb \
	--jobid 8E6Y \
	--clustering=False
	--ncpu=16

# Example usage (If references not available)
python src/analyse_models.py --jobid 8E6Y --afout_path examples/8E6Y/ --clustering=False --ncpu=16      

Clustering and reference-free state determination

$ pip install af_sample2

How to Cite

@article {Kalakoti2024.05.28.596195,
	author = {Kalakoti, Yogesh and Wallner, Bj{\"o}rn},
	title = {AFsample2: Predicting multiple conformations and ensembles with AlphaFold2},
	elocation-id = {2024.05.28.596195},
	year = {2024},
	doi = {10.1101/2024.05.28.596195},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2024/06/02/2024.05.28.596195},
	eprint = {https://www.biorxiv.org/content/early/2024/06/02/2024.05.28.596195.full.pdf},
	journal = {bioRxiv}
}

@article{Wallner2023,
	title = {AFsample: improving multimer prediction with AlphaFold using massive sampling},
	volume = {39},
	ISSN = {1367-4811},
	url = {http://dx.doi.org/10.1093/bioinformatics/btad573},
	DOI = {10.1093/bioinformatics/btad573},
	number = {9},
	journal = {Bioinformatics},
	publisher = {Oxford University Press (OUP)},
	author = {Wallner,  Bj\"{o}rn},
	editor = {Kelso,  Janet},
	year = {2023},
	month = sep 
}

License

APACHE