alphafold_MSA

This implementation of the Alphafold 2 algorithm was made for my Bachelor thesis and it is made to be able to run the Alphafold 2 monomer model direcly with MSAs as input and to get the predicted distances between pairs (disotgram) as an output.

Installation guide

This installation guide was extracted from https://github.com/kalininalab/alphafold_non_docker, and modified to adapt it to out purpose.

First Miniconda should be installed as new en- vironment to run the program has to be created (recommended).

conda create −−name alphafold python==3.8

Then the environment has to be activated:

conda activate alphafold

Now the dependencies will be installed:

conda install −y −c conda−forge openmm==7.5.1 cudnn==8.2.1.32 cudatoolkit==11.0.3 pdbfixer==1.7

Now the following packages have to be installed:

pip install absl−py==0.13.0 biopython==1.79 chex==0.0.7 dm−haiku ==0.0.4 dm−tree ==0.1.6 immutabledict ==2.0.0 jax ==0.2.14 ml−collections ==0.1.0 numpy==1.19.5 scipy ==1.7.0 tensorflow ==2.5.0 pandas==1.3.4 tensorflow−cpu==2.5.0

pip install −−upgrade jax==0.2.14 jaxlib==0.1.69+cuda111 −f https://storage.googleapis.com/jax−releases/jax cuda releases.html

pip install matplotlib

Finally the parameters of the models have to be downloaded in the model paramet folder (have in mind that the size of the parameters is ∼ 3.5 GB):

wget −P /model paramet https://storage.googleapis.com / alphafold / alphafold params 2021 −07−14. tar

And then the .tar file has to be decompressed. The final model_paramet folder should contain the models in .npz format.

Execution guide

In order to execute the program on the bash Terminal there have been established 2 variables:

• aln msa path: This variable refers to the folder where the MSAs are located. (Have in mind that the app will predict iteratively all the MSA’s given in the folder path.)
• output dir:This variable refers to the direction where the output of the app will be saved. To run the app this command has to be run:

alphafold run .py −−aln_msa_path path −−output_dir path

Outputs

The program returns a folder in the --output_path with the name of the MSA that contains two files:

• protein.pdb (3d structure of the predicted protien)
• disotgram.pkl, a file that contains the distogram of distances between pairs of amino acids.