usage.md

Usage

Run features

Run on CPUs to get features:

./run_alphafold.sh \
-d data \
-o output \
-p monomer_ptm \
-i input/GA98.fasta \
-t 1800-01-01 \
-m model_1 \
-f

-f means only run the featurization step, result in a feature.pkl file, and skip the following steps.

8 CPUs is enough, according to my test, more CPUs won't help with speed

Featuring step will output the feature.pkl and MSA folder in your output folder: ./output/[FASTA_NAME]/

PS: Here we put input files in an input folder to organize files in a better way.

Run monomer prediction

After the feature step, you can run run_alphafold.sh using GPU:

./run_alphafold.sh \
-d data \
-o output \
-m model_1,model_2,model_3,model_4,model_5 \
-p monomer_ptm \
-i input/GA98.fasta \
-t 1800-01-01 

If you have successfully output feature.pkl, you can have a very fast featuring step

Run multimer prediction

./run_alphafold.sh \
-d data \
-o output \
-m model_1_multimer,model_2_multimer,model_3_multimer,model_4_multimer,model_5_multimer \
-p multimer \
-i input/GA98.fasta \
-t 1800-01-01 

Draw figures

[This function is under repair]

You can run run_figure.py to visualize your result: [This will be available soon]

python3 run_figure.py [SystemName]

This python file will create a figure folder in your output folder.

Notice: run_figure.py need a local conda environment with matplotlib, pymol and numpy.