AlphaFold-Multimer Peptide-Receptor ranking

Identifying peptide-receptor interactions using AlphaFold-Multimer.

Prerequisites

• Installation of AlphaFold 2.2.0 - we used the docker-free version provided in https://github.com/kalininalab/alphafold_non_docker
• As we split MSA generation from prediction, copy af_scripts/run_alphafold_msaonly.py into the root directory of alphafold (that contains run_alphafold.py). This script only runs the data generation pipeline and omits the neural network execution.

Run AlphaFold

• Execute af_scripts/precompute_msas.py to make all MSAs. The working directory needs to be the alphafold root dir. To change the data or run parameters, modify the variables on lines 14 to 21.
• Execute af_scripts/predict_from_precomputed.py to predict all pairwise complexes. Modify the variables on lines 18 to 26 if you changed the data or msa directories. The script is meant to be executed on a GPU node and spawns multiple AlphaFold processes in parallel. Modify GPU_AVAILABLE starting from line 34 to match your GPU setup (default assumes 8 GPUs available)

Rank receptors

• The function to extract the metrics from a single alphafold result is defined in qc_metrics.py. In benchmark.ipynb, we apply this function to all results, aggregate the metrics and rank the receptors. The notebook produces the results presented in the manuscript.