RL-MLZerD: Multimeric protein docking using reinforcement learning

This is the dataset used in the publication: RL-MLZerD: Multimeric protein docking using reinforcement learning by Tunde Aderinwale, Charles Christoffer and Daisuke Kihara, in submission 2022.

The dataset included 30 protein complexes targets from 3 to 5 chains used for docking. Each target in the dataset are randomly rotated and shifted three times to remove bias to the starting conformation.

We also include the docking results for each target in the corresponding directory for all the methods reported in the manuscript.

For each target, 1500 to 12,000 models were provided that were  generated by RL-MLZerD, 1700 to 12,000 models for Multi-LZerD, about 100 to 300 models by Combdock, and 5 models each by Alphafold-Multimer, Alphafold-Multimer (no template), and ColabFold respectively.

Directory description:

Target: contains all the files related to all experiment for that target e.g 1A0R
Target/*_mod_run_? contain files for all random rotation experiments and combdock results. e.g 1A0R/1A0R_mod_run_1/
Target/*_mod_combine_athird contains files for combined rotation experiment for RL-MLZerD and Multilzerd. e.g 1A0R/1A0R_mod_combine_athird/
Target/alphafold_multimer contains prediction files for AlphaFold-Multimer, both regular and no template run. e.g alphafold_multimer/1a0r and alphafold_multimer/1a0r_nodb
Target/colabfold contains prediction files for ColabFold results. e.g colabfold/prediction_1a0r