Protein Models: Autoimmunity-associated DIORA1 binds the MRCK family of serine/threonine kinases and controls cell motility

Models generated for the interaction between DIORA1 and MRCKB with massive sampling using AFsample (Wallner, 2022) File descriptions The data is compressed with zstandard to extract an archive use the following command: tar --use-compress-program=unzstd -xvf all_models_scores.tar.zst all_models_scores.tar.zst - Contains scores for all generated models, extracts to the folder all_models/selected_models.tar.zst - Contains top-ranked selected models that are also highlighted in the paper, extracts to selected_models/ Models for different interaction partners All models and corresponding results *.pkl (distogram removed to save space) are archived in different files for each interaction partner. Stoichiometry 1:1 (main text) KIM_diora_1_1.tar.zst (2.2GB) - All 3000 models generated for the KIM region of MRCKB to interact with DIORA1.MRCKB_1020_1582-diora1_1_214.tar.zst (3.9GB) - All 3,000 models generated for the MRCKB range 1020-1582 corresponding to the C1-PH-CNH domains. Different models with different stoichiometries (Supplementary Information) KIM_diora1_1_2.tar.zst (1.9GB) - 3,000 models for KIM:DIORA1 1:2KIM_diora1_2_1.tar.zst (1.4GB) - 3,000 models for KIM:DIORA1 2:1KIM_diora1_2_2.tar.zst (2.6GB) - 3,000 models for KIM:DIORA1 2:2 MRCKB_1020_1582-diora1_1_214_1_2.tar.zst (5.3GB) - 3,000 models for C1-PH-CNH:DIORA1 1:2MRCKB_1020_1582-diora1_1_214_2_1.tar.zst (8.8GB) - 3,000 models for C1-PH-CNH:DIORA1 2:1MRCKB_1020_1582-diora1_1_214_2_2.tar.zst (10.8GB) - 3,000 models for C1-PH-CNH:DIORA1 2:2 Modelling protocol Models generated for the interaction between DIORA1 and MRCKB with massive sampling using AFsample (Wallner, 2022) Code: http://wallnerlab.org/AFsample Alphafold was run with dropout activated at inference using all 15 (3x5) neural network models available for multimeric prediction: model_[1-5]_multimer model_[1-5]_multimer_v2 model_[1-5]_multimer_v3 The following command was used to generate 200 models per neural network model, 3000 (15x200) models in total for each modeling input python alphafoldv2.3.1/run_alphafold.py--dropout--nstruct 200--use_precomputed_msas --data_dir=/proj/beyondfold/apps/alphafold_data --template_mmcif_dir=/proj/beyondfold/apps/alphafold_data/pdb_mmcif/mmcif_files --obsolete_pdbs_path=/proj/beyondfold/apps/alphafold_data/pdb_mmcif/obsolete.dat --mgnify_database_path=/proj/beyondfold/users/x_bjowa/databases/mgnify/mgy_all.fa --uniref90_database_path=/proj/beyondfold/apps/alphafold_data/uniref90/uniref90.fasta --max_template_date=3000-01-01 --use_gpu_relax=True --model_preset=multimer --db_preset=full_dbs --uniref30_database_path=/proj/beyondfold/apps/alphafold_data/uniclust30/UniRef30_2021_03/UniRef30_2021_03 --bfd_database_path=/proj/beyondfold/apps/alphafold_data/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt --pdb_seqres_database_path=/proj/beyondfold/apps/alphafold_data/pdb_seqres/pdb_seqres.txt --uniprot_database_path=/proj/beyondfold/apps/alphafold_data/uniprot/uniprot.fasta