design_path -> path where to save designs and statistics binder_name -> what to prefix your designed binder files with starting_pdb -> the path to the PDB of your target protein chains -> which chains to target in your protein, rest will be ignored target_hotspot_residues -> which position to target for binder design, for example `1,2-10` or chain specific `A1-10,B1-20` or entire chains `A`, set to null if you want AF2 to select binding site; better to select multiple target residues or a small patch to reduce search space for binder lengths -> range of binder lengths to design number_of_final_designs -> how many designs that pass all filters to aim for, script will stop if this many are reached 

conda activate BindCraft cd /path/to/bindcraft/folder/ python -u ./bindcraft.py --settings './settings_target/PDL1.json' --filters './settings_filters/default_filters.json' --advanced './settings_advanced/default_4stage_multimer.json' 

omit_AAs -> which amino acids to exclude from design (note: they can still occur if no other options are possible in the position) force_reject_AA -> whether to force reject design if it contains any amino acids specified in omit_AAs design_algorithm -> which design algorithm for the trajecory to use, the currently implemented algorithms are below use_multimer_design -> whether to use AF2-ptm or AF2-multimer for binder design; the other model will be used for validation then num_recycles_design -> how many recycles of AF2 for design num_recycles_validation -> how many recycles of AF2 use for structure prediction and validation sample_models = True -> whether to randomly sample parameters from AF2 models, recommended to avoid overfitting rm_template_seq_design -> remove target template sequence for design (increases target flexibility) rm_template_seq_predict -> remove target template sequence for reprediction (increases target flexibility) rm_template_sc_design -> remove sidechains from target template for design rm_template_sc_predict -> remove sidechains from target template for reprediction predict_initial_guess -> Introduce bias by providing binder atom positions as a starting point for prediction. Recommended if designs fail after MPNN optimization. predict_bigbang -> Introduce atom position bias into the structure module for atom initilisation. Recommended if target and design are large (more than 600 amino acids). # Design iterations soft_iterations -> number of soft iterations (all amino acids considered at all positions) temporary_iterations -> number of temporary iterations (softmax, most probable amino acids considered at all positions) hard_iterations -> number of hard iterations (one hot encoding, single amino acids considered at all positions) greedy_iterations -> number of iterations to sample random mutations from PSSM that reduce loss greedy_percentage -> What percentage of protein length to mutate during each greedy iteration # Design weights, higher value puts more weight on optimising the parameter. weights_plddt -> Design weight - pLDDT of designed chain weights_pae_intra -> Design weight - PAE within designed chain weights_pae_inter -> Design weight - PAE between chains weights_con_intra -> Design weight - maximise number of contacts within designed chain weights_con_inter -> Design weight - maximise number of contacts between chains intra_contact_distance -> Cbeta-Cbeta cutoff distance for contacts within the binder inter_contact_distance -> Cbeta-Cbeta cutoff distance for contacts between binder and target intra_contact_number -> how many contacts each contact esidue should make within a chain, excluding immediate neighbours inter_contact_number -> how many contacts each contact residue should make between chains weights_helicity -> Design weight - helix propensity of the design, Default 0, negative values bias towards beta sheets random_helicity -> whether to randomly sample helicity weights for trajectories, from -1 to 1 # Additional losses use_i_ptm_loss -> Use i_ptm loss to optimise for interface pTM score? weights_iptm -> Design weight - i_ptm between chains use_rg_loss -> use radius of gyration loss? weights_rg -> Design weight - radius of gyration weight for binder use_termini_distance_loss -> Try to minimise distance between N- and C-terminus of binder? Helpful for grafting weights_termini_loss -> Design weight - N- and C-terminus distance minimisation weight of binder # MPNN settings mpnn_fix_interface -> whether to fix the interface designed in the starting trajectory num_seqs -> number of MPNN generated sequences to sample and predict per binder max_mpnn_sequences -> how many maximum MPNN sequences per trajectory to save if several pass filters sampling_temp = 0.1 -> sampling temperature for amino acids, T=0.0 means taking argmax, T>>1.0 means sampling randomly.") # MPNN settings - advanced backbone_noise -> backbone noise during sampling, 0.00-0.02 are good values model_path -> path to the MPNN model weights mpnn_weights -> whether to use "original" mpnn weights or "soluble" weights save_mpnn_fasta -> whether to save MPNN sequences as fasta files, normally not needed as the sequence is also in the CSV file # AF2 design settings - advanced num_recycles_design -> how many recycles of AF2 for design num_recycles_validation -> how many recycles of AF2 use for structure prediction and validation optimise_beta -> optimise predictions if beta sheeted trajectory detected? optimise_beta_extra_soft -> how many extra soft iterations to add if beta sheets detected optimise_beta_extra_temp -> how many extra temporary iterations to add if beta sheets detected optimise_beta_recycles_design -> how many recycles to do during design if beta sheets detected optimise_beta_recycles_valid -> how many recycles to do during reprediction if beta sheets detected # Optimise script remove_unrelaxed_trajectory -> remove the PDB files of unrelaxed designed trajectories, relaxed PDBs are retained remove_unrelaxed_complex -> remove the PDB files of unrelaxed predicted MPNN-optimised complexes, relaxed PDBs are retained remove_binder_monomer -> remove the PDB files of predicted binder monomers after scoring to save space zip_animations -> at the end, zip Animations trajectory folder to save space zip_plots -> at the end, zip Plots trajectory folder to save space save_trajectory_pickle -> save pickle file of the generated trajectory, careful, takes up a lot of storage space! max_trajectories -> how many maximum trajectories to generate, for benchmarking acceptance_rate -> what fraction of trajectories should yield designs passing the filters, if the proportion of successful designs is less than this fraction then the script will stop and you should adjust your design weights start_monitoring -> after what number of trajectories should we start monitoring acceptance_rate, do not set too low, could terminate prematurely # debug settings enable_mpnn = True -> whether to enable MPNN design enable_rejection_check -> enable rejection rate check 

MPNN_score -> MPNN sequence score, generally not recommended as it depends on protein MPNN_seq_recovery -> MPNN sequence recovery of original trajectory pLDDT -> pLDDT confidence score of AF2 complex prediction, normalised to 0-1 pTM -> pTM confidence score of AF2 complex prediction, normalised to 0-1 i_pTM -> interface pTM confidence score of AF2 complex prediction, normalised to 0-1 pAE -> predicted alignment error of AF2 complex prediction, normalised compared AF2 by n/31 to 0-1 i_pAE -> predicted interface alignment error of AF2 complex prediction, normalised compared AF2 by n/31 to 0-1 i_pLDDT -> interface pLDDT confidence score of AF2 complex prediction, normalised to 0-1 ss_pLDDT -> secondary structure pLDDT confidence score of AF2 complex prediction, normalised to 0-1 Unrelaxed_Clashes -> number of interface clashes before relaxation Relaxed_Clashes -> number of interface clashes after relaxation Binder_Energy_Score -> Rosetta energy score for binder alone Surface_Hydrophobicity -> surface hydrophobicity fraction for binder ShapeComplementarity -> interface shape complementarity PackStat -> interface packstat rosetta score dG -> interface rosetta dG energy dSASA -> interface delta SASA (size) dG/dSASA -> interface energy divided by interface size Interface_SASA_% -> Fraction of binder surface covered by the interface Interface_Hydrophobicity -> Interface hydrophobicity fraction of binder interface n_InterfaceResidues -> number of interface residues n_InterfaceHbonds -> number of hydrogen bonds at the interface InterfaceHbondsPercentage -> number of hydrogen bonds compared to interface size n_InterfaceUnsatHbonds -> number of unsatisfied buried hydrogen bonds at the interface InterfaceUnsatHbondsPercentage -> number of unsatisfied buried hydrogen bonds compared to interface size Interface_Helix% -> proportion of alfa helices at the interface Interface_BetaSheet% -> proportion of beta sheets at the interface Interface_Loop% -> proportion of loops at the interface Binder_Helix% -> proportion of alfa helices in the binder structure Binder_BetaSheet% -> proportion of beta sheets in the binder structure Binder_Loop% -> proportion of loops in the binder structure InterfaceAAs -> number of amino acids of each type at the interface HotspotRMSD -> unaligned RMSD of binder compared to original trajectory, in other words how far is binder in the repredicted complex from the original binding site Target_RMSD -> RMSD of target predicted in context of the designed binder compared to input PDB Binder_pLDDT -> pLDDT confidence score of binder predicted alone Binder_pTM -> pTM confidence score of binder predicted alone Binder_pAE -> predicted alignment error of binder predicted alone Binder_RMSD -> RMSD of binder predicted alone compared to original trajectory