BindCraft
v1.2.0
使用 AlphaFold2 反向传播、MPNN 和 PyRosetta 的简单活页夹设计流程。选择您的目标，让脚本完成其余的工作，并在您有足够的设计可供订购后完成！
BindCraft 的预印本链接
首先，您需要克隆此存储库。将[install_folder]替换为您要安装的路径。
git clone https://github.com/martinpacesa/BindCraft [install_folder]
使用cd导航到安装文件夹并运行安装代码。 BindCraft 需要兼容 CUDA 的 Nvidia 显卡才能运行。在cuda设置中，请指定与您的显卡兼容的CUDA版本，例如“11.8”。如果不确定，请留空，但安装可能会选择错误的版本，从而导致错误。在pkg_manager中指定您使用的是“mamba”还是“conda”，如果留空它将默认使用“conda”。
注意：此安装脚本将安装 PyRosetta，它需要商业用途的许可证。
bash install_bindcraft.sh --cuda '12.4' --pkg_manager 'conda'
始终尝试将输入目标 PDB 修剪到尽可能小的大小！它将显着加快绑定器生成速度并最大限度地减少 GPU 内存需求。
准备好运行至少几百个轨迹来查看一些可接受的绑定器，对于困难的目标，甚至可能是几千个。
要在本地运行脚本，首先需要在settings_target文件夹中配置目标 .json 文件。 json 文件中有以下设置：
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
然后运行活页夹设计脚本：
sbatch ./bindcraft.slurm --settings './settings_target/PDL1.json' --filters './settings_filters/default_filters.json' --advanced './settings_advanced/default_4stage_multimer.json'
设置标志应指向您在上面设置的目标 .json。过滤器标志指向指定设计过滤器的 json（默认为 ./filters/default_filters.json）。高级标志指向您的高级设置（默认为 ./advanced_settings/default_4stage_multimer.json）。如果您省略过滤器和高级设置标志，它将自动指向默认值。
或者，如果您的机器不支持SLURM，您可以通过激活conda中的环境并运行python代码来直接运行代码：
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'
我们建议生成至少 100 个通过所有过滤器的最终设计，然后订购前 5-20 个进行实验表征。如果需要高亲和力结合物，最好进行更多筛选，因为用于排名的 ipTM 指标不是亲和力的良好预测因子，但已被证明是结合的良好二元预测因子。
以下是各个过滤器和高级设置的说明。
以下是控制设计过程的高级设置：
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
# 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
max_tm-score_filter -> filter out final lower ranking designs by this TM score cut off relative to all passing designs
max_seq-similarity_filter -> filter out final lower ranking designs by this sequence similarity cut off relative to all passing designs
sampling_temp = 0.1 -> sampling temperature for amino acids, T=0.0 means taking argmax, T>>1.0 means sampling randomly.")
# MPNN settings - advanced
sample_seq_parallel -> how many sequences to sample in parallel, reduce if running out of memory
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
以下是您的设计将被过滤的功能，如果您不想使用某些功能，只需将阈值设置为null即可。较高选项指示是否应保留高于阈值的值（true）或低于阈值（false）。以 N_ 开头的特征对应于每个 AlphaFold 模型的统计数据，平均值是所有预测模型的平均值。
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
感谢 Lennart Nickel、Yehlin Cho、Casper Goverde 和 Sergey Ovchinnikov 在编码和讨论想法方面提供的帮助。该存储库使用以下代码：
