This repo contains code for AttnPacker
Pre-trained models, and PDB files used to generate all results are available at https://zenodo.org/record/7713779#.ZApZHezMIVU
UPDATE (04/19/2023): AttnPacker+Design now supports conditioning on partial sequence and rotamers. Pre trained models are available at https://zenodo.org/record/7843977#.ZEAWqezML0o. The main inference notebook has been updated to reflect this option.
$ git clone git@github.com:MattMcPartlon/AttnPacker.git
$ conda create -n attnpacker python=3.8
$ conda activate attnpacker
$ pip install -r ./AttnPacker/requirements.txtNote: The default pytorch installation may not include GPU support. Since this is often system-specific it is left to the user to change this.
Inference with AttnPacker is outlined in protein_learning/examples/inference.ipynb. This includes examples for sequence design, side-chain post processing
per-residue confidence prediction and more. A notbook with examples specific to sampling is available at protein_learning/examples/sampling.ipynb. Additional examples are outlined below.
usage: post_process.py [-h] [--pdb_path_out PDB_PATH_OUT] [--steric_wt STERIC_WT] [--steric_tol_allowance STERIC_TOL_ALLOWANCE]
[--steric_tol_frac STERIC_TOL_FRAC] [--steric_hbond_allowance STERIC_HBOND_ALLOWANCE]
[--max_optim_iters MAX_OPTIM_ITERS] [--torsion_loss_wt TORSION_LOSS_WT] [--device DEVICE]
pdb_path_in
Project Protein Sidechains onto Continuous Rotamer and Minimize Steric Clashes
positional arguments:
pdb_path_in path to input pdb
optional arguments:
-h, --help show this help message and exit
--pdb_path_out PDB_PATH_OUT
path to save projected pdb to (defaults to post-processed-<input pdb name>.pdb (default: None)
--steric_wt STERIC_WT
weight to use for steric clash loss (default: 0.2)
--steric_tol_allowance STERIC_TOL_ALLOWANCE
subtract this number from all atom vdW radii (default: 0.05)
--steric_tol_frac STERIC_TOL_FRAC
set vdW radii to steric_tol_frac*vdW(atom_type) (default: 0.9)
--steric_hbond_allowance STERIC_HBOND_ALLOWANCE
subtract this number from the sum of vdW radii for hydrogen bond donor/acceptor pairs (default: 0.6)
--max_optim_iters MAX_OPTIM_ITERS
maximum number of iterations to run optimization procedure for (default: 250)
--torsion_loss_wt TORSION_LOSS_WT
penalize average deviaiton from initial dihedral angles with this weight (default: 0)
--device DEVICE device to use when running this procedure (default: cpu)
(py38)[mmcpartlon@raptorx11 AttnPacker]$ python protein_learning/examples/post_process.py ./protein_learning/examples/pdbs/T1057-predicted.pdb --steric_tol_allowance 0 --steric_tol_frac 0.95 --max_optim_iters 200 --device cuda:0
[fn: project_onto_rotamers] : Using device cuda:0
[INFO] Beginning rotamer projection
[INFO] Initial loss values
[RMSD loss] = 0.103
[Steric loss] = 0.035
[Angle Dev. loss] = 0.0
beginning iter: 0, steric weight: 0.2
[INFO] Final Loss Values
[RMSD loss] = 0.064
[Steric loss] = 0.002
[Angle Dev. loss] = 0.001
Saving to: ./protein_learning/examples/pdbs/post-processed-T1057-predicted.pdb
Finished in 3.32 secondsfrom protein_learning.assessment.sidechain import assess_sidechains, summarize
import pprint
predicted_pdb = "./pdbs/post-processed-T1080-predicted.pdb"
target_pdb = "./pdbs/T1080.pdb"
res_level_stats = assess_sidechains(target_pdb, predicted_pdb, steric_tol_fracs = [1,0.9,0.8])
target_level_stats = summarize(assessment_stats)
print(pprint.pformat(target_level_stats))Output:
{'ca_rmsd': tensor( 0.000),
'clash_info': {'100': {'energy': tensor(2.010),
'num_atom_pairs': 308580,
'num_clashes': 16},
'80': {'energy': tensor(0.),
'num_atom_pairs': 308580,
'num_clashes': 0},
'90': {'energy': tensor(0.),
'num_atom_pairs': 308580,
'num_clashes': 0}},
'dihedral_counts': tensor([98, 57, 12, 7]),
'mae_sr': tensor(0.520),
'mean_mae': tensor([28.504, 22.006, 73.557, 45.663]),
'num_sc': 98,
'rmsd': tensor(0.743),
'seq_len': 133}In the example above, assessment_stats contains residue level information regarding dihedral MAE, RMSD, clashing atom pairs, etc. The summarize function produces target-level statistics by averaging over all residues with at least two side-chain atoms. For this target, a total of 138 residues were analyzed, and 98 had at least two side chain atoms (i.e. were not Glycine or Alanine).
The common folder contains protein specific constants such as residue names, atom types, side-chain dihedral information, etc. It also contains functionality for working with sequence and pdb files, data-loading, and model configuration settings.
This sub-folder contains base classes for protein model input and output (model_data.py)
as well as protein datasets. The dataset subfolder contains PyTorch datasets for training a protein-learning model
which are compatible with PyTorch's DataLoader.
Implementation of
This folder contains loss functions for residue, pair, and coordinate features
This folder contains functions for computing input features.
The InputEmbedding class (features/input_embedding.py) can be used to generate and embed all input features.
The FeatureGenerator class (features/generator.py) is passed to a ProteinDataset instance to generate input
features during training. Options for which input features to include can be found in features/feature_config.py. You
can also subclass FeatureGenerator and/or InputEmbedding to obtain any additional functionality.
Code used to compare predicted side-chain packing with ground truth can be found here (sidechains.py).
This will generate per-residue statistics such as: