wengong-jin / abdockgen Goto Github PK
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License: MIT License
train_data.jsonl.gz
file$git clone https://github.com/wengong-jin/abdockgen.git
$cd abdockgen
$gunzip data/rabd/train_data.jsonl.gz
I use mamba forge to python package manager, therefore
$mamba create -n abdockgen
$mamba activate adbdockgen
$(abdockgen)mamba install pytorch torchvision torchaudio pytorch-cuda=11.8 biopython matplotlib tqdm pdbfixer biotite -c pytorch -c nvidia
$(abdockgen)git clone https://github.com/bjornwallner/DockQ/
$(abdockgen)cd DockQ
$(abdockgen)make
$(abdockgen)cd ..
$(abdockgen)python dock_train.py --hierarchical --L_target 20 --save_dir ckpts/HERN-dock
outputs
Epoch 19, Ligand RMSD = 24.054, All atom RMSD = 11.635
100%|███████████████████████████████████████████| 58/58 [00:01<00:00, 30.10it/s]
Test Ligand RMSD = 23.981, All atom RMSD = 11.677
$(abdockgen)mkdir outputs
$(abdockgen)python predict.py ckpts/HERN_dock.ckpt data/rabd/test_data.jsonl
It produced PDB files like this.
outputs/
├── 1a14_pred.pdb
├── 1a2y_pred.pdb
├── 1fe8_pred.pdb
├── 1ic7_pred.pdb
├── 1iqd_pred.pdb
├── 1n8z_pred.pdb
├── 1ncb_pred.pdb
├── 1osp_pred.pdb
├── 1uj3_pred.pdb
├── 2adf_pred.pdb
├── 2b2x_pred.pdb
├── 2cmr_pred.pdb
├── 2dd8_pred.pdb
├── 2ghw_pred.pdb
├── 2vxt_pred.pdb
├── 2xqy_pred.pdb
├── 2xwt_pred.pdb
├── 2ypv_pred.pdb
├── 3bn9_pred.pdb
├── 3cx5_pred.pdb
├── 3ffd_pred.pdb
├── 3hi6_pred.pdb
├── 3k2u_pred.pdb
├── 3l95_pred.pdb
├── 3mxw_pred.pdb
├── 3nid_pred.pdb
├── 3o2d_pred.pdb
├── 3rkd_pred.pdb
├── 3s35_pred.pdb
├── 3uzq_pred.pdb
├── 3w9e_pred.pdb
├── 4cmh_pred.pdb
├── 4dtg_pred.pdb
├── 4dvr_pred.pdb
├── 4etq_pred.pdb
├── 4ffv_pred.pdb
├── 4fqj_pred.pdb
├── 4g6j_pred.pdb
├── 4g6m_pred.pdb
├── 4h8w_pred.pdb
├── 4ki5_pred.pdb
├── 4lvn_pred.pdb
├── 4ot1_pred.pdb
├── 4qci_pred.pdb
├── 4xnq_pred.pdb
├── 4ydk_pred.pdb
├── 5b8c_pred.pdb
├── 5bv7_pred.pdb
├── 5d93_pred.pdb
├── 5d96_pred.pdb
├── 5en2_pred.pdb
├── 5f9o_pred.pdb
├── 5ggs_pred.pdb
├── 5hi4_pred.pdb
├── 5j13_pred.pdb
├── 5l6y_pred.pdb
├── 5mes_pred.pdb
└── 5nuz_pred.pdb
0 directories, 58 files
In DockQ repo, there is a short instruction like this.
./DockQ.py <model> <native>
but I don't have a native pdb file, right?
Hi im trying to run inference using my own data/pdbs.
When trying to make pdb's into jsons by doing for example python process_data.py 6bdz H A
(6bdz is found in rabd/val_data.jsonl)
I get this error:
Traceback (most recent call last):
File "process_data.py", line 28, in <module>
hcoords = hcoords.reshape((len(hseq), 14, 3))
ValueError: cannot reshape array of size 9855 into shape (219,14,3)
Why is it trying to reshape hcoords
which is of shape (3285, 3)
into (219,14,3)
? I see the 14
is hardcoded. What does it mean? (I understand 219
is the length of hseq
)
I'd like to run docking on my own PDB files. What I don't understand is how to include the pdb
of the antigen.
python process_data.py ${pdb_file} ${heavy_chain_id} ${antigen_chain_id}
Is the ${pdb_file}
meant to be for the antibody, the antigen, or both?
How could I do it with one file called antigen.pdb
and another called antibody.pdb
?
It seems like process_data.py doesn't work with sidechainnet=1.0.1.
I didn't test any other versions but 0.7.1 works fine.
Hi, thanks for your work!
Could you please tell me how to generate 3D structure of predicted CDR-H3 after finishing "python generate.py ckpts/HERN_gen.ckpt data/rabd/test_data.jsonl 1 > results/HERN.txt". I'd appreciate it if you could answer!
Hi
Thanks for uploading the code, this looks really interesting!
I have had some memory issues running your example, maybe you can help me?
it comes when I run the model.generate(epitope, surface)
function
out = model.generate(epitope, surface)
RuntimeError: CUDA out of memory. Tried to allocate 120.00 MiB (GPU 0; 4.00 GiB total capacity; 3.09 GiB already allocated; 0 bytes free; 3.34 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
I've put my system details below
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.81 Driver Version: 513.29 CUDA Version: 11.6 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA T500 On | 00000000:01:00.0 Off | N/A |
| N/A 43C P8 N/A / N/A | 3848MiB / 4096MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 2175 C /python3.9 N/A |
+-----------------------------------------------------------------------------+
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Hi,
The file rabd_test.txt
contains 60 pdb entries and that count is also reported in the paper. However, the corresponding file test_data.jsonl
for RAbD contains 58 entries only. After looking through the pdb entries, I found that pdb entries '1w72' and '3h3b' are missing. Can you kindly provide the data for these two pdb entires as well.
In the file
generate.py.
topk = sys.argv[3]
will get a string type value. But in line for cdr,ppl in new_res[:topk]:
, the 'topk' should be a int type.
So it should add an transform line to change the value type. This is my way:
topk = sys.argv[3]
if not isinstance(topk, int):
topk = int(topk)
Hi wengong-jin
Thanks for providing the code to this awesome paper!
I had a question about the side chain coordinates. In the paper you say 'the docking module only predicts the four backbone atoms (N, Cα, C, O) for the paratope,' but in some of your diagrams you show the docked HCDR3 structures with their sidechains.
How do you add the side chain coordinate predictions to your final structure?
Hi, I have a complex that I made by re-folding the 6bdz sequence (which is part of your test set). The antigen-antibody pose is badly docked and I thought it could be fixed with this abdockgen repo.
I processed the pdb file into a json by doing python process_data.py ./af2_6bdz.pdb B C
(and saved the processed file to ./processed/af2_6bdz.json
)
And then did inference by doing python predict.py ckpts/HERN_dock.ckpt ./processed/af2_6bdz.json
and the output pdb file is stored in /outputs/
.
The problem is that the output file only contains the antigen and it is positioned exactly identically to how it was in the input file. So basically abdockgen's predict did absolutely nothing. If you overlay the output file onto the original, the antigen takes the exact same position.
Am I doing something wrong?
Hello,
The generation example produce error as;
python generate.py ckpts/HERN_gen.ckpt data/rabd/test_data.jsonl 1 > results/HERN.txt
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 58/58 [00:01<00:00, 49.16it/s]
0%| | 0/58 [00:24<?, ?it/s]
Traceback (most recent call last):
File "/home/serbulent_antiverse_io/code/abdockgen/generate.py", line 60, in
for cdr,ppl in new_res[:topk]:
TypeError: slice indices must be integers or None or have an index method
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Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.