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View Code? Open in Web Editor NEWGeometric deep learning method to predict protein binding interfaces from a protein structure.
Home Page: https://pesto.epfl.ch
License: Other
Geometric deep learning method to predict protein binding interfaces from a protein structure.
Home Page: https://pesto.epfl.ch
License: Other
Dear authors,
Thanks for developing this very useful tool. I am testing PeSTo, and got an error with numpy:
model/save/i_v4_1_2021-09-07_11-21/src/structure.py", line 99, in tag_hetatm_chains
structure = tag_hetatm_chains(structure)
AttributeError: module 'numpy' has no attribute 'object'
This is perhaps a numpy version issue. When I created the environment, conda automatically installed numpy 1.25. Can you please tell the numpy version that works? Or even better specify versions in pesto.yml file? Thanks.
Hi,
the following files are missed in this repo.
training_exclusion_lists = [ # "data/lists/ppdb5_set.txt", # "data/lists/masif-site_test_set.txt", # "data/lists/skempi_v2.txt", # "data/lists/memcplxdb.txt", # "data/lists/excluded.txt" ]
would you like to upload them?
Best,
Zhangzhi
I have tried to run PeSTo/md_analysis/apply_model_md.ipynb.
The folder "model" does not exist here, but I copied it from the root directory.
The from X import statements need to refer to "model."
In
from CLoNe.clone import CLoNe
"CLoNe" does not exist
The data "meta" does not exist, so the data_manager.py just throws a
FileNotFoundError: [Errno 2] No such file or directory: 'database/meta'
Also, the symlink for directory
datasets/
points to an non-existent location
Could you upload the data, for testing?
Hi!
I've seen that the prediction scores may complete change depending on the long range context... For example, the same protein domain is predicted to have different interfaces if a second (native) domain is included in the calculation, even though the 2nd domain is in the opposite face and 2 nm away from the initially predicted interface...
Similarly, I've found that mutating high scoring residues can lead to the prediction of new interfaces of equally good scores at a completely different (and distant) surface in the protein...
I wander if these issues are "artifacts" of the attention layer, which may tend to prioritize and rank different interfaces, while just pointing "the best of the best" with a given confidence... if that is the case, then the computed score may not have a coherent or "absolute" scale, it will depend on how other interactions are being evaluated in the system... Hence, there seems to be room for finding even more hidden interaction interfaces...
Best
Hello, I noticed that some module calls do not match the directory structure in the .ipynb file.
Eg.
from config import config_model, config_data
should be (according to the github structure)
from model.config import config_model, config_data
Also, the benchmark data is missing in the github. Where could I retrieve it from?
dataset = Dataset("datasets/contacts_rr5A_64nn_8192.h5")
Could you please share the details of the training process? Thank you!
Modules are not called according to the repository structure, eg:
from model import Model
should be
from model.model import Model
Also, example data missing:
dataset = Dataset("datasets/contacts_rr5A_64nn_8192.h5")
I could suggest to rerun all .ipynb files in a fresh conda enviroment with a cloned git, to ensure they work?
The results file below is missing from the repository, so the .ipynb notebook cannot be run:
results/interface_ppi_cuda_profiling.csv
Hello,
Thank you for sharing such a good job PeSTo.
I'm doing some work about protein-lipid interactions.
Is it convenient for you to tell me how can I get these protein-lipid data in PeSTo ?
Thanks !
CJ Wu
Is there a ready-made docker image ?
the conda install is Very time-consuming
Hi,
I am trying to install the environment with your .yml file it do not seems to work. Do you have an idea why ?
Dear Authors,
Thanks for developing this very useful tool. I did a test on a PDB structure. The tool generated 5 files (with names of *_i[0-4].pdb). The b factor column in each of the five files is different. I didn't find documents about the these files. Please advise on how to use the files for interface inference, such as meaning of b factor column in each file, and cutoffs (like the mean values in a window and size of window to call interface, etc. Thanks!
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