vsomnath / holoprot Goto Github PK

Hello! I've learned a lot from your model and it has inspired some ideas for me. I'm trying to replicate your code to obtain global embeddings for surface graph summarization and then conduct testing. In the code snippet I've added (highlighted in red), the second line for global pooling seems to be causing an error. It appears that there might be a mismatch in dimensions between 'x' and 'batch'. I'm unsure whether I've correctly defined 'batch' in that line. Could you please take a look and provide some guidance? Thank you! My English skills might not be perfect, so I apologize if my explanation isn't very clear.

Hello,

Thanks for the work and clean repo. I wanted to use your processed data for the EC task, with my own models. I untared raw and processed surface2backbone. However, there seems to be a missing class in the data/base.py. Indeed, when I try to load the data using the following code :

if __name__ == '__main__':
    raw_dir = os.path.abspath(f"../../data/enzyme/")
    processed_dir = os.path.abspath(f"../../data/")
    train_dataset = EnzymeDataset(mode='train', raw_dir=raw_dir,
                                  processed_dir=processed_dir,
                                  add_targets=True,
                                  prot_mode='surface2backbone')
    item = train_dataset[0]

I get the following error :

Traceback (most recent call last):
  File "/home/vmallet/anaconda3/envs/atom2d/lib/python3.8/site-packages/torch/serialization.py", line 789, in load
    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
  File "/home/vmallet/anaconda3/envs/atom2d/lib/python3.8/site-packages/torch/serialization.py", line 1131, in _load
    result = unpickler.load()
  File "/home/vmallet/anaconda3/envs/atom2d/lib/python3.8/site-packages/torch/serialization.py", line 1124, in find_class
    return super().find_class(mod_name, name)
AttributeError: Can't get attribute 'BackboneData' on <module 'holoprot.data.base' from '/home/vmallet/projects/atom2d/atom2d/holoprot/data/base.py'>

Do you think this class definition could be missing ? Also, am I using the right loading ? I am trying to retrieve the meshes, residue graphs and node/vertices features that you used for training.

Thanks a ton in advance for your help !

Best,
Vincent

I install Blender with "pip install bpy==2.91a0 && bpy_post_install",
but i cant find "BLENDER_BIN=/path/to/blender/blender",
I want to konw how to install Blender correctly.

I tested pulling out one of the patches after running ers.computeSegmentation and then pulled out a submesh from for instance:

submesh = pymesh.submesh(mesh, np.where(patch_labels==2)[0], 0)

And I expected the the triangles to all be collected in a patch, however it looks like they are scattered all about from the pymol ply plugin from MaSIF. Is this intended? I cannot get much from the paper on what the indended behavior is.

4d7b: Could not compute charges because of Charges cannot be computed. Missing file. 4d7b_out.csv. Trying with fixed file.
4d7b: Could not compute charges for fixed file because of Charges cannot be computed. Missing file. 4d7b_out.csv. Returning None

APBS 1.5 cant work. I have APBS 3.4.1 but some file cant compute charge.(apbs cant get xxx.out.csv)

Hello guys, I really enjoyed the paper and the approach of combining different levels of protein representation. I was wondering if I could try a similar approach with my use case. I do not work on the surface level, but I more interested in combining the residue level of my graph (let's call this residue graph Gr) and the more fine-grained atom level (Ga). I can relate each residue node of Gr to many atom nodes from Ga. My questions to you would be:

1. Would that approach be doable in my case (from your intuition)?
2. If yes, I am mostly interested in the part of your code that takes care of the update and messaging between the 2 levels (i.e. the computation of hidden features hr for graph Gr, the creation of the new mapping x for graph Ga, then the computation of embeddings ha, and finally the aggregation of original graph embeddings c). Can you guys point me to the methods/code of interest in the repository?

Thanks a lot! Great work!

Hello authors,

First off, really well written paper & great code base. Enjoyed reading it and managed to understand the key concepts on just the first pass.

As you have rightly pointed out in your paper, dMaSIF is a concurrent work that greatly optimises MaSIF, especially by removing the need for pre-computed features, which is not trivial to set up + slows down inference.

Would you happen to have any intention to integrate dMaSIF into your work?

Or phrased another way, what are the steps needed to accomplish it? I am willing to contribute.

Keep up the great research! (I also really enjoyed GraphRetro, having worked under Connor myself!)

Best,
Min Htoo

I want to implement the comparison algorithm Masif as described in the article. I downloaded the code of Masif, but it was written in tensorflow1, while this article uses pytorch. I want to know how to use masif to replace the protein representation in this article. Can you share the code? Thank you very much!