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kevinmicha / ANTIPASTI

ANTIPASTI (ANTIbody Predictor of Affinity from STructural Information) is a Deep Learning model that predicts the binding affinity of antibodies from their three-dimensional structure.
https://kevinmicha.github.io/ANTIPASTI/
MIT License
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ag_residues always 0 #5

Open igortru opened 1 day ago

igortru commented 1 day ago

Hi, I am talking only about provided notebooks. I don't see how you include antigen structure in affinity calculation process: ag_residues always 0.

ag_residues=0, self.ag_residues = ag_residues antigen_max_pixels = self.ag_residues idx_list += [i+max_res_h+max_res_l for i in range(min(antigen_max_pixels, img.shape[-1]-(h+l)))]

technically, my interest - check affinity for complex I folded myself, it is not clear how it can be done. for example : how create npy file which contain chothia numbering for antibody (anarci?) and antigen at the same time.

kevinmicha commented 1 day ago

Hi igortu,

ag_residues is a legacy variable, so it is not a bug that it is always zero.

ANTIPASTI expects Chothia numbering for the antibody regions; you might find this function helpful. Although I have not tested it myself, it appears straightforward and promising. It is part of the IgFold repository and AbNumber-based.

Alternatively, if you already have a PDB file with any numbering scheme and a list of residues using Chothia numbering, you can save them in the appropriate folders under notebook/test_data/ (i.e., structure/ and list_of_residues/), and then adapt the second cell of the affinity prediction notebook.

Hope this helps!

igortru commented 1 day ago

Am I understand correctly that masked dccm matrix (input_shape) has always constant size and somehow include information about antigen? How exactly you create list_of_residues files from pdbs? They have different from anarci format.