problem with model loading about alphalink

[SimpleNumber]

Apparently there is a problem with my server..it just kills the process. I will try to figure out what is wrong.

Thank you for your help! The tool is really cool!:)

Cheers,
Julia

[lhatsk]

Hi Julia,

You are using the distogram flag with the contact network. In case your restraints are just contacts, you should remove the --distogram flag or if you want to use the distogram network, you need to use the other network weights: finetuning_model_5_ptm_distogram.pt

[SimpleNumber]

Thank you for a prompt response!

I've used an example that you have in read.me:

     python predict_with_crosslinks.py 7K3N_A.fasta restraints.csv --distograms --checkpoint_path resources/AlphaLink_params/finetuning_model_5_ptm_CACA_10A.pt --uniref90_database_path uniref90.fasta --mgnify_database_path mgy_clusters.fa --pdb70_database_path pdb70/pdb70 --uniclust30_database_path uniclust30_2018_08/uniclust30_2018_08

Here I have one more question: should the restrains.csv file be the one that preprocessing_distributions.py produces? If so, in this file there is no info about cross-linked amino acids anymore...

In the article, you give a very nice example with T1064, can you please share the input file for this prediction? I am basically trying to repeat your results:)

Best,
Julia

[lhatsk]

No, it doesn't have to be produced by preprocessing_distributions.py, but it has to contain a distogram. contacts_to_distograms.py would also work or you generate one manually. All the input files only include residue positions, esp. the distogram network is amino acid-agnostic. If you want to have residue-residue-specific distograms you would need to combine multiple distograms.

Note that the T1064 example was done with the contact network. You can find the features and crosslinks for some predictions on modelarchive, e.g., this one should be very close in performance: https://modelarchive.org/doi/10.5452/ma-rap-alink-1259 The pickled feature file can be used with --features

[SimpleNumber]

Sorry, I didn't get how the input file should look like. Could you pls give an example?

Note that the T1064 example was done with the contact network. You can find the features and crosslinks for some predictions on modelarchive, e.g., this one should be very close in performance: https://modelarchive.org/doi/10.5452/ma-rap-alink-1259 The pickled feature file can be used with --features

this one I also didn't get this, sorry:( There are no csv files at all. I am not so experienced in the field. Could you pls give a more detailed explanation?

Thank you in advance,
Julia

[lhatsk]

Here I have one more question: should the restrains.csv file be the one that preprocessing_distributions.py produces?

Sorry, didn't properly address this. Yes, restraints.csv would be the output of the script.

Sorry, I didn't get how the input file should look like. Could you pls give an example?

The CSV input to the distogram network looks something like this:

residueFrom residueTo 1..128
128 163 0.05 0.05 0.05 0.05 ...

Columns 2-130 contain the probability of each bin in a distogram going from 2.3125 to 42 Angstrom. The 128th bin is a catch-all bin for distances >= 42.
The first two columns are your residue positions (starting from 1).

this one I also didn't get this, sorry:( There are no csv files at all. I am not so experienced in the field. Could you pls give a more detailed explanation?

In the associated data zip-file under Downloads are two inputs, both are already preprocessed so it skips the CSV part.

T1064.pkl contains the features (including MSAs) and T1064_8_LEU_10A_CA.pt contains the crosslinks. You can use them directly to sidestep the feature generation etc. like so (untested):

python predict_with_crosslinks.py T1064.fasta T1064_8_LEU_10A_CA.pt --features T1064.pkl --checkpoint_path resources/AlphaLink_params/finetuning_model_5_ptm_CACA_10A.pt --uniref90_database_path uniref90.fasta --mgnify_database_path mgy_clusters.fa --pdb70_database_path pdb70/pdb70 --uniclust30_database_path uniclust30_2018_08/uniclust30_2018_08

[SimpleNumber]

Hi there!

I am back to try to make AlphaLink work:)
Sorry for this lengthy discussion. And thank you for your help! However, it still doesn't work.

Now, I use this histogram mode. So restraints.csv looks like a table with 130 columns:
128 163 0.05 0.05 0.05 0.05 ...
147 77 0.01 0.015 0.05 0.05 ...
147 41 0.04 0.1 0.05 0.052 ...

I use this command:

 python predict_with_crosslinks.py /users/julia/alphalink/test/test/input.fasta /users/julia/alphalink/test/test/restrains_distr.csv --distograms --checkpoint_path /users/julia/alphalink/resources/finetuning_model_5_ptm_distogram.pt --uniref90_database_path /resources/alphafold2/uniref90/uniref90.fasta --mgnify_database_path /resources/alphafold2/mgnify/mgy_clusters.fa --pdb70_database_path /resources/alphafold2/pdb70/ --uniclust30_database_path /resources/alphafold2/uniclust30/uniclust30_2018_08/

This command gives me the following traceback:

INFO:/users/julia/alphalink/AlphaLink/predict_with_crosslinks.py:Loaded OpenFold parameters at /users/julia/alphalink/resources/finetuning_model_5_ptm_distogram.pt...
INFO:/users/julia/alphalink/AlphaLink/predict_with_crosslinks.py:Using precomputed alignments for bsa1 at /users/julia/alphalink/AlphaLink/alignments...
INFO:/users/julia/alphalink/AlphaLink/predict_with_crosslinks.py:Loaded 3 restraints...
Killed

since there is no error, I am puzzled. Why is it killed? Do you have any ideas about what could go wrong?

Best regards,
Julia

[lhatsk]

Sorry, I have absolutely no idea. Never seen this before. Are you somehow resources constrained? Like running out of memory? Is this target very large? If you are able to share the data, I could run it locally to verify.

[SimpleNumber]

It is a dummy example that I am trying to make work. Under this link the files that I've used. Let me know if you can't access the folder.

Thank you for your help!
cheers,
J

[lhatsk]

Sorry, I'm out sick. Won't be able to test it before next week. But killed points towards some external event since the process seemed to be killed.

[lhatsk]

Worked fine for me. Required 18G of GPU memory.

INFO:predict_with_crosslinks.py:Loaded OpenFold parameters at resources/AlphaLink_params/finetuning_model_5_ptm_distogram.pt...
INFO:predict_with_crosslinks.py:Loaded 3 restraints...
INFO:predict_with_crosslinks.py:Running inference for bsa1...
INFO:predict_with_crosslinks.py:Inference time: 498.2697855802253
INFO:predict_with_crosslinks.py:Output written to bsa1_model_5_ptm_crosslinks_unrelaxed.pdb...
INFO:predict_with_crosslinks.py:Relaxed output written to bsa1_model_5_ptm_crosslinks_relaxed.pdb...

bsa1_model_5_ptm_crosslinks_relaxed.pdb.zip

[lhatsk]

Do you run it locally or is it submitted to some compute nodes with, e.g., SLURM?