testing DeepRank-Mut

[imerelli]

Hi, I'm trying to use DeepRank-Mut. The first problem is that I don't get how to run the tests, because in the documentation it is stated to enter in the test directory and run pytest, but this command is not valid.

However, from the root directory I can tun the test scripts that are in the test directory. Here is the output. While test/test_tools.py and test/test_atomic_features.py provide an output, test/test_generate.py and test/test_learn.py do not provide any output. Is this excepted? There is something that I can do differently?

( deeprank ) $ python test/test_atomic_features.py 
test/test_atomic_features.py:4: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
  import pkg_resources
/opt/tools/deg/miniforge3/envs/deeprank/lib/python3.8/site-packages/pdb2sql/pdb2sqlcore.py:263: UserWarning: Missing chainID and set it with segID
  warnings.warn("Missing chainID and set it with segID")
AtomicFeature coulomb and vdw exported to file ./atomic_pair_interaction.dat
.
----------------------------------------------------------------------
Ran 1 test in 1.645s

OK

( deeprank ) $ python test/test_generate.py 

( deeprank ) $ python test/test_learn.py 

( deeprank ) $ python test/test_tools.py 
/opt/tools/deg/DeepRank-mut/deeprank/tools/sasa.py:109: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  assert len(resA[:, 0].astype(np.int).tolist()) == len(
/opt/tools/deg/DeepRank-mut/deeprank/tools/sasa.py:110: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  np.unique(resA[:, 0].astype(np.int)).tolist())
/opt/tools/deg/DeepRank-mut/deeprank/tools/sasa.py:111: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  assert len(resB[:, 0].astype(np.int).tolist()) == len(
/opt/tools/deg/DeepRank-mut/deeprank/tools/sasa.py:112: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  np.unique(resB[:, 0].astype(np.int)).tolist())
/opt/tools/deg/DeepRank-mut/deeprank/tools/sasa.py:115: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  self.xyz[chain1] = resA[:, 2:].astype(np.float)
/opt/tools/deg/DeepRank-mut/deeprank/tools/sasa.py:116: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  self.xyz[chain2] = resB[:, 2:].astype(np.float)
/opt/tools/deg/DeepRank-mut/deeprank/tools/sasa.py:61: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  resSeqA = np.unique(resA[:, 0].astype(np.int))
/opt/tools/deg/DeepRank-mut/deeprank/tools/sasa.py:62: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  resSeqB = np.unique(resB[:, 0].astype(np.int))
.
----------------------------------------------------------------------
Ran 1 test in 0.412s

OK

[cbaakman]

Hello and thanks for showing interest in Deeprank-Mut.
It's true that the tests should be run from the root and not from the test directory. Thanks for pointing this out. I'll see If I can change this in the documentation.
If the tests do not show any output, that means that it ran fine. I see you got some warnings, because our code was made with an older version of numpy. I'll see if I can make those warnings go away.

[imerelli]

Thank you for your help.
If I have understood the workflow of how to use your tool correctly, I need three scripts: 1) one for creating the database with multiple variants that I already know to be pathogenic or benign, 2) one to train the network, and 3) one to make predictions with a novel, unknown variant. Is this correct?
If yes, could you please provide a template for these three scripts? There is something about the first two (but with only one variant), but nothing for step 3. Sorry, I'm not a Python programmer and I need help with this.

[cbaakman]

To design a script, let me start by asking how your data is organized.
Deeprank-mut requires:

• PDB files, containing the protein structures
• residue number and chain identifier of the residue that gets swapped
• amino acid to replace the residue by

Do you perhaps have a sample of your data table? That could help us design a preprocessing script.

[imerelli]

deeprank.zip

I have all the requested information. Please find in attachment a zip archive with the pdb and the pssm files as well as a tentative script for the first two steps (inside there are the information about the known mutations, but I don't know how to proceed with more than one mutation). No idea on how to write the third script.

[cbaakman]

That's an odd looking PSSM. How does one read this?
The PSSM format that deeprank works with looks like this:

dbresi pdbresn seqresi seqresn    A    R    N    D    C    Q    E    G    H    I    L    K    M    F    P    S    T    W    Y    V   IC
      0       M       1       M   -2   -2   -3   -4   -2   -1   -3   -4   -3    0    1   -2    9   -1   -4   -2   -2   -2   -2    0 1.05
      1       V       2       V    1   -2   -1   -2   -2   -1   -1    5   -2   -3   -3   -2   -2   -3   -2    1    1   -3   -3   -2 0.64
      2       L       3       L   -3   -3   -5   -5   -2   -3   -4   -5   -4    1    6   -4    1    0   -4   -4   -2   -3   -2    0 0.94
      3       S       4       S    0   -2    2   -1   -2   -1   -1   -1   -2   -3   -3   -1    0   -3   -2    5    2   -4   -3   -2 0.69
      4       E       5       E   -2   -2    0    6   -4    0    4   -1   -2   -4   -5   -1   -4   -5   -2    0   -2   -5   -4   -4 0.98
      5       G       6       G    4   -1   -1   -1   -2    1    0    3   -2   -3   -3    0   -2   -4   -2    1   -1   -4   -3   -1 0.41
      6       E       7       E   -3   -2    1    5   -5    2    5   -3   -2   -5   -5   -1   -4   -5   -3   -2   -2   -5   -4   -4 1.05
      7       W       8       W   -4    1   -4   -5   -4   -1   -4   -4    1   -3   -2   -2   -3    4   -5   -4   -4   10    3   -2 1.59
      8       Q       9       Q   -2    0    1    4   -4    5    2   -3   -1   -3   -4    1   -3   -4   -3   -1   -2   -4   -3   -1 0.67
      9       L      10       L    0    0   -1   -3   -2    0   -1   -3    1    0    2    1    3   -1   -2   -1    1   -3   -2    0 0.15

[imerelli]

It's in json format instead of matrix. We will convert it if necessary. But it is not mandatory right?

[cbaakman]

I think you need to convert it. Sorry!

[cbaakman]

I've looked through your generate.py script. You can use it for preprocessing known variant data (1) just as well for preprocessing unknown variant data (3). With only slight modifications for (3):

A) leave out the variant_class argument, when instantiating a PdbVariantSelection object. It's optional.
B) set compute_targets=[] in the DataGenerator object. This will make sure that the preprocessing won't look for a class value to store.

[imerelli]

1. Concerning the pssm matrix, I achieved it using Psi-blast at NCBI. How do you usually compute this matrix? I can write a script for conversion but it will be quite tricky.

2. Before getting to part (3) our problem is to put in the database more than one variation in step (1). I don't get the python syntax that I should use.

[cbaakman]

1. We used PSIBLAST too to compute this matrix, but the PSSM data needs to be mapped to the PDB file.
   For that, we used a tool called PSSMgen: https://github.com/DeepRank/pssmgen

2. You need to have a CSV table file, containing your variant data: PDBID, residue number, amino acid,
   Then the script can use the pandas library to extract the data from that CSV file and put it in PdbVariantSelection objects.

[imerelli]

Hi, thank you for provicing the 3 scripts. I created the table.csv and the generate.py. Here the error I'm getting now:

python generate.py 
Traceback (most recent call last):
  File "generate.py", line 5, in <module>
    from deeprank.generate import *
  File "/opt/tools/deg/DeepRank-Mut/deeprank/generate/__init__.py", line 1, in <module>
    from .DataGenerator import DataGenerator
  File "/opt/tools/deg/DeepRank-Mut/deeprank/generate/DataGenerator.py", line 17, in <module>
    from deeprank.generate import GridTools as gt
  File "/opt/tools/deg/DeepRank-Mut/deeprank/generate/GridTools.py", line 13, in <module>
    from deeprank.operate import hdf5data
  File "/opt/tools/deg/DeepRank-Mut/deeprank/operate/hdf5data.py", line 7, in <module>
    from deeprank.domain.amino_acid import amino_acids
  File "/opt/tools/deg/DeepRank-Mut/deeprank/domain/amino_acid.py", line 52
    amino_acids_by_code {
                        ^
SyntaxError: invalid syntax

Also, two other minor points:

• I can't still find the pytest script to test the installation
• If I try to download the developmental brach, I get this

$ git clone --branch development https://github.com/DeepRank/DeepRank-mut.git
Cloning into 'DeepRank-mut'...
fatal: Remote branch development not found in upstream origin

It would be very useful to have a working example of this scripts, if possible...

[cbaakman]

Sorry about the syntax error. I fixed it.
I wouldn't recommend the developmental branch.

I'm trying to fix the pytest script, but it will take some time. Sorry, currently things are quite busy on our side.

[imerelli]

Sorry, there are still some errors.

python generate.py
Traceback (most recent call last):
File "generate.py", line 22, in
wildtype_amino_acid = amino_acids_by_code[row["WILDTYPE"]]
TypeError: 'set' object is not subscriptable

I don't see how to solve it.

There is also and indentation error here:
File "generate.py", line 53
grid_info = {
^
IndentationError: unexpected indent

But that was easy to solve.

[cbaakman]

Indeed. I pushed the fix. Sorry!

[imerelli]

Ok, now it's running. But I got this error.

803, 'O', 'O', 'A', 352, 'SER', '', '', 1.0], [-32.719, 25.157, 60.035, 2804, 'OG', 'O', 'A', 352, 'SER', '', '', 1.0], [-29.576, 22.331, 60.691, 2805, 'OXT', 'O', 'A', 352, 'SER', '', '', 1.0]] for x,y,z,rowID,name,element,chainID,resSeq,resName,iCode,altLoc,occ  
Creating database        : 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:23<00:00,  1.93s/it, variant=1CR4.pdb]
Molecules with errored features are removed:
['1CR4:A:200:Glutamine->Alanine-m7857361863297430751', '1CR4:A:281:Histidine->Alanine-m3856591489522861677', '1CR4:A:284:Isoleucine->Alanine-m6427764906900908035', '1CR4:A:259:Isoleucine->Alanine-m6594343748205688219', '1CR4:A:113:Histidine->Alanine-m8237672030338117968', '1CR4:A:203:Histidine->Alanine-7177783304471683210', '1CR4:A:171:Aspartate->Asparagine-614671715635415912', '1CR4:A:175:Alanine->Phenylalanine-7912783878010190788', '1CR4:A:187:Aspartate->Alanine-3861484985167401279', '1CR4:A:255:Tyrosine->Alanine-9130804594370861896', '1CR4:A:262:Aspartate->Asparagine-m4545163254089794910', '1CR4:A:288:Glutamate->Alanine-m5086511475306243927']

# Successfully created database: train_data.hdf5

Traceback (most recent call last):
  File "generate.py", line 59, in <module>
    database.map_features(grid_info,try_sparse=True, time=False, prog_bar=True)
  File "/opt/tools/deg/DeepRank-Mut/deeprank/generate/DataGenerator.py", line 1015, in map_features
    raise ValueError(f'No variants found in {self.hdf5}.')
ValueError: No variants found in train_data.hdf5.

I don't understand with my molecules are removed. The substitutions that I have indicated in the table.csv are valid.