CAD-score-LT Python bindings via SWIG
Project description
CAD-score-LT Python bindings
The CAD-score-LT Python interface PyPI package is hosted at https://pypi.org/project/cadscorelt/.
Installation
Install with pip using this command:
pip install cadscorelt
Additionally, it is recommended to have the pandas library for data analysis available in the Python environment.
This allows the CAD-score result tables to be converted to pandas data frames.
The pandas library can also be installed using pip:
pip install pandas
CAD-score-LT also provides integration with some common libraries for reading macromolecular files - Biotite, Gemmi, Biopython,
if those libraries are available in the Python environment.
They can be installed via pip:
pip install biotite
pip install gemmi
pip install biopython
Usage examples
Basic example
Below is an example script that calculates CAD-scores for inter-chain residue-residue contact areas,
produces a table of global scores,
converts that table to pandas data frame,
and preints the top rows of the data frame:
import cadscorelt
# init a CAD-score computation object
css = cadscorelt.CADScoreComputer.init(subselect_contacts="[-inter-chain]")
# add a target structure
css.add_target_structure_from_file("./input/data/protein_homodimer1/target.pdb")
# add a target structure
css.add_model_structure_from_file("./input/data/protein_homodimer1/model1.pdb")
css.add_model_structure_from_file("./input/data/protein_homodimer1/model2.pdb")
# get a list of global scores and convert it to pandas data frame
df_global_scores_residue_residue = css.get_all_cadscores_residue_residue_summarized_globally().to_pandas()
# print the first rows of the data frame
cadscorelt.print_head_of_pandas_data_frame(df_global_scores_residue_residue)
Below is an example of the printed output:
target_name model_name CAD_score F1_of_areas target_area model_area TP_area FP_area FN_area renamed_chains
target model2 0.621922 0.774894 1047.807935 941.514533 784.870893 193.071031 262.937041 .
target model1 0.507319 0.639249 1047.807935 792.834440 648.098138 331.779276 399.709796 .
Basic example using different structure readers
Below is an example script that is similar to the previous example script, but it shows how to input structures from different sources:
import cadscorelt
# init a CAD-score computation object
csc = cadscorelt.CADScoreComputer.init(subselect_contacts="[-inter-chain]")
# add a target structure read by Biotite
import biotite.structure.io
structure_target = biotite.structure.io.load_structure("./input/data/protein_homodimer1/target.pdb")
csc.add_target_structure_from_biotite(structure_target, "target")
# add a model structure read by Gemmi
import gemmi
structure_model1 = gemmi.read_structure("./input/data/protein_homodimer1/model1.pdb")
csc.add_model_structure_from_gemmi(structure_model1[0], "model1")
# add a model structure read by Biopython
import Bio.PDB
parser = Bio.PDB.PDBParser(QUIET=True)
structure_model2 = parser.get_structure("id", "./input/data/protein_homodimer1/model2.pdb").get_atoms()
csc.add_model_structure_from_biopython(structure_model2, "model2")
# get a list of global scores and convert it to pandas data frame
df_global_scores_residue_residue = csc.get_all_cadscores_residue_residue_summarized_globally().to_pandas()
# print the first rows of the data frame
cadscorelt.print_head_of_pandas_data_frame(df_global_scores_residue_residue)
Below is an example of the printed output:
target_name model_name CAD_score F1_of_areas target_area model_area TP_area FP_area FN_area renamed_chains
target model2 0.621922 0.774894 1047.808013 941.514579 784.870926 193.071041 262.937087 .
target model1 0.507319 0.639249 1047.808013 792.834440 648.098141 331.779274 399.709873 .
Advanced example
Below is an example script that that includes:
- residue-residue contact scoring (enabled by default);
- atom-atom contact scoring;
- automatic chain remapping to maximize grobal similarity;
- recording local scores.
import cadscorelt
from pathlib import Path
# to make comparison more strict, globally enable inclusion of residue names into atom and residue identifiers
cadscorelt.enable_considering_residue_names()
# init a CAD-score computation object, enable atom-atom contact scoring, enable automatic chain remapping to maximize grobal similarity, enable recording local scores
csc = cadscorelt.CADScoreComputer.init(subselect_contacts="[-inter-chain]", score_atom_atom_contacts=True, remap_chains=True, record_local_scores=True)
# set reference sequences and stoichiometry for automatic residue renumbering and chain namne assignment
csc.set_reference_sequences_from_file("./input/data/protein_heteromer1/sequences.fasta")
csc.set_reference_stoichiometry([2, 2, 2])
# input structures from all the files in a directory
input_directory = Path("./input/data/protein_heteromer1/structures")
for file_path in input_directory.iterdir():
if file_path.is_file():
csc.add_structure_from_file(str(file_path))
# get the table of structure decriptors and print its top rows
df_structure_descriptors = csc.get_all_structure_descriptors().to_pandas()
print("")
print(" # Table of structure decriptors:")
print("")
cadscorelt.print_head_of_pandas_data_frame(df_structure_descriptors)
print("")
# get the table of globals scores based on residue-residue contacts, print top rows
df_global_scores_residue_residue = csc.get_all_cadscores_residue_residue_summarized_globally().to_pandas()
print("")
print(" # Table of globals scores based on residue-residue contacts:")
print("")
cadscorelt.print_head_of_pandas_data_frame(df_global_scores_residue_residue)
print("")
# get the table of globals scores based on atom-atom contacts, print top rows
df_global_scores_atom_atom = csc.get_all_cadscores_atom_atom_summarized_globally().to_pandas()
print("")
print(" # Table of globals scores based on atom-atom contacts:")
print("")
cadscorelt.print_head_of_pandas_data_frame(df_global_scores_atom_atom)
print("")
# set placeholder variable for structure names
target_name="cf_woTemplates_model_3_multimer_v3_pred_47"
model_name="cf_woTemplates_model_2_multimer_v3_pred_26"
# get the table of per-residue scores based on residue-residue contacts, print top rows
df_local_scores_per_residue = csc.get_local_cadscores_residue_residue_summarized_per_residue(target_name, model_name).to_pandas()
print("")
print(" # Table of per-residue scores based on residue-residue contacts:")
print("")
cadscorelt.print_head_of_pandas_data_frame(df_local_scores_per_residue)
print("")
# get the table of scores for every residue-residue contact, print top rows
df_local_scores_residue_residue = csc.get_local_cadscores_residue_residue(target_name, model_name).to_pandas()
print("")
print(" # Table of scores for every residue-residue contact (CAD-score values of -1 idicate that the contact was not present in the target structure):")
print("")
cadscorelt.print_head_of_pandas_data_frame(df_local_scores_residue_residue)
print("")
# get the table of per-atom scores based on atom-atom contacts, print top rows
df_local_scores_per_atom = csc.get_local_cadscores_atom_atom_summarized_per_atom(target_name, model_name).to_pandas()
print("")
print(" # Table of per-atom scores based on atom-atom contacts (CAD-score values of -1 idicate that the atom had no relevant contacts in the target structure):")
print("")
cadscorelt.print_head_of_pandas_data_frame(df_local_scores_per_atom)
print("")
# get the table of scores for every atom-atom contact, print top rows
df_local_scores_atom_atom = csc.get_local_cadscores_atom_atom(target_name, model_name).to_pandas()
print("")
print(" # Table of scores for every atom-atom contact (CAD-score values of -1 idicate that the contact was not present in the target structure):")
print("")
cadscorelt.print_head_of_pandas_data_frame(df_local_scores_atom_atom)
print("")
Below is an example of the printed output:
# Table of structure decriptors:
name is_target is_model renamed_chains reference_alignment
afm_basic_model_5_multimer_v1_pred_35 True True B=A,C=D,D=B,E=E,F=C,G=F available
afm_dropout_full_model_1_multimer_v2_pred_42 True True B=A,C=D,D=B,E=E,F=C,G=F available
afm_dropout_full_model_2_multimer_v1_pred_65 True True B=A,C=D,D=B,E=E,F=C,G=F available
afm_dropout_full_model_3_multimer_v3_pred_64 True True B=A,C=D,D=B,E=E,F=C,G=F available
afm_dropout_full_model_3_multimer_v3_pred_66 True True B=A,C=D,D=B,E=E,F=C,G=F available
afm_dropout_full_woTemplates_model_3_multimer_v1_pred_4 True True B=A,C=D,D=B,E=E,F=C,G=F available
afm_dropout_full_woTemplates_model_3_multimer_v1_pred_45 True True B=A,C=D,D=B,E=E,F=C,G=F available
afm_dropout_full_woTemplates_model_4_multimer_v3_pred_50 True True B=A,C=D,D=B,E=E,F=C,G=F available
cf_woTemplates_model_2_multimer_v3_pred_26 True True A=A,B=D,C=B,D=E,E=C,F=F available
cf_woTemplates_model_3_multimer_v3_pred_47 True True A=A,B=D,C=B,D=E,E=C,F=F available
# Table of globals scores based on residue-residue contacts:
target_name model_name CAD_score F1_of_areas target_area model_area TP_area FP_area FN_area renamed_chains
afm_dropout_full_model_1_multimer_v2_pred_42 afm_dropout_full_woTemplates_model_4_multimer_v3_pred_50 0.847662 0.701359 3970.007175 3836.593118 3582.890995 2664.103761 387.116180 A=A;B=B;C=F;D=D;E=E;F=C
cf_woTemplates_model_3_multimer_v3_pred_47 cf_woTemplates_model_2_multimer_v3_pred_26 0.704688 0.780891 7483.973156 6871.838277 5912.094425 1745.848929 1571.878730 A=D;B=E;C=F;D=A;E=B;F=C
cf_woTemplates_model_2_multimer_v3_pred_26 cf_woTemplates_model_3_multimer_v3_pred_47 0.699040 0.781187 7657.943354 6855.599895 5914.330867 1569.642289 1743.612487 A=A;B=B;C=C;D=D;E=E;F=F
afm_dropout_full_model_3_multimer_v3_pred_66 afm_dropout_full_model_3_multimer_v3_pred_64 0.657842 0.675283 7313.203511 5519.154809 5124.561234 2739.763621 2188.642277 A=A;B=B;C=C;D=D;E=E;F=F
afm_dropout_full_model_3_multimer_v3_pred_64 afm_dropout_full_model_3_multimer_v3_pred_66 0.620764 0.675283 7864.324855 5420.088717 5124.561234 2188.642277 2739.763621 A=A;B=B;C=C;D=D;E=E;F=F
afm_dropout_full_model_3_multimer_v3_pred_66 cf_woTemplates_model_2_multimer_v3_pred_26 0.575097 0.645935 7313.203511 5696.007986 4835.195167 2822.748187 2478.008344 A=A;B=E;C=C;D=D;E=B;F=F
cf_woTemplates_model_2_multimer_v3_pred_26 afm_dropout_full_model_3_multimer_v3_pred_66 0.573808 0.645935 7657.943354 5446.078054 4835.195167 2478.008344 2822.748187 A=A;B=E;C=C;D=D;E=B;F=F
afm_dropout_full_model_3_multimer_v3_pred_66 cf_woTemplates_model_3_multimer_v3_pred_47 0.566239 0.627281 7313.203511 5493.415167 4640.996876 2842.976280 2672.206635 A=D;B=B;C=F;D=A;E=E;F=C
cf_woTemplates_model_3_multimer_v3_pred_47 afm_dropout_full_model_3_multimer_v3_pred_66 0.561418 0.627332 7483.973156 5252.699687 4641.373085 2671.830426 2842.600071 A=A;B=E;C=C;D=D;E=B;F=F
cf_woTemplates_model_2_multimer_v3_pred_26 afm_dropout_full_model_3_multimer_v3_pred_64 0.550970 0.603414 7657.943354 5255.694013 4683.177891 3181.146964 2974.765463 A=A;B=E;C=C;D=D;E=B;F=F
# Table of globals scores based on atom-atom contacts:
target_name model_name CAD_score F1_of_areas target_area model_area TP_area FP_area FN_area renamed_chains
afm_dropout_full_model_1_multimer_v2_pred_42 afm_dropout_full_woTemplates_model_4_multimer_v3_pred_50 0.694870 0.612790 3970.007175 3631.663716 3130.437751 3116.557004 839.569423 A=A;B=B;C=F;D=D;E=E;F=C
afm_dropout_full_model_3_multimer_v3_pred_66 afm_dropout_full_model_3_multimer_v3_pred_64 0.600300 0.630986 7313.203511 5287.148112 4788.403033 3075.921822 2524.800478 A=A;B=B;C=C;D=D;E=E;F=F
afm_dropout_full_model_3_multimer_v3_pred_64 afm_dropout_full_model_3_multimer_v3_pred_66 0.566073 0.630986 7864.324855 5222.053418 4788.403033 2524.800478 3075.921822 A=A;B=B;C=C;D=D;E=E;F=F
cf_woTemplates_model_3_multimer_v3_pred_47 cf_woTemplates_model_2_multimer_v3_pred_26 0.559002 0.635331 7483.973156 5931.794420 4810.063036 2847.880319 2673.910120 A=D;B=E;C=F;D=A;E=B;F=C
cf_woTemplates_model_2_multimer_v3_pred_26 cf_woTemplates_model_3_multimer_v3_pred_47 0.550378 0.635281 7657.943354 5972.073003 4809.683681 2674.289475 2848.259673 A=A;B=B;C=C;D=D;E=E;F=F
afm_dropout_full_model_3_multimer_v3_pred_66 cf_woTemplates_model_2_multimer_v3_pred_26 0.455307 0.527325 7313.203511 5000.847832 3947.330056 3710.613299 3365.873455 A=A;B=E;C=C;D=D;E=B;F=F
cf_woTemplates_model_2_multimer_v3_pred_26 afm_dropout_full_model_3_multimer_v3_pred_66 0.446372 0.527325 7657.943354 4761.917277 3947.330056 3365.873455 3710.613299 A=A;B=E;C=C;D=D;E=B;F=F
cf_woTemplates_model_2_multimer_v3_pred_26 afm_dropout_full_model_3_multimer_v3_pred_64 0.445539 0.506868 7657.943354 4677.771912 3933.871243 3930.453612 3724.072111 A=A;B=E;C=C;D=D;E=B;F=F
cf_woTemplates_model_3_multimer_v3_pred_47 afm_dropout_full_model_3_multimer_v3_pred_64 0.439929 0.489985 7483.973156 4453.132335 3760.215370 4104.109485 3723.757786 A=D;B=B;C=F;D=A;E=E;F=C
afm_dropout_full_model_3_multimer_v3_pred_66 cf_woTemplates_model_3_multimer_v3_pred_47 0.439601 0.499394 7313.203511 4526.494827 3694.810365 3789.162791 3618.393146 A=D;B=B;C=F;D=A;E=E;F=C
# Table of per-residue scores based on residue-residue contacts:
ID_chain ID_rnum ID_icode CAD_score F1_of_areas target_area model_area TP_area FP_area FN_area
A 4 . 0.000000 0.000000 5.723360 0.000000 0.000000 14.123374 5.723360
A 6 . 0.000000 0.504191 7.178632 14.612032 7.160650 14.065207 0.017982
A 15 . 0.000000 0.000000 1.346514 0.000000 0.000000 2.051384 1.346514
A 17 . 0.175261 0.298250 25.529513 4.474315 4.474315 0.000000 21.055197
A 18 . 0.378808 0.293841 26.519188 10.045678 10.045678 31.810069 16.473510
A 19 . 0.715554 0.783253 69.951082 79.263636 63.086289 28.050569 6.864793
A 20 . 0.228410 0.399188 97.824322 75.742435 35.484263 44.473835 62.340058
A 21 . 0.892938 0.923627 55.431386 54.675708 52.086259 5.268696 3.345127
A 22 . 0.000000 0.277170 0.401577 2.496120 0.401577 2.094543 0.000000
A 23 . 0.406606 0.631095 34.355429 20.906229 17.437673 3.468556 16.917756
# Table of scores for every residue-residue contact (CAD-score values of -1 idicate that the contact was not present in the target structure):
ID1_chain ID1_rnum ID1_icode ID2_chain ID2_rnum ID2_icode CAD_score F1_of_areas target_area model_area TP_area FP_area FN_area
A 4 . D 6 . 0.000000 0.000000 0.043450 0.000000 0.000000 0.000000 0.043450
A 4 . D 61 . 0.000000 0.000000 5.679910 0.000000 0.000000 0.000000 5.679910
A 4 . D 206 . -1.000000 0.000000 0.000000 0.000000 0.000000 14.123374 0.000000
A 6 . D 4 . 0.000000 0.000000 0.017982 0.000000 0.000000 0.000000 0.017982
A 6 . D 65 . 0.000000 0.657765 7.160650 14.612032 7.160650 7.451382 0.000000
A 6 . D 208 . -1.000000 0.000000 0.000000 0.000000 0.000000 6.613825 0.000000
A 15 . C 31 . -1.000000 0.000000 0.000000 0.000000 0.000000 2.051384 0.000000
A 15 . D 137 . 0.000000 0.000000 1.346514 0.000000 0.000000 0.000000 1.346514
A 17 . C 31 . 0.086301 0.158889 16.995494 1.466721 1.466721 0.000000 15.528773
A 17 . C 32 . 0.557286 0.715714 5.396860 3.007595 3.007595 0.000000 2.389265
# Table of per-atom scores based on atom-atom contacts (CAD-score values of -1 idicate that the atom had no relevant contacts in the target structure):
ID_chain ID_rnum ID_icode ID_atom_name CAD_score F1_of_areas target_area model_area TP_area FP_area FN_area
A 4 . CD -1.000000 0.000000 0.000000 0.000000 0.000000 0.589435 0.000000
A 4 . CE -1.000000 0.000000 0.000000 0.000000 0.000000 6.218582 0.000000
A 4 . CG -1.000000 0.000000 0.000000 0.000000 0.000000 0.040717 0.000000
A 4 . NZ 0.000000 0.000000 5.723360 0.000000 0.000000 7.274641 5.723360
A 6 . CB -1.000000 0.000000 0.000000 0.000000 0.000000 0.005776 0.000000
A 6 . CG1 -1.000000 0.000000 0.000000 0.000000 0.000000 10.924598 0.000000
A 6 . CG2 0.315134 0.527725 7.178632 8.956361 4.610766 5.684717 2.567866
A 15 . CB -1.000000 0.000000 0.000000 0.000000 0.000000 1.960049 0.000000
A 15 . O 0.000000 0.000000 1.346514 0.000000 0.000000 0.091335 1.346514
A 17 . CB 0.057452 0.097769 25.529513 1.466721 1.466721 3.007595 24.062792
# Table of scores for every atom-atom contact (CAD-score values of -1 idicate that the contact was not present in the target structure):
ID1_chain ID1_rnum ID1_icode ID1_atom_name ID2_chain ID2_rnum ID2_icode ID2_atom_name CAD_score F1_of_areas target_area model_area TP_area FP_area FN_area
A 4 . CD D 206 . OE1 -1.0 0.0 0.000000 0.0 0.0 0.589435 0.000000
A 4 . CE D 206 . NE2 -1.0 0.0 0.000000 0.0 0.0 1.398847 0.000000
A 4 . CE D 206 . OE1 -1.0 0.0 0.000000 0.0 0.0 4.819735 0.000000
A 4 . CG D 206 . OE1 -1.0 0.0 0.000000 0.0 0.0 0.040717 0.000000
A 4 . NZ D 6 . CG2 0.0 0.0 0.043450 0.0 0.0 0.000000 0.043450
A 4 . NZ D 61 . CZ 0.0 0.0 0.413695 0.0 0.0 0.000000 0.413695
A 4 . NZ D 61 . OH 0.0 0.0 5.266215 0.0 0.0 0.000000 5.266215
A 4 . NZ D 206 . CD -1.0 0.0 0.000000 0.0 0.0 0.008074 0.000000
A 4 . NZ D 206 . NE2 -1.0 0.0 0.000000 0.0 0.0 5.372747 0.000000
A 4 . NZ D 206 . OE1 -1.0 0.0 0.000000 0.0 0.0 1.893820 0.000000
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