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| # https://github.com/grandrea/alphalink-ihm-template/ | ||
| # Author Andrea Graziadei | ||
| import ihm | ||
| import ihm.location | ||
| import ihm.dataset | ||
| import ihm.restraint | ||
| import ihm.protocol | ||
| import ihm.model | ||
| import ihm.cross_linkers | ||
| import ihm.reference | ||
| import ihm.dumper | ||
| import ihm.representation | ||
| import pandas as pd | ||
| import ihm.reader | ||
| import ihm.citations | ||
| import sys | ||
|
|
||
| if len(sys.argv)!=4: | ||
| print("usage: python make_ihm.py model.cif ") | ||
|
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||
| model_file = "model_1.cif" #model file converted to cif by MAXIT | ||
| uniprot_accession ="P0A910" | ||
| protein_name ="OmpA" | ||
| restraint_file = "restraint.csv" # a file with 3 space-separated columns for residue from, residue to and restraint confidence (1-FDR) | ||
|
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||
| with open(model_file) as fh: | ||
| system = ihm.reader.read(fh) | ||
| system=system[0] | ||
|
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||
| #change to PRIDE or whatever repository you have your cx-ms results in | ||
| crosslink_dataset = ihm.dataset.CXMSDataset(ihm.location.DatabaseLocation(db_name="jPOSTrepo", | ||
| db_code="JPST001851")) | ||
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| model_sequence = ihm.reference.UniProtSequence.from_accession(uniprot_accession) | ||
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| #this would need to be a loop for systems with more than one entity | ||
| entityA = system.entities[0] | ||
| entityA.references = [model_sequence] | ||
|
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||
| #change source as needed using ihm terms | ||
| entityA.source = ihm.source.Natural(ncbi_taxonomy_id=83333, | ||
| common_name="Escherichia coli", | ||
| scientific_name="Escherichia coli K12", | ||
| strain="K12") | ||
|
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||
| entityA.description = (protein_name) | ||
| asymA = system.asym_units[0] | ||
| asymA.description = str(protein_name) | ||
| asymA.details = str(protein_name) | ||
| assembly = ihm.Assembly((asymA,), name='Modeled assembly', description="modeled assembly") | ||
|
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| title_string = str("model of " + protein_name) | ||
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| system.title = title_string | ||
|
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| #change authors as needed | ||
| system.authors = ["author 1", | ||
| "author 2"] | ||
|
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| citation = ihm.Citation( | ||
| pmid=None, | ||
| title='mytitle', | ||
| journal='myjournal', volume=None, page_range=(1, 2), year=2023, | ||
| authors=["author 1", | ||
| "author 2"], | ||
| doi=None) | ||
| system.citations.append(alphalink) | ||
|
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||
| alphalink_software = ihm.Software(name="AlphaLink", | ||
| classification="model building", | ||
| description="protein structure prediction by deep learning assisted by experimental distance restraints", | ||
| location="https://github.com/lhatsk/AlphaLink", | ||
| version="1.0", | ||
| citation=alphalink) | ||
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| system.software.append(alphalink_software) | ||
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| rep = ihm.representation.Representation([ihm.representation.AtomicSegment(asymA, rigid=False)]) | ||
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| # Since our input models are plain PDBx, not IHM, we need to add additional | ||
| # required information on the model representation and assembly. This may be | ||
| # better handled by passing the file through python-ihm's | ||
| # util/make-mmcif.py first. | ||
| for state_group in system.state_groups: | ||
| for state in state_group: | ||
| for model_group in state: | ||
| for model in model_group: | ||
| if not model.assembly: | ||
| model.assembly = assembly | ||
| if not model.representation: | ||
| model.representation = rep | ||
|
|
||
| #define crosslinker. Here, photo-leucine. | ||
| photo_leucine = ihm.ChemDescriptor(auth_name="L-Photo-Leucine", | ||
| chem_comp_id=None, | ||
| smiles="CC1(C[C@H](N)C(O)=O)N=N1", | ||
| inchi="1S/C5H9N3O2/c1-5(7-8-5)2-3(6)4(9)10/h3H,2,6H2,1H3,(H,9,10)/t3-/m0/s1", | ||
| inchi_key="MJRDGTVDJKACQZ-VKHMYHEASA-N", | ||
| common_name="L-Photo-Leucine") | ||
|
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||
|
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| #if not using photo-leucine, use ihm.crosslinkers definitions | ||
| crosslink_restraint = ihm.restraint.CrossLinkRestraint(dataset=crosslink_dataset, | ||
| linker=photo_leucine) | ||
|
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||
| crosslink_df = pd.read_csv(restraint_file, sep=" ", names=["from", "to", "confidence"], header=None) | ||
| crosslinks = [] | ||
| # Usually cross-links use an upper bound restraint on the distance | ||
| distance = ihm.restraint.UpperBoundDistanceRestraint(10) | ||
| for index, line in crosslink_df.iterrows(): | ||
| res1ind = int(line["from"]) | ||
| res2ind = int(line["to"]) | ||
| # This assumes that residue indices in the CSV file map 1:1 to mmCIF | ||
| # seq_ids. Verify by checking the residue names in the ihm_cross_link_list | ||
| # in the output mmCIF. You may need to add an offset or otherwise map | ||
| # the residue indices, because it looks off to me. | ||
| residue_pair = ihm.restraint.ExperimentalCrossLink( | ||
| residue1=entityA.residue(res1ind), residue2=entityA.residue(res2ind)) | ||
| # This takes a list of all ambiguous cross-links. Here we're saying there | ||
| # is no ambiguity. | ||
| crosslink_restraint.experimental_cross_links.append([residue_pair]) | ||
| residue_pair_restraint = ihm.restraint.ResidueCrossLink(experimental_cross_link=residue_pair, | ||
| asym1=asymA, | ||
| asym2=asymA, | ||
| psi=(1 - line["confidence"]), | ||
| distance=distance) | ||
| crosslink_restraint.cross_links.append(residue_pair_restraint) | ||
|
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||
| system.restraints.append(crosslink_restraint) | ||
|
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| all_datasets = ihm.dataset.DatasetGroup((crosslink_dataset,)) | ||
|
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| protocol = ihm.protocol.Protocol(name='AlphaLink') | ||
| protocol.steps.append(ihm.protocol.Step( | ||
| assembly=system.complete_assembly, dataset_group=all_datasets, | ||
| method='AlphaLink', name='AlphaLink', | ||
| num_models_begin=0, num_models_end=5, multi_scale=False, ensemble=False)) | ||
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| for state_group in system.state_groups: | ||
| for state in state_group: | ||
| for model_group in state: | ||
| for model in model_group: | ||
| model.protocol = protocol | ||
| if not model.representation: | ||
| model.representation = rep | ||
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| with open("model.cif", "w", encoding="utf-8") as fh: | ||
| ihm.dumper.write(fh, [system]) | ||
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