This notebook gives an example of how to map a single protein sequence to its structure, along with conducting sequence alignments and visualizing the mutations.
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import sys
import logging
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# Import the Protein class
from ssbio.core.protein import Protein
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# Printing multiple outputs per cell
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
Set the logging level in logger.setLevel(logging.<LEVEL_HERE>) to specify how verbose you want the pipeline to be. Debug is most verbose.
CRITICALERRORWARNINGINFO (default)DEBUG
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# Create logger
logger = logging.getLogger()
logger.setLevel(logging.INFO) # SET YOUR LOGGING LEVEL HERE #
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# Other logger stuff for Jupyter notebooks
handler = logging.StreamHandler(sys.stderr)
formatter = logging.Formatter('[%(asctime)s] [%(name)s] %(levelname)s: %(message)s', datefmt="%Y-%m-%d %H:%M")
handler.setFormatter(formatter)
logger.handlers = [handler]
Set these three things:
ROOT_DIRPROTEIN_ID will be createdPROTEIN_IDPROTEIN_SEQA directory will be created in ROOT_DIR with your PROTEIN_ID name. The folders are organized like so:
ROOT_DIR
└── PROTEIN_ID
├── sequences # Protein sequence files, alignments, etc.
└── structures # Protein structure files, calculations, etc.
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# SET FOLDERS AND DATA HERE
import tempfile
ROOT_DIR = tempfile.gettempdir()
PROTEIN_ID = 'SRR1753782_00918'
PROTEIN_SEQ = 'MSKQQIGVVGMAVMGRNLALNIESRGYTVSVFNRSREKTEEVIAENPGKKLVPYYTVKEFVESLETPRRILLMVKAGAGTDAAIDSLKPYLEKGDIIIDGGNTFFQDTIRRNRELSAEGFNFIGTGVSGGEEGALKGPSIMPGGQKDAYELVAPILTKIAAVAEDGEPCVTYIGADGAGHYVKMVHNGIEYGDMQLIAEAYSLLKGGLNLSNEELANTFTEWNNGELSSYLIDITKDIFTKKDEDGNYLVDVILDEAANKGTGKWTSQSALDLGEPLSLITESVFARYISSLKAQRVAASKVLSGPKAQPAGDKAEFIEKVRRALYLGKIVSYAQGFSQLRAASDEYHWDLNYGEIAKIFRAGCIIRAQFLQKITDAYAENADIANLLLAPYFKKIADEYQQALRDVVAYAVQNGIPVPTFSAAVAYYDSYRAAVLPANLIQAQRDYFGAHTYKRTDKEGIFHTEWLE'
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# Create the Protein object
my_protein = Protein(ident=PROTEIN_ID, root_dir=ROOT_DIR, pdb_file_type='mmtf')
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# Load the protein sequence
# This sets the loaded sequence as the representative one
my_protein.load_manual_sequence(seq=PROTEIN_SEQ, ident='WT', write_fasta_file=True, set_as_representative=True)
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# Mapping using BLAST
my_protein.blast_representative_sequence_to_pdb(seq_ident_cutoff=0.9, evalue=0.00001)
my_protein.df_pdb_blast.head()
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# Download all mapped PDBs and gather the metadata
my_protein.download_all_pdbs()
my_protein.df_pdb_metadata.head(2)
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# Set representative structures
my_protein.set_representative_structure()
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my_protein.__dict__
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# Input your mutated sequence and load it
mutated_protein1_id = 'N17P_SNP'
mutated_protein1_seq = 'MSKQQIGVVGMAVMGRPLALNIESRGYTVSVFNRSREKTEEVIAENPGKKLVPYYTVKEFVESLETPRRILLMVKAGAGTDAAIDSLKPYLEKGDIIIDGGNTFFQDTIRRNRELSAEGFNFIGTGVSGGEEGALKGPSIMPGGQKDAYELVAPILTKIAAVAEDGEPCVTYIGADGAGHYVKMVHNGIEYGDMQLIAEAYSLLKGGLNLSNEELANTFTEWNNGELSSYLIDITKDIFTKKDEDGNYLVDVILDEAANKGTGKWTSQSALDLGEPLSLITESVFARYISSLKAQRVAASKVLSGPKAQPAGDKAEFIEKVRRALYLGKIVSYAQGFSQLRAASDEYHWDLNYGEIAKIFRAGCIIRAQFLQKITDAYAENADIANLLLAPYFKKIADEYQQALRDVVAYAVQNGIPVPTFSAAVAYYDSYRAAVLPANLIQAQRDYFGAHTYKRTDKEGIFHTEWLE'
my_protein.load_manual_sequence(ident=mutated_protein1_id, seq=mutated_protein1_seq)
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# Input another mutated sequence and load it
mutated_protein2_id = 'Q4S_N17P_SNP'
mutated_protein2_seq = 'MSKSQIGVVGMAVMGRPLALNIESRGYTVSVFNRSREKTEEVIAENPGKKLVPYYTVKEFVESLETPRRILLMVKAGAGTDAAIDSLKPYLEKGDIIIDGGNTFFQDTIRRNRELSAEGFNFIGTGVSGGEEGALKGPSIMPGGQKDAYELVAPILTKIAAVAEDGEPCVTYIGADGAGHYVKMVHNGIEYGDMQLIAEAYSLLKGGLNLSNEELANTFTEWNNGELSSYLIDITKDIFTKKDEDGNYLVDVILDEAANKGTGKWTSQSALDLGEPLSLITESVFARYISSLKAQRVAASKVLSGPKAQPAGDKAEFIEKVRRALYLGKIVSYAQGFSQLRAASDEYHWDLNYGEIAKIFRAGCIIRAQFLQKITDAYAENADIANLLLAPYFKKIADEYQQALRDVVAYAVQNGIPVPTFSAAVAYYDSYRAAVLPANLIQAQRDYFGAHTYKRTDKEGIFHTEWLE'
my_protein.load_manual_sequence(ident=mutated_protein2_id, seq=mutated_protein2_seq)
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# Conduct pairwise sequence alignments
my_protein.pairwise_align_sequences_to_representative()
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# View IDs of all sequence alignments
[x.id for x in my_protein.sequence_alignments]
# View the stored information for one of the alignments
my_alignment = my_protein.sequence_alignments.get_by_id('WT_Q4S_N17P_SNP')
my_alignment.annotations
str(my_alignment[0].seq)
str(my_alignment[1].seq)
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# Summarize all the mutations in all sequence alignments
s,f = my_protein.sequence_mutation_summary(alignment_type='seqalign')
print('Single mutations:')
s
print('---------------------')
print('Mutation fingerprints')
f
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import ssbio.databases.uniprot
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this_examples_uniprot = 'P14062'
sites = ssbio.databases.uniprot.uniprot_sites(this_examples_uniprot)
my_protein.representative_sequence.features = sites
my_protein.representative_sequence.features
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# Returns a dictionary mapping sequence residue numbers to structure residue identifiers
# Will warn you if residues are not present in the structure
structure_sites = my_protein.map_seqprop_resnums_to_structprop_resnums(resnums=[1,3,45],
use_representatives=True)
structure_sites
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The awesome package nglview is utilized as a backend for viewing structures within a Jupyter notebook. ssbio view functions will either return a NGLWidget object, which is the same as using nglview like the below example, or act upon the widget object itself.
# This is how NGLview usually works - it will load a structure file and return a NGLWidget "view" object.
import nglview
view = nglview.show_structure_file(my_protein.representative_structure.structure_path)
view
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# View just the structure
view = my_protein.representative_structure.view_structure(recolor=True)
view
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# Map the mutations on the visualization (scale increased) - will show up on the above view
my_protein.add_mutations_to_nglview(view=view, alignment_type='seqalign', scale_range=(4,7),
use_representatives=True)
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# Add sites as shown above in the table to the view
my_protein.add_features_to_nglview(view=view, use_representatives=True)
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import os.path as op
my_protein.save_json(op.join(my_protein.protein_dir, '{}.json'.format(my_protein.id)))