Note

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Conservation of binding site#

The web page on sequence logos on Wikipedia shows the sequence logo of the LexA-binding motif of Gram-positive bacteria. In this example we look at the other side: What is the amino acid sequence logo of the DNA-binding site of the LexA repressor? What is the consensus sequence?

We start by searching the NCBI Entrez database for lexA gene entries in the UniProtKB database and downloading them afterwards as GenPept file. In order to ensure that the file contains the desired entries, we check the entires for their definition (title) and source (species).

# Code source: Patrick Kunzmann
# License: BSD 3 clause

import matplotlib.pyplot as plt
import biotite.application.clustalo as clustalo
import biotite.database.entrez as entrez
import biotite.sequence as seq
import biotite.sequence.graphics as graphics
import biotite.sequence.io.genbank as gb

# Search for protein products of LexA gene in UniProtKB/Swiss-Prot database
query = entrez.SimpleQuery("lexA", "Gene Name") & entrez.SimpleQuery(
    "srcdb_swiss-prot", "Properties"
)
# Search for the first 200 hits
# More than 200 UIDs are not recommended for the EFetch service
# for a single fetch
uids = entrez.search(query, db_name="protein", number=200)
file = entrez.fetch_single_file(uids, None, db_name="protein", ret_type="gp")
# The file contains multiple concatenated GenPept files
# -> Usage of MultiFile
multi_file = gb.MultiFile.read(file)
# Separate MultiFile into single GenBankFile instances
files = [f for f in multi_file]
print("Definitions:")
for file in files[:20]:
    print(gb.get_definition(file))
print()
print("Sources:")
for file in files[:20]:
    print(gb.get_source(file))

Definitions:
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.
RecName: Full=Transcription regulator LexA; AltName: Full=LexA-related repressor.
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.

Sources:
Mycobacterium tuberculosis H37Rv
Herpetosiphon aurantiacus DSM 785
Clostridium botulinum A3 str. Loch Maree
Klebsiella variicola 342
Brucella canis ATCC 23365
Bacillus cereus ATCC 14579
Acidothermus cellulolyticus 11B
Syntrophobacter fumaroxidans MPOB
Escherichia coli HS
Pseudomonas paraeruginosa PA7
Klebsiella pneumoniae subsp. pneumoniae MGH 78578
Carboxydothermus hydrogenoformans Z-2901
Saccharophagus degradans 2-40
Myxococcus xanthus DK 1622
Synechocystis sp. PCC 6803 substr. Kazusa
Escherichia coli K-12
Idiomarina loihiensis L2TR
Staphylococcus aureus subsp. aureus N315
Bradyrhizobium diazoefficiens USDA 110
Leptospira interrogans serovar Lai str. 56601

The names of the sources are too long to be properly displayed later on. Therefore, we write a function that creates a proper abbreviation for a species name.

def abbreviate(species):
    # Remove possible brackets
    species = species.replace("[", "").replace("]", "")
    splitted_species = species.split()
    return "{:}. {:}".format(splitted_species[0][0], splitted_species[1])


print("Sources:")
all_sources = [abbreviate(gb.get_source(file)) for file in files]
for source in all_sources[:20]:
    print(source)

Sources:
M. tuberculosis
H. aurantiacus
C. botulinum
K. variicola
B. canis
B. cereus
A. cellulolyticus
S. fumaroxidans
E. coli
P. paraeruginosa
K. pneumoniae
C. hydrogenoformans
S. degradans
M. xanthus
S. sp.
E. coli
I. loihiensis
S. aureus
B. diazoefficiens
L. interrogans

Much better. For the alignment (required for sequence logo) we need to extract the slice of the sequence, that belongs to the DNA-binding site. Hence, we simply index the each sequence with the feature for the binding site and remove those sequences, that do not have a record specifying the required feature.

But we have still an issue: Some species seem to be overrepresented, as they show up multiple times. The reason for this is that some species, like M. tuberculosis, are represented by multiple strains with (almost) equal LexA sequences. To reduce this bias, we only want each species to occur only a single time. So we use a set to store the source name of sequences we already listed and ignore all further occurrences of that source species.

# List of sequences
binding_sites = []
# List of source species
sources = []
# Set for ignoring already listed sources
listed_sources = set()
for file, source in zip(files, all_sources):
    if source in listed_sources:
        # Ignore already listed species
        continue
    bind_feature = None
    annot_seq = gb.get_annotated_sequence(file, include_only=["Site"], format="gp")
    # Find the feature for DNA-binding site
    for feature in annot_seq.annotation:
        # DNA binding site is a helix-turn-helix motif
        if (
            "site_type" in feature.qual
            and feature.qual["site_type"] == "DNA binding"
            and "H-T-H motif" in feature.qual["note"]
        ):
            bind_feature = feature
    if bind_feature is not None:
        # If the feature is found,
        # get the sequence slice that is defined by the feature...
        binding_sites.append(annot_seq[bind_feature])
        # ...and save the respective source species
        sources.append(source)
        listed_sources.add(source)
print("Binding sites:")
for site in binding_sites[:20]:
    print(site)

Binding sites:
IREIGDAVGLTSTSSVAHQLR
IRDIQRELSISSTSVVAYNLR
VREICKAVGLSSTSSVHFHLK
RAEIAQRLGFRSPNAAEEHLK
FDEMKEALDLASKSGIHRLIT
VREIGQAVGLASSSTVHGHLS
MREIGEAVGLTSTSSVAHQLM
YEEIRQNLGFRSLNAVFKHLK
RAEIAQRLGFRSPNAAEEHLK
RAEIAQELGFKSPNAAEEHLK
RAEIAQRLGFRSPNAAEEHLK
IREICQGVGLSSPSTVHHHLK
RAEIANQLGFRSANAAEEHLK
IREIGEHMDIRSTNGVNDHLK
RAEIAHKLGFRSANSAEEHLK
VREIGEAVGLASSSTVHGHLS
FDEMKDALDLRSKSGIHRLIT
IREIGDEFGITAKGAYDHLK
VREIGEAVGLASSSTVHGHLA
MREIGQAVGLSSTSSVAHQLM

Now we can perform a multiple sequence alignment of the binding site sequences. Here we use Clustal Omega to perform this task. Since we have up to 200 sequences we visualize only a small portion of the alignment.

alignment = clustalo.ClustalOmegaApp.align(binding_sites)
fig = plt.figure(figsize=(4.5, 4.0))
ax = fig.add_subplot(111)
graphics.plot_alignment_similarity_based(
    ax, alignment[:, :20], labels=sources[:20], symbols_per_line=len(alignment)
)
# Source names in italic
ax.set_yticklabels(ax.get_yticklabels(), fontdict={"fontstyle": "italic"})
fig.tight_layout()
lexa conservation

Finally we can generate our sequence logo and the consensus sequence.

profile = seq.SequenceProfile.from_alignment(alignment)

print("Consensus sequence:")
print(profile.to_consensus())

fig = plt.figure(figsize=(8.0, 3.0))
ax = fig.add_subplot(111)
graphics.plot_sequence_logo(ax, profile, scheme="flower")
ax.set_xticks([5, 10, 15, 20])
ax.set_xlabel("Residue position")
ax.set_ylabel("Bits")
# Only show left and bottom spine
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
fig.tight_layout()

plt.show()
lexa conservation
Consensus sequence:
RAEIARALGFRSPNAAEEHLK

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