Conservation of LexA DNA-binding siteΒΆ

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 numpy as np
import matplotlib.pyplot as plt
import biotite.sequence as seq
import biotite.sequence.io.fasta as fasta
import biotite.sequence.io.genbank as gb
import biotite.sequence.graphics as graphics
import biotite.application.clustalo as clustalo
import biotite.database.entrez as entrez
# 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=LexA repressor.
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.
RecName: Full=LexA repressor.

Sources:
Clostridioides difficile 630 (Clostridium difficile 630)
Desulfatibacillum aliphaticivorans
Clostridium botulinum Ba4 str. 657
Azotobacter vinelandii DJ
Desulfitobacterium hafniense DCB-2
Clostridium botulinum A2 str. Kyoto
Brucella melitensis ATCC 23457
Brevibacillus brevis NBRC 100599
Bifidobacterium longum subsp. infantis ATCC 15697 = JCM 1222 = DSM
Bacillus cereus Q1
Anaeromyxobacter dehalogenans 2CP-1
Burkholderia multivorans ATCC 17616
Cupriavidus taiwanensis LMG 19424
Delftia acidovorans SPH-1
Clostridium botulinum B1 str. Okra
Bifidobacterium longum DJO10A
Bordetella petrii DSM 12804
Burkholderia cenocepacia J2315
Clostridium botulinum A3 str. Loch Maree
Brucella abortus S19

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:
C. difficile
D. aliphaticivorans
C. botulinum
A. vinelandii
D. hafniense
C. botulinum
B. melitensis
B. brevis
B. longum
B. cereus
A. dehalogenans
B. multivorans
C. taiwanensis
D. acidovorans
C. botulinum
B. longum
B. petrii
B. cenocepacia
C. botulinum
B. abortus

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 occurences 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:
VREICTAVGLRSTSTVHSHLN
VRELCDELGFKSPNTAHFHLK
VREICKAVGLSSTSSVHFHLK
RAEIARELGFKSPNAAEEHLK
VREIGDAVGLMSSSTVHGHLQ
FDEMKEALDLASKSGIHRLIT
VREIGEAVGLASSSTVHGHLA
FREIGNAAGLKSPSSVKHQLQ
VREIGQAVGLASSSTVHGHLS
IREIGEALDIRSTNGVNDHLK
RAEIAAELGFSSPNAAEEHLR
RAEIAAEFGFSSPNAAEEHLR
RAEIANTLGFKSANAAEEHLQ
RAEIARALGFRSPNAAEDHLK
RAEIAAELGFSSPNAAEEHLR
FDEMKEALDLASKSGIHRLIT
RAEIAAELGFSSPNAAEEHLR
RAEIAAELGFSSPNAAEEHLR
VREIGQAVGLASSSTVHGHLS
RAEIARILGFKSANAAEEHIK

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:
RAEIADALGLRSPSAAHEHLK

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