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))

Out:

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:
Rhizorhabdus wittichii RW1
Citrifermentans bemidjiense Bem
Marinobacter nauticus VT8 (Marinobacter aquaeolei VT8)
Lactiplantibacillus plantarum WCFS1
Vibrio campbellii CAIM 519 = NBRC 15631 = ATCC BAA-1116
Limosilactobacillus reuteri JCM 1112
Limosilactobacillus fermentum IFO 3956
Syntrophotalea carbinolica DSM 2380
Lacticaseibacillus paracasei ATCC 334
Lacticaseibacillus casei BL23
Latilactobacillus sakei subsp. sakei 23K
Ligilactobacillus salivarius UCC118
Cereibacter sphaeroides ATCC 17029
Cereibacter sphaeroides ATCC 17025
Brucella anthropi ATCC 49188
Cereibacter sphaeroides 2.4.1
Acetivibrio thermocellus ATCC 27405
Alkalihalobacillus clausii KSM-K16
Alkalihalobacillus halodurans C-125
Dinoroseobacter shibae DFL 12 = DSM 16493

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)

Out:

Sources:
R. wittichii
C. bemidjiense
M. nauticus
L. plantarum
V. campbellii
L. reuteri
L. fermentum
S. carbinolica
L. paracasei
L. casei
L. sakei
L. salivarius
C. sphaeroides
C. sphaeroides
B. anthropi
C. sphaeroides
A. thermocellus
A. clausii
A. halodurans
D. shibae

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)

Out:

Binding sites:
FEEMKEALDLKSKSGVHRLIS
LREIAAQLGISGTLGVMKHLE
RAEIAAELGFRSANAAEEHLR
VREIGEAVDLSSTSTVHGHIS
RAEIARELGFRSANAAEEHLK
VREICSAVGLSSTSTVHGHIS
VREICGAVGLSSTSTVHGHIN
QQEIARAFGFRSLGTVRNYLV
VREIGKSVGLSSSSTVAAYLE
VREIGKSVGLSSSSTVAAYLE
VREICEAVNLSSTSTVHGHLA
VREICNAVGLSSTSTVHGHLS
FDEMKDALDLRSKSGIHRLIT
FDEMKEALDLASKSGIHRLIT
VREICNAVGFKSTSTVHSYLE
VREIGEAVGLASSSTVHGHLA
VREIGEAVGLASSSTVHGHLS
FDEMKDALDLRSKSGIHRLIT
VREIGEAVGLASSSTVHGHLA
FDEMKEALDLRSKSGIHRLIT

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()
# sphinx_gallery_thumbnail_number = 2

plt.show()
lexa conservation

Out:

Consensus sequence:
VREIAEALGLRSTSAVHEHLK

Gallery generated by Sphinx-Gallery