1
SUPPLEMENTARY INFORMATION
Global landscape of cell envelope protein complexes in Escherichia coli
Mohan Babu1,15,16,17, Cedoljub Bundalovic-Torma2,3,15, Charles Calmettes3,4,15, Sadhna Phanse1,5,
Qingzhou Zhang1, Yue Jiang2, Zoran Minic1, Sunyoung Kim1, Jitender Mehla6, Alla Gagarinova8,
Irina Rodionova7, Ashwani Kumar9, Hongbo Guo5, Olga Kagan5, Oxana Pogoutse5, Hiroyuki
Aoki1, Viktor Deineko1, J. Harry Caufield6, Erik Holtzapple7, Zhongge Zhang7, Ake Vastermark7,
Yogee Pandya5, Christine Chieh-lin Lai3, Majida El Bakkouri3, Yogesh Hooda3, Megha Shah3, Dan
Burnside10, Mohsen Hooshyar10, James Vlasblom1, Sessandra V. Rajagopala11, Ashkan Golshani10,
Stefan Wuchty12, Jack Greenblatt5,13, Milton Saier7,16, Peter Uetz6,16, Trevor Moraes3,16,
John Parkinson2,3,13,16, and Andrew Emili5,13,14,16,17
17Correspondence should be addressed to A.E. ([email protected]) or M.B. ([email protected])
SUPPLEMENTARY NOTE 1
Detergent selection for CEP target purification
Our initial strategy was to identify a ‘best’ set of detergents for MP extraction. Consequently, we
systematically evaluated 14 different detergents (ionic, nonionic and zwitterionic) compatible
with MS to define a set of conditions and the maximum concentrations of detergent suitable for
the routine purification for large numbers of E. coli CEPs. These included, for example, dodecyl
maltoside (DDM) and octaethylene glycol monododecyl ether (C12E8), relatively gentle
detergents that likely preserve protein function and protein-protein interactions (PPI)1,2.
To evaluate the effectiveness of these detergents, we compared the total recovery of 11
SPA-tagged CEPs with and without detergent (Supplementary Fig. 1b). We then computed
detergent extraction efficiency by quantifying the relative band signal intensity, reported in terms
of percentile. Three detergents (DDM, C12E8, and Triton X-100) in particular were deemed most
effective and complementary. We note that the amounts of detergents used during extraction and
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purification were chosen based on the critical micelle concentration (CMC) for each detergent,
and that the detergent concentration was never exceeded 1%. Most of the detergents tested are
widely used to isolate CEPs at around 1% concentration3-6, and the literature further supports 1%
non-ionic DDM as ideal for extraction of native CEPs4-6. In all subsequent purification and
washing steps, the buffers used contained 0.1% detergent, or around twice the CMC in all cases.
The table shown below, indicating the actual CMC (mM) of a given detergent compared to the
amount used for purification.
Detergent used
Abbreviation
Detergent CMC
in mM (or in %)
Detergent CMC in mM
used for CE extraction
(% detergent used)
n-dodecyl-β-D
maltopyranoside
DDM
0.17 (0.0087%)
19.5 (1%)
Octaethylene glycol
monododecyl ether
C12E8 0.09 (0.0048%) 18.8 (1%)
Triton X-100 TX-100 0.23(0.015%) 15.3 (1%)
3-[(3-cholamidopropyl)
dimethylammonio]-1-
propanesulfonate
CHAPS
8 (0.49%)
16.3 (1%)
n-Octyl-β-D-gluco
pyranoside
OG
18-20 (0.53%)
34.0 -37.7 (1%)
Lauryldimethylamine-
N-oxide
LDAO
1-2 (0.023%
43.5-87.0 (1%)
n-decyl-β-D-
maltopyranoside
DM
1.8 (0.087%)
20.7 (1%)
Digitonin Digitonin 0.75 (0.08%) 9.36 (1%)
Sodium cholate SC (or Sod.
Cholate)
9.5 (0.41%)
23.2 (1%)
Decanoyl-N-
hydroxyethylglucamide
HEGA-10
7.0 (0.26%)
26.9 (1%)
N,N-dimethyl-1-
tetradecanamine-N-oxide
TDAO
0.29 (0.0075%)
38.6 (1%)
n-nonyl-β-D-thiomaltoside NTM 6 (0.28%) 21.4 (1%)
3-[(3-Cholamidopropyl)
dimethylammonio]-2-
hydroxy-1-propane
sulfonate
CHAPSO
8 (0.50%)
16.0 (1%)
n-dodecyl-β-D-thiomalto
pyranoside
LTM
0.05 (0.0026%)
19.2 (1%)
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We then investigated how well three chosen (Triton, DDM, C12E8) detergents performed in
complete large-scale purifications of a broader set of 39 SPA-tagged E. coli CEPs with diverse
molecular weights, abundance or number of transmembrane helices. We were able to identify
both the bait and putative binding partners in about half the analyses, indicating that many
bacterial CEP complexes can be affinity-purified in the presence of at least one of these
detergents (data not shown). Based on the results from these preliminary proteomics screens, we
selected DDM, C12E8 and Triton X-100 for large-scale affinity purifications.
AP/MS methods
For purifications, the C-terminally SPA-tagged E. coli CE strains created with a kanamycin
selectable marker integrated by targeted homologous recombination in the E. coli chromosome
were grown to exponential (mid-log) phase in 1 L of Luria-Bertani (LB) media using shake batch
culture flasks. After mechanical lysis of harvested cells by sonication and pelleting of debris, the
lysates were ultra-centrifuged to isolate the membranes. The membrane fractions were washed
and stably-associated CEPs extracted with buffers containing various non-denaturing detergents,
with the remaining insoluble material was removed by a second round of ultra-centrifugation.
The solubilized CEP baits, and their stably associated proteins, were then purified successively
on anti-FLAG and calmodulin columns, as is usual for the SPA method7,8, except that all but the
first three column-washing steps included calmodulin wash buffer (30 mM Tris-HCl pH 7.9, 150
mM NaCl, 2 mM CaCl2, and 0.1-0.5 mM TCEP) in the presence of 0.1% detergent, followed by
the last two column-washing steps performed in the absence of detergent. Buffers used in each
step of the procedure are described in detail in our earlier publications7,8, including a video file
illustrating these procedures available via the Journal of Visualized Experiments8.
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The purified protein preparations extracted using a PerfectFOCUSTM kit (G-Biosciences)
to remove residual detergent were resuspended in 25μl of 8 M urea (1.5 M final concentration),
20 mM HEPES (pH 8) and then reduced with 5 mM Tris (2-carboxyethyl) phosphine for 45 min
at room temperature and alkylated with iodoacetamide (15 mM) for 60 min in the dark. Sequence
grade modified trypsin (0.3 mg; Promega) was added and samples were digested overnight with
gentle shaking at room temperature. The peptide mixtures were then acidified with formic acid
and desalted using C-18 TopTips (Glygen). The resulting peptides were loaded using an
autosampler onto a 75-μm inner diameter microcapillary fused silica column packed with ~10
cm of reverse phase resin (C18, 3 μm, 100 Å; Phenomenex) placed in-line with a Proxeon EASY
nLC 1000 (Proxeon) nano high-performance liquid chromatography and the Orbitrap Velos or
Elite mass spectrometer (Thermo Fisher Scientific). Bound peptides were eluted by electrospray
ionization using a 100 min water/acetonitrile gradient with a stable tip flow rate of ~0.30 µl min-
1. Precursor ions [350-2000 m/z] were subjected to data-dependent, collision-induced
dissociation while the mass spectrometer cycled through one full mass scan followed by 15
successive tandem mass scans of the intense precursor ions with dynamic exclusion enabled.
Prior to generating the high-quality interactions using the integrated log-likelihood score
threshold (Σ LLS; see below), we mapped the MS/MS spectra from each SPA-tagged bait
purification to reference E. coli protein sequences using both the SEQUEST (ver. 27 - rev.9)7,9,10
and MS-GF+11 algorithms. Notably, we opted to use the same proven methodology (e.g. search
engine) applied in our previous E. coli soluble interactome studies9,12, allowing for comparison
and integration of the data to address additional and unique research questions. Like Mascot and
X-Tandem, SEQUEST is still one of the most commonly used search engines, but we note that
we also employed a newer alternative, MS-GF+, for peptide identifications in a parallel
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analysis11. As in our previous study9, precursor mass tolerance was set to 3 Da (daughter mass
ion tolerance set to the default of 0), while enabling partial tryptic enzyme and single site missed
cleavages. The STATQUEST filtering algorithm13 was then applied to all putative SEQUEST
search results to assign statistical confidence. High confidence (>90% likelihood) spectral counts
from SEQUEST/STATQUEST were combined and averaged with the MS-GF+ search results. If
the spectral count was not detected by MS-GF+, the interaction was still considered as long as it
passed a 90% confidence threshold by SEQUEST/
STATQUEST. The default parameters used in SEQUEST/STATQUEST and MS-GF+ searches
are listed in the tables below:
SEQUEST (ver. 27 - rev.9) Parameters Comments
Database EcoCyc (ver.14.1) Target and decoy sequences
Precursor mass tolerance 3.0 Default SEQUEST parameter
Fragment ion tolerance 0 Default SEQUEST parameter
Missed cleavage 1 Default SEQUEST parameter
Fixed modification + 57 Cysteine carbamido-methylation Default SEQUEST parameter
Variable modification +16 methionine oxidation Default SEQUEST parameter
Peptide-Protein identification FDR 0.01 (~ 99%) Default SEQUEST parameter
Protein identifications (hits)
filtered
20 ppm Remove false positives
STATQUEST Parameters Comments
Assign confidence score p-value 0.01 (~ 99%) Confidence score ranges
between 99.6% to 50%
MS-GF+ (ver. 9949) Parameters Comments
Database EcoCyc (ver.14.1) Target and decoy sequences
ParentMassTolerance -t 20ppm Default MS-GF+ parameter
IsotopeErrorRange -ti
-1, 2 The combination of -t and -ti
determines the precursor
mass tolerance
Number of tolerable (tryptic)
termini –ntt
2 This parameter is used to
apply the enzyme cleavage
specificity rule when
searching the database
Fixed modification + 57 Cysteine carbamidomethylation Default MS-GF+ parameter
Variable modification +16 methionine oxidation Default MS-GF+ parameter
Q-value FDR 0.01 (~ 99%) Default MS-GF+ parameter
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To boost coverage, we employed several AP/MS strategies. From inception of this study,
we purified bait CEPs using a two-step (anti-FLAG and calmodulin bead capture) affinity
purification protocol. For failed baits, on a case-by-case basis, we attempted a more efficient
one-step affinity purification using anti-FLAG M2 agarose beads or with dynabeads immobilized
antibody (Life Technologies), which occasionally worked better. The antibody-coupling to
dynabeads pulldown strategy has been shown to exhibit surface activated chemistry with
hydrophobic characteristics and with ultra-low background binding.
Briefly, after the cells were cultured and lysed using the procedure described in our SPA
method7, the supernatant was coupled with 30 ul dynabeads slurry [i.e. 200 ul beads and 3ul
FLAG antibody incubated for 10 min at room temperature, and beads washed twice with 200 ul
of purification buffer (30 mM Tris-Hcl pH 7.9, 150 mM NaCl, 0.02% Tween 20, 0.1-0.5 mM
TCEP)] and incubated for 30 min at 4 ºC following the manufacturer instructions with slight
modifications. The resulting supernatant was discarded, and the beads were washed three times
with 200 ul purification buffer. Proteins that bound to the beads were subsequently eluted with
50 ul elution buffer (10% ammonium hydroxide, 0.1-0.5 mM TCEP) after incubating at room
temperature for 15 min. The purified protein preparations were finally subjected to detergent
removal, trypsin digestion, and protein identification by MS as aforementioned above.
In total, we purified 590 bait CEPs (of 785 tagged) using only one method, while 195 were
purified using two methods (see Table below). In general, we found that both single- (anti-
FLAG) and dual-step (anti-FLAG + calmodulin) purifications recovered >85% of the bait CEPs
from MS, while the dynabeads purifications were slightly less effective (~60% bait recovery). As
noted in the table below, we were able to detect 582 baits by MS using various purification
methods. However, for generating the cePPI network, we took all 785 CEPs into consideration
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for analysis because even though certain baits were not detected by MS, we were nevertheless
able to identify PPI pairs in some cases that were either reported in the literature or had known
functional associations (e.g. encoded in same operon).
Purification
methods
Number of CEPs
purified as baits
Number of CEPs
recovered*
Successful
purification (%)
Two-step (FLAG-Calmodulin-
binding peptides)
277
247
89.16
One-step (FLAG) 80 71 88.75
Dynabeads antibody coupling 233 138 59.22
Two-step and one-step 3 3 100.00
Two-step and dynabeads
strategy
180 114 63.33
One-step and dynabeads strategy 12 9 75.00
All three methods (Two-step,
one-step, and dynabeads)
0 0 0
Total 785 582
* Numbers indicate bait CEP detection by MS.
Scoring procedures to identify reliable PPIs and define CEP complexes
As with any other high-throughput AP/MS study, detection of promiscuous non-specific
interactors is an inherent concern. We mitigated this in three ways. First, proteins identified in
mock AP/MS analyses (standard anti-FLAG and calmodulin affinity workflow) of an untagged
(WT) negative control strain (without a tagged bait) were removed from further consideration.
Second, proteins detected routinely with 80% or more of bait were deemed contaminants
(frequent flyers). Third, the PPI data from each bait CEP was filtered using a probability score of
90% or greater as the majority of known interactions curated in EcoCyc passed this threshold
(Supplementary Fig. 3a).
As for the number of biological repeats and variance between repeats (i.e. 466 CEPs
purified in 3 detergents, 39 in 2 detergents, and 280 only using one detergent; Supplementary
Fig. 2a), we evaluated the results obtained for 290 SPA-tagged CEP preparations in different
non-ionic detergents, with at least 2 biological replicates per detergent, for a total of 1,751
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AP samples analyzed by precision Orbitrap MS. We computed the variance and average Pearson
correlation of proteins co-purifying (spectral counts) between each replicate per detergent.
The filtered interactions were then subjected to HyperGeometric Spectral Counts score
(HGSCore)14 and Comparative Proteomic Analysis Software Suite (CompPASS or S-score)15
scoring algorithms to define high-quality associations. The HGS incorporates the spectral counts
into Hart’s hypergeometric distribution error model to compute the probability of an interaction
being observed at random16. The HGSCore algorithm assumes a matrix model of interactions,
inferring potential prey-prey links from the bait-prey data captured in the experiments. First, the
normalized spectral abundance factor (NSAF) of the prey protein for each AP experiment is
calculated as follows:
NSAF = SPCk / (Lk . Σ (SPC/L))
For a prey protein, k, its NSAF is determined from the number of spectral counts (SPC)
associated with protein k divided by its length (L), divided by the sum of SPC/L for all N
proteins in the experiment17.
𝑁𝑆𝐴𝐹𝑘 = (𝑆𝑃𝐶/𝐿)𝑘
∑ (𝑆𝑃𝐶/𝐿)𝑖𝑁𝑖=1
NSAF values are then normalized (by dividing all scores by the lowest NSAF score) and
converted to a TN score. For any two putatively interacting proteins, i and j, we take the smaller
of the two TN scores as a measure of the frequency of co-occurrence and use this value to
calculate the corresponding hypergeometric probability of interaction between the two proteins
using the following formula:
𝑃 (∑ min(𝑇𝑁) > 𝑘|𝑛, 𝑚, 𝑁) = ∑ 𝑃ℎ𝑦𝑔𝑒𝑜(𝑥|𝑛, 𝑚, 𝑁)
min(𝑛,𝑚)
𝑥=𝑘
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𝑃ℎ𝑦𝑔𝑒𝑜(𝑥|𝑛, 𝑚, 𝑁) = (𝑛
𝑥) (𝑁−𝑛𝑚−𝑥)
(𝑁𝑚)
Where
k = ∑ min (TN) for experiments with TN;i > 0 and TN;j > 0
n = ∑ min (TN) for experiments with TN;i > 0
m = ∑ min (TN) for experiments with TN;j > 0
N = ∑ min (TN) for experiments
The final HGSCore among all potential prey-prey interactions are calculated as follows:
𝐻𝐺𝑆𝐶𝑜𝑟𝑒𝑖.𝑗 = − log (𝑃ℎ𝑦𝑔𝑒𝑜;𝑖.𝑗)
In the case of CompPASS, the S-score assumes a spoke model of interactions, where each
column is indicated by a prey and each row to a bait. If a bait-prey interaction is observed over
multiple experiments, their average SPC is used to calculate the S-score using the following
formula:
𝑆𝑖,𝑗 = √(𝑘
∑ 𝑓𝑖,𝑗𝑖=𝑘𝑖=1
) 𝑥𝑖,𝑗; 𝑓𝑖,𝑗 = {1; 𝑥𝑖,𝑗>0
Where k is the total number of baits in the dataset, xi,j is the average SPC for bait i with prey j, fi,j
is the frequency that prey j interacts with the set of baits.
After computing the HGSCore and S-score, we employed a log likelihood scoring scheme
(LLS) to integrate the HGSCore and S-score into a single combined score. The LLS represents
the likelihood that the interaction is genuine and is calculated as previously described18.
LLS = In 𝑃 (𝐿|𝐸)/ ~ 𝑃 (𝐿|𝐸)
𝑃 (𝐿)/ ~ 𝑃 (𝐿)
Where P(L|E) represent the frequency of interactions (L) in dataset (E), which contains true
positive (TP) interactions from the literature curated EcoCyc complexes (i.e. 1,249 PPIs in total;
Supplementary Table 2). ~P(L|E) represents the frequency of L in a set of true negative (TN)
interactions (defined as those that occur between two proteins that belong to different
complexes). P(L)/~P(L) represents the prior odds ratio of TPs and TNs.
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In order to generate the LLS score, we used the aforementioned scoring procedures to our
interaction dataset, which resulted in 1,348,793 PPI pairs with HGSCore > 0 and 140,805 pairs
with S score > 0. For each pairwise interaction within a score set, we calculated the LLS
independently, and used a weighted sum to produce a final score:
S = ∑𝐿𝐿𝑆𝑖
𝐷(𝑖−1)
n
i=1
Where LLSi represents the LLS of data set i, D is a free parameter representing the relative
degree of dependency between various datasets, and n is the number of interactions after
resampling. Here, we tested a series of D value from 0 to ∞ and found D = 1 gave the best
performance for both coverage and accuracy. Based on the area-under-the ROC (receiver
operating characteristic) curve analysis and a set of 1,249 cePPIs for E. coli proteins compiled
from EcoCyc (Supplementary Table 2), we chose a Σ LLS cut-off ≥ 5.27, resulting in 12,801
high-confidence associations (Supplementary Table 2). Since a quarter (28%, 351) of all
literature curated PPIs from the EcoCyc training set is involved in flagellar process, we repeated
the area-under-the ROC performance by eliminating these associations, and found that the ROC
performance was comparable with the inclusive training set, resulting in a score threshold of
Σ LLS ≥ 5.26 (data not shown) compared to 5.27 selected for study.
As an independent test of reliability, during our initial scoring optimization, we tested the
two algorithms (HGSCore, CompPASS) to see how well they perform in capturing known
EcoCyc complexes. By recapitulating the reference cePPIs according to area-under-the ROC
analysis, we were able to define stringent score thresholds for each (6.58 for CompPASS, 5.13
for HGSCore). Consistent with the notion that these scoring metrics work differently in capturing
interactions19, at the selected cut-off, we found cePPIs of certain literature curated complexes,
such as the β-barrel assembly machine (BAM; see table inset below), were preferentially
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detected or penalized by either algorithm, despite the fact that these PPIs were clearly evident in
our raw data. Hence, by integrating the output from CompPASS and HGSCore into a single
probabilistic log-likelihood score (LLS), we maximized coverage and reliability.
Bait
protein
Prey
protein
CompPASS score
(cut-off at 6.58)
HGScore
(cut-off at 5.13) ∑ LLS score
(cut-off at 5.27)
BamA BamB
BamD
5.15E-14
9.60E-30
10.4
13.5
8.71
8.90
BamB BamD 1.93E-31 5.64 7.87
BamD BamE 7.20E-39 5.51 7.81
Notably, the purpose of using two scoring algorithms is to: (1) capture as many
biologically relevant complexes as possible; (2) reliably predict HC or MC PPIs; and (3)
maximize coverage and accuracy in benchmarking against an established reference set of curated
cePPIs derived from EcoCyc. It is worth noting that the majority (3,841) of the “prey-prey”
associations involve one or more CEPs in our reported network, but only 3% (131) were deemed
HC, while the remaining (3,710) were assigned to the MC category. Whereas only a few (<1%,
or 104) cytoplasmic (prey-prey) protein pairs had no connection to CEPs, we gained meaningful
functional insights by confirming their physiological relevance as detailed in three mechanistic
follow-up studies.
First, we provided independent evidence that the cytoplasmic phosphocarrier protein, HPr
(an essential component of the bacterial phosphotransferase system) allosterically regulates
cytoplasmic phosphofructokinase (PfkB, but not PfkA), glucosamine 6-phosphate deaminase
(NagB), and adenylate kinase (Adk), suggesting that HPr serves as a global regulator of carbon
and energy metabolism and probably other physiological processes in enteric bacteria
(Rodionova et al., 201720).
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Second, we provide further evidence that the cytoplasmic nitrogen regulatory PII protein,
GlnB, and N-acetyl-glucosamine 6-phosphate epimerase, NanE, allosterically activate
glucosamine 6-phosphate deaminase (NagB) in E. coli (Rodionova et al., Journal of
Bacteriology; Manuscript Submitted).
Third, in the current study, we have shown by steady-state kinetics (Supplementary Fig.
7a) that the cytoplasmic protein, PykF is activated upon binding to non-phosphorylated HPr,
suggesting that, like PykA of Vibrio vulnificus21, HPr regulates PykF by increasing its affinity for
phosphoenolpyruvate.
In fact, we estimate that the rate of spurious PPIs in our network is likely less than 3% (as
derived from the estimated true/false positive ratio of 33/1 based on the benchmark precision vs.
the EcoCyc reference set reported in Supplementary Table 2). This represents a putative total
of approximately 371 spurious interactions, which is a level of precision comparable to that
reported in a previous AP/MS survey of membrane protein complexes in yeast (Babu et al.,
Nature 2012, 489, 585-9). The 12,801 PPIs were then clustered using core-attachment-based
clustering algorithm, as previously described22, to generate 540 clusters (420 multiprotein
complexes with at least one CEP; 120 with cytosolic proteins; Supplementary Table 4). While
all proteins in a predicted cluster are expected to be part of the same complex, the core-
attachment algorithm employed in our study predicts clusters with “cores” and then adds
“attachment” onto the cores to form biologically meaningful structures23. However, without
structural information (i.e. crystallographic data) or detailed experimental follow-up for each
subunit of a predicted complex, it is practically impossible (solely based on their degree of PPI
connectivity) to suggest whether the proteins form discrete sub-assemblies.
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Biochemical fractionation coupled to MS (BF/MS)
A global interaction mapping approach was conducted to validate the original 12,801 putative
PPIs based on the chromatographic separation of detergent solubilized macromolecules extracted
from E. coli using 0.020% Triton or 0.05% DDM (below the CMC for each detergent).
(i) Isolation and preparation of membrane (or CE) extracts
Unless otherwise stated, all steps were performed at 4oC. E. coli DY330 cells collected from 1 L
exponential (mid-log) phase culture was centrifuged at 10,000 x g for 15 min and washed with
25 ml of 50 mM Tris HCl (pH 7.5). The pellet was resuspended in 12 ml of 50 mM Tris HCl (pH
7.5) with Benzonase (Sigma) added to the buffer. The cells were disrupted with a sonicator for
15 to 20-s pulses with ~1 min waiting time between pulses. After lysing, cells were centrifuged
at 1,500 x g for 15 min at 4 oC. The membranes were purified by layering the lysate onto two
discontinuous sucrose gradients (6 ml of lysate/gradient), comprising 8 ml of 2.02 M sucrose in
50 mM Tris HCl (pH 7.5) overlaid with 10 ml of 0.44 M sucrose in 50 mM Tris HCl (pH 7.5).
The gradients are then centrifuged in a SW 60 Ti rotor at 60,000 rpm (255,000 x g) for a
total of 75 min in a Beckman L8-M Ultracentrifuge. The middle (membrane) layer is collected
after carefully removing the top layer with a pipette. The membrane is diluted with 4 volumes of
50 mM Tris HCl (pH 7.5) buffer and pelleted by centrifugation at 255,000 x g for 3 h. The
membrane pellets were resuspended in 1 ml of Tris HCl (pH 7.5) and forced through hypodermic
needles (23 gauge). The resulting membrane solution was frozen with liquid nitrogen and stored
at -80 oC until use. After thawing, the E. coli membrane was solubilized in 0.5 mL ice-cold
buffer [20 mM Tris-HCl (pH 7.5), 5% glycerol, 0.020 % Triton X-100 or 0.05% DDM,
phosphatase inhibitor cocktail 2 (Sigma), phosphatase inhibitor cocktail 3 (Sigma), and protease
inhibitors] and ground using mortar and pestle for 10 min. The suspension was centrifuged for
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10 min at 15, 000 x g, and protein concentration for the supernatant collected was determined
using Bradford assay.
(ii) Size exclusion chromatography (SEC)
The chromatographic fractionation of proteins was performed using an Agilent 1100 semi-
preparative high-performance liquid chromatography (HPLC) equipped with a binary pump
system (Agilent Technologies). Protein elution was monitored by absorption at 280 nm. A total
of 200 µg of membrane (or CE) extract was subjected to a 300 × 7.8 mm BioSep4000 Column
(Phenomenex), pre-calibrated with the following markers of known molecular mass: blue
dextran (2000 kDa), bovine serum albumin (67 kDa), ovalbumin (43 kDa), chymotrypsin
(25 kDa) and ribonuclease (13.7 kDa). Equilibration and elution was performed with 50 mM
Tris-HCl (pH 7.5), 50 mM NaCl, 0.015% Triton X-100 or 0.025% DDM, and
1% glycerol. About 0.1 mL of the protein fractions was collected at a flow rate of 0.5 mL min−1.
The extracts were fractionated into 84 biochemical fractions using SEC, fractionated
complexes were proteolytically digested, and the resulting peptides analyzed, in duplicate, by
precision tandem MS. Fragmentation spectra was collected on an Orbitrap mass spectrometer
from a total of 336 samples (i.e. 84 fractions x 2 detergents x 2 replicates) and were searched
against an E. coli target-decoy sequence database using the SEQUEST algorithm, and the
identifications filtered to a minimum of 90% probability matching score using STATQUEST7.
As in our previous studies of eukaryotic protein assemblies24,25, we determined the patterns of
co-eluting together across the 336 samples analyzed to derive stably-associated assemblies using
interaction likelihood metrics as previously reported24,25.
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(iii) Generation of cePPIs from BF/MS using machine learning approaches
To objectively score the co-fractionation profiles, we used the protein spectral counts
consistently detected in both replicates of each of the two detergent solubilized extracts to
compute interaction likelihood metrics as previously reported24,25. These include: (1) Pearson
correlation coefficient (PCC), with added Poisson noise to reduce the influence of low-count
proteins; (2) weighted cross-correlation (WCC); and (3) co-apex score, which we then compared
to a reference set of curated cePPIs derived from EcoCyc (Supplementary Fig. 4a and
Supplementary Table 2). A threshold cut-off was subsequently computed for each score based
on the capture of annotated PPIs (area-under-curve ROC analysis; Supplementary Fig. 4c). This
resulted in 128,919 unique PPI pairs (Supplementary Fig. 4a), of which, we validated 1,678
candidate protein pairs with either a co-apex score of 1 or a PCC or WCC measure above the
threshold that had likewise also been detected by AP/MS originally (Supplementary Table 2).
Selection of paralogous CEPs
Proteins that have undergone paralogous expansion in the cePPI network were identified using
the integration of complementary orthology prediction databases, OMA (Orthologous Matrix)26
and EggNOG27. Briefly, predicted orthology groups were downloaded from the current releases
of OMA (ver. 17) and EggNOG (ver. 4) and orthology group memberships for interaction
networks of CEPs were retrieved. Paralogous genes that are duplicated and diverged following a
speciation event were identified based on membership in both the same EggNOG and OMA
orthology groups. Given that EggNOG provides orthologous group predictions at different
taxonomic resolutions, the degree of overlap was compared between OMA and three EggNOG
(bactNOG, proNOG and gproNOG) orthology groups. The proNOG had the greatest degree of
overlap with OMA ortholog group and hence was used for subsequent analysis.
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Paralog PPI overlap
Physical interaction subnetworks for each identified paralog was extracted from the finalized CE
network and used to construct a set of paralog PPI profiles. The proportion of physical
interactions shared between paralogous CE protein pairs was calculated using the Jaccard Index
of their respective PPI profiles, where, for proteins A and B, the Jaccard Index of their physical
interaction profiles, JPPI-AB, is calculated by:
JPPI-AB = OverlapPPI-AB / (OverlapPPI-AB + UniquePPI-A + UniquePPI-B)
Individual paralog PPI subnetworks extracted from the CEP network were imported and
visualized in Cytoscape (ver. 3.2.0).
External sources used in this study
Data type Short description Pathways and complexes EcoCyc pathways and complexes
(http://bioinformatics.ai.sri.com/ecocyc/dist/flatfiles-52983746/)
Experimental/Protein
information Protein expression (PaxDB database28)
Experimental PPI network derived from AP/MS datasets9,29
Experimental Yeast two-hybrid (Y2H) dataset12
Experimental Phenotypic dataset30
Subcellular localization
EcoCyc, Díaz-Mejía et al (2009)2, EchoLocation31, GO, UniProt,
and STEPdb32,
Signal peptide prediction SignalP (http://www.cbs.dtu.dk/services/SignalP/)
TMH prediction for IMPs Phobius33
ß-barrel prediction for OMPs BOCTOPUS234
Genetic interaction network CE35, genome-integrity36, and genome-wide screens37
PPI network PPIs from IntAct database38
Protein information GO annotations
Orthology information InParanoid39, OMA26 and EggNOG27
Protein information UniProt
Validation strategy to test PPIs
We randomly selected 103 PPIs above the Σ LLS scores (≥ 5.27), involving CEPs for validation
using the orthogonal bacterial (B2H) and yeast (Y2H) two-hybrid assays. Of those tested, we
were able to confirm 44 interactions by B2H and/or Y2H screens, resulting in the confirmation
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of 43% (44 of 103 tested) of the physical associations. Assays were conducted using the
following procedures:
(i) B2H assay
(a) Gateway cloning
The recombination reactions were performed according to the manufacturer’s guidelines
(Invitrogen). The entry clones for all protein (baits and prey) pairs tested were obtained from the
E. coli ORFeome clones assembled into the pDONR221 vector system. The ORFeome library
was constructed and sequence-verified essentially as previously described40. The attL-flanked
ORFs were then recombined into the attR-flanked bacterial two-hybrid (BACTH)-DEST
plasmids (pST25-DEST and pUT18C-DEST) using the LR reaction to generate an attB-flanked
ORFs within the BACTH vectors. The LR recombination reaction was performed according to
the Gateway recombination manual to generate B2H expression clones (pST25-DEST, pSTM25-
DEST, pUT18C-DEST, and pUTM18-DEST). All extra-cytoplasmic and IMPs were screened in
pST25-DEST and pUT18C-DEST, whereas the OM or MR proteins were transferred into
pSTM25-DEST or pUTM18-DEST B2H modified expression vectors containing an extra
transmembrane segment fused downstream to T25 or T18 cyclase domains41.
(b) BACTH screening
The B2H expression clones for both bait and prey were co-transformed into an adenylate cyclase
(cya) deficient E. coli strain (BTH101). The BTH101 competent cells was prepared using the
standard protocol. The co-transformed cells were plated on LB plates containing 100 ug/ml
ampicillin and 100 ug/ml spectinomycin. Plates were incubated at 30°C for 48 h. The co-
transformants were selected and screened onto two different indicator plates (i.e. LB-X-Gal-
IPTG and McConkey/Maltose medium) at 30°C for 24 to 36 h. The PPIs were quantified using
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the ß-galactosidase assay, with the same batch of cells used in all screening experiments. The ß-
galactosidase activity was measured from ~3 biological replicate experiments, and the miller
units is represented as mean log10 after subtracting the mean value from the negative control.
The quantification results of each PPI pair from replicate experiments, along with
Student’s t-test p-values, are shown in Supplementary Table 3. Most notably, we only
considered PPI pairs with a p-value ≤ 0.05 and a 2 to 5 fold difference in miller units compared
to negative control as a reliably confirmed interaction. In some cases, higher standard deviations
were observed for certain PPI pairs and this is likely due to the instability of the plasmid
construct used, consistent with studies reported previously for analyzing cePPIs by B2H42,43.
(ii) Y2H assay
Y2H screens were performed as previously described44. Briefly, the baits (in pGBGT7g) and
prey (in pGADT7g) arrays for test interaction sets were created. To identify self-activation, the
baits were mated with yeast carrying empty prey vectors (pGADT7g). In parallel, we also
conducted our bait vs prey Y2H screens. Here, each bait (DBD-X) was mated with specific prey
(AD-Y) on YPDA for 36 to 48 h at 30°C, followed by the selection of diploid cells onto selective
-Leu-Trp agar plates for 2 to 3 days. The diploids were then screened for interacting proteins on
selective medium (-Leu -Trp -His) at 30°C for another 6 to 7 days. As reported in our previous
yeast two-hybrid studies12,45,46, we used the minimal 3-AT concentration strategy by screening
all the baits for auto-activation by titrating the 3-AT concentration on which the baits do not
auto-activate. To suppress non-specific background or self-activation from baits, -Leu -Trp -His
plates containing 3-AT were used for screening. The plates were monitored each day, and
positive colonies were evaluated with respect to the background growth, and only positives with
high signal-to-noise ratio were used.
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Steady-state kinetic analysis
(i) Protein purification
Recombinant proteins containing an N-terminal His6 tag were overexpressed in E. coli and
purified using Ni2+-chelating chromatography. The E. coli overexpression strains, PykF and PtsH
(Hpr) from the ASKA collection47 were grown in 50 ml Luria-Bertani (LB) medium, induced by
0.6 mM isopropyl-β-d-thiogalactopyranoside (IPTG), and harvested after 4 h of shaking. Rapid
purification of the recombinant proteins on a Ni-nitrilotriacetic acid (NTA) agarose minicolumn
was performed as described previously48.
Briefly, cells were harvested and resuspended in 20 mM HEPES buffer (pH 7.0),
containing 100 mM NaCl, 2 mM β-mercaptoethanol, and 0.03% Tween 20 with 2 mM
phenylmethyl sulfonyl fluoride. Cells were lysed by incubation with lysozyme (1 mg/ml) for 30
min, followed by a freeze-thaw cycle and sonication. After centrifugation, Tris-HCl (pH 8.0)
buffer was added to the supernatant to a final concentration of 50 mM. The supernatant was then
loaded onto a Ni-NTA agarose minicolumn (0.2 ml) from Qiagen Inc. (Valencia, CA). After
bound proteins were washed with At-buffer containing 50 mM Tris-HCl buffer (pH 8.0), 0.5 M
NaCl, 5 mM Imidazole and 0.3% Tween 20, they were eluted with 0.3 ml of the same buffer
supplemented with 250 mM imidazole. Protein size, expression level, distribution between
soluble and insoluble forms, and the extent of purification were monitored by SDS-PAGE.
Since except HPr, PykF was obtained with high yield (>1 mg) and purity (80 to 90%), we
modified the protocol for Hpr. The E. coli overexpression strain for HPr protein purification was
as follows: Cells were grown in 2L LB medium at 37 oC, induced by 0.6 mM IPTG at 24 oC and
harvested after 12 hrs of shaking. After harvesting, the cells were resuspended in the same buffer
as described above, and after lysis by sonication followed by centrifugation, the insoluble
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fraction was resuspended in At-buffer containing 7M urea and 25 mM imidazole. Inclusion
bodies were dissolved, and after sonication and centrifugation, the protein was purified on an
NTA agarose column. Then the bound proteins were washed with 7M urea At-buffer, and HPr
was refolded by washing the column with At-buffer before eluting with the same buffer
containing 300 mM imidazole on an FPLC system. The buffer was changed to At-buffer by
dialysis. Protein concentration was measured using the Bradford assay.
(ii) Enzyme assays
Activity of the purified recombinant E. coli PykF was routinely assayed in a cuvette at 37 °C
using the standard enzymatic coupling assays as described previously49. To determine the effect
of HPr on PykF activity, 1 µM phosphorylated (HPr-P) and non-phosphorylated (HPr) forms of
HPr was added to the assay mixture. HPr was phosphorylated in the assay mixture containing
100mM Tris (pH 8), 2mM DTT, 8 mM PEP (phosphoenol-pyruvate), 10 mM MgCl2, and 10 ng
enzyme I (EI) and incubated for 40 min at 30oC. Then 1 µM of P-HPr protein, 10 ng of EI, and 8
mM PEP were added to the assay mixture. The observed rates (calculated using an NADH
extinction coefficient of 6.22 mM−1 cm−1) were compared to those for the two sets of control
samples: one control without the tested enzyme and another without adenosine monophosphate
(AMP) or PEP. The Km and Vmax values were determined by the GraphPad Prism software.
Pyruvate kinase activity was tested similarly using a coupled assay with lactate dehydrogenase
(LDH). Pyruvate kinase (25 ng) was added to 100 μl of a reaction mixture containing 200 mM
Tris-HCl (pH 7.5), 10 mM MgSO4, 1.5 mM ADP, 0-8 mM PEP, 0.3 mM NADH, 0.2M KCl, 100
µM ZnSO4, 15mM phosphate, and 1.2 U of LDH.
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SUPPLEMENTARY NOTE 2
Evidences supporting cell envelope protein (CEP) interactions captured in the network
(i) Periplasmic solute binding receptors of ABC transport systems interacting with each other
ABC transport systems usually consist of two and five subunits: two cytoplasmically localized
ATPases, which can be present as a homodimer or a heterodimer; two integral membrane
subunits, which similarly may be present as a homodimer or a heterodimer; and, for uptake
systems, but not for efflux systems, a periplasmic solute binding receptor is usually, but not
always, present50-52. The ATPase energizes the transport process, while the receptor feeds the
substrate into the integral membrane channel complex. Binding of the substrate-occupied
receptor transmits a signal to the ATPase, which increases its ATPase activity. Usually, the
periplasmic receptor is synthesized in much larger amounts than the membrane constituent(s) or
the ATPase(s). However, in several characterized systems, the receptor only gains high affinity
for the integral membrane constituents when the substrate is bound to it.
With this background, we examined the interactions observed for the periplasmic solute
binding proteins of the various ABC transport systems in E. coli (Supplementary Table 3). The
first two entries in Supplementary Table 3 involve the interactions of different binding
receptors that function in the uptake of sulfur-containing compounds, for example, the
interaction of CysP with the alkanesulfonate receptor, SsuA (Σ LLS score 12.9). While this
system is involved in the uptake of sulfur-containing compounds, the transport systems to which
they belong are constituents of different families within the ABC superfamily. The second
interaction involving CysP was with the sulphate binding protein, Sbp (Σ LLS score 11.8).
Most notably, the ABC sulphate/thiosulphate uptake porter includes the transporter
CysTWAP complex, where CysP is the periplasmic thiosulphate binding receptor53. A second
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binding receptor, Sbp, specific for sulphate, can function with this transporter. We also found
that CysP binds with the two subunits, SsuD and SsuE (Supplementary Table 2), of the
alkanesulfonate monooxygenase (Σ LLS scores 13.5, respectively). It is known that the two
protein constituents of this enzyme system interact to form the heterodimer54, but an interaction
between the monooxygenase subunits and the periplasmic binding protein had not been
documented. Since CysP normally resides in the periplasm (PE) while SsuDE resides in the
cytoplasm (CY), it is difficult to understand how these proteins might interact. It is possible that
CysP is not restricted to the PE and is initially synthesized in the CY before being exported via
the general secretory pathway. In some instances, some of the secreted proteins remain in the
CY, and this could be true for CysP, explaining the observed interaction.
A similar situation was observed for the two arginine binding proteins, ArtI and ArtJ,
which function with ArtPQM transport system. In contrast, we also observed receptors binding
to two different transport systems within the same ABC superfamily. For example, like methyl-
galactosidase (MglB) and D-ribose transporter (RbsB) subunit association, the interaction was
observed between two distinct amino acid systems: FliY (TcyJ), the cysteine/diaminopimelate
receptor, and the glutamine receptor, GlnH, with a score of 3.7 (below the chosen threshold).
Lastly, the ZnuA zinc receptor interacted with the AraF L-arabinose receptor with a score
of 13.9 (Supplementary Fig. 7d). As well, the ZnuA zinc receptor proved to interact with the
periplasmic ZinT (YodA) protein with a score of 6.6. ZinT and ZnuA are believed to feed into
the same ABC transport system, ZnuBC. Recent studies indicate that these two proteins act in
series rather than in parallel, and that both are required for maximal rates of Zn2+ uptake55-57.
Since small amounts of ZinT are found extracellularly, although most is periplasmic, it has been
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suggested that ZinT serves a scavenging function, feeding zinc into the ZnuA periplasmic
binding receptor58. This is the first report to suggest that these two proteins interact.
(ii) Physiologically important CE protein-protein interactions (cePPIs) in E. coli
Many of the potential metabolons identified in this study are described in Supplementary Table
3. Considering the phosphotransferase system (PTS) first, we see that the three constituents of
the N, N’-diacetylchitobiose-specific enzyme II complex, IIA (ChbA), IIB (ChbB) and IIC
(ChbC), interact, and the IIC constituent is also associated with the NAD (P)-binding phospho-β-
glucosidase (ChbF). This observation suggests that the disaccharide transport, phosphorylation
and hydrolysis appear to be catalyzed by a single physical complex (a metabolon) involving all
of the sugar-specific constituents of this system59.
Another potential metabolon involves the D-alanine-D-alanine dipeptide transport system,
in which the periplasmic receptor, DdpA, interacts with the peptidase, YegQ, of the U32
peptidase super family60. The iron enterobactin transport protein FepG61 (TCDB search; TC#
3.A.1.14.2) appears to interact with the enterobactin biosynthetic enzyme, EntA62. The alkane
sulfonate ABC transporter, SsuA, interact with both subunits of the heterodimeric alkane
sulfonate monooxygenase, SsuDE63. Moreover, as aforesaid, the periplasmic CysP sulphate
receptor (TC# 3.A.1.6.1) was linked with both subunits of the SsuDE monooxygenase. Perhaps
all of these proteins comprise a metabolon jointly with other proteins in sulphur metabolism.
Next, the allantoinase, AllB64,65, was found to interact with the YbbW putative allantoin
transport protein (TC# 3.A.39.3.8) with a score of 6.5 (Supplementary Table 3), implicating
these proteins comprise a metabolon in mediating both the transport and degradation of allantoin.
We also observed two subunits of heme lyase, CcmF and CcmH, forming a tight binding
complex with a score of 13. Similarly, the BaeS sensor kinase, which phosphorylates the
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response regulator, BaeR66, to induce synthesis of the MdtBC exporter, also physically coupled
with the multidrug resistance efflux pump, MdtBC67, with a score of 14. While it is known that
the BaeS/BaeR sensor kinase/response regulator pair controls expression of the Mdt exporter, a
physical interaction between these proteins and the exporter had not been reported before. The
interaction we observed between BaeS and MdtBC suggests that MdtABC regulate the kinase
activity of BaeS, and therefore the expression of its target transport systems.
We also noted an interaction of the FliI ATPase of the flagellar system68 with the DNA-
binding transcriptional regulator, NikR (Σ LLS score 6.8), as well as between the two inner
membrane proteins (IMPs) involved in cell division and septum formation, FtsL and FtsQ, as
part of the cell division complex69. The latter interaction had been reported previously70. While
MdtBC71 and MdtEF72 multidrug resistance (MDR) pumps function independently of each other,
we found MdtB interacting with MdtC, and MdtBCF with membrane fusion protein (MFP),
YbhG, and nucleoside transporter, NupC, as well as between member of the major facilitator
superfamily (MFS) MdtH and hypothetical protein, YceI.
The physical association we found between glucuronide transporter UidB in the inner
membrane (IM)73 and putative glucuronide porin UidC in the outer membrane (OM) suggest that
these proteins interact to allow transport across both membranes of the E. coli envelope in a
single energy-coupling step. No such example of interaction between an OM porin and an IM
uptake transporter has been documented, although many examples of equivalent interactions for
export systems have been reported. However, these efflux systems use an adaptor, the MFP, to
link the OM porin to the IM transporter. If this glucuronide uptake system proves to be a
transenvelope transport channel, this would be a novel finding.
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Additionally, many poorly characterized proteins, some of unknown function (YxxX
proteins; Supplementary Table 3), prove to interact with each other. Many of these are likely to
have physiological ramifications. For example, YbhF (ABC transporter) and YbhG (a MFP)
interact with a score of 11.6. YegT, a putative MFS nucleoside transporter (TC# 2.A.1.10.4),
interacts with the sensor kinase, YehU with a score of 6.5. YehU phosphorylates YehT, the
corresponding response regulator. YehU/YehT control synthesis of transporters such as the
homolog of the oxalate::formate antiporter (TC# 2.A.1.11.3) and the peptide uptake porter YjiY
of the CstA family (TC# 2.A.114)74. This uptake system may play a role in adaptation to
stationary phase conditions.
The fimbrial adhesion protein, YehA was found to interact with the periplasmic chaperone,
YehC, with a score of 13.7. Two subunits, YejB and YejE, both in the IM, interacted with a
score of 14. These two proteins are believed to be constituents of an ABC-type oligopeptide
uptake permease. YicJ, a putative sugar transporter, was physically coupled with YicI, an alpha
glucosidase, with a score of 7.6. YnfF and YnfG, two putative subunits of an oxidoreductase,
were also found to be associated with a score of 14.9. Finally, the putative OM protein (OMP),
YtfM, as expected75, interact with a hypothetical protein, YtfN, with a score of 14.
CadC of E. coli is an acid-sensing DNA-binding transcriptional activator that interacted
with the lysine uptake transporter LysP with a score of 14. Together, these two proteins induce
lysine-dependent adaptation to acidic stress conditions76. In vivo analyses revealed that, in the
absence of either stimulus, the two proteins form a stable association, which is modulated by
lysine and low pH76,77. In addition to its transmembrane helix, the periplasmic domain of CadC
participated in the interaction. It was concluded that CadC was inhibited by LysP via
intramembrane and periplasmic contacts under non-inducing conditions. Upon induction, lysine-
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dependent conformational changes in LysP transduce the lysine signal via a conformational
coupling to CadC without resolving the interaction completely76. This recent study76 confirms
and extends the results of our interactome analysis.
Moreover, it has been proposed that CadC is a pleiotropic regulator relevant to several
systems, including but not limited to the lysine::cadaverine antiporter, CadB, which functions
with CadA, the cytoplasmic enzyme that converts lysine to cadaverine, promoting pH
homeostasis78. While the relationship of CadAB to CadC has not been investigated, we predict
that binding of CadC to LysP represents a key regulatory step76.
(iii) Membrane transport metabolon
(a) PTS system
All E. coli K-12 proteins that belong to the PTS are known through sequence homology
searches, although the functions of several of these have not been defined79. We examined all
proteins of the PTS system for potential interactions (Supplementary Table 3). The most
surprising result was that several of the integral membrane transport proteins of the PTS (IIC
constituents) showed interactions with other IIC constituents, suggesting that the integral
membrane PTS transporter/enzymes might form a large complex.
While published evidence suggests that the soluble energy coupling proteins of the PTS
(Enzyme I, HPr, and sugar-specific IIA proteins) associate with PTS permeases80, no evidence
had been available prior to this study to suggest that the different transport complexes of the PTS
might interact. For instance, several PTS enzyme IIs81 form a complex (Supplementary Table
3). These include fructose permease (FruA) interaction, as expected82, with its cognate energy
coupling protein, FruB, and with the following enzyme IIC constituents: (1) NagE, specific for
N-acetylglucosamine, (2) GatC, for galactitol, (3) TreB, for trehalose and (4) MngA, for
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mannosyl glycerate. Besides, FruB also interacted with the N-acetylglucosamine permease
(NagE)80. Establishment of such a complex would be both novel and important, as presence of
multiple PTS permeases in a single complex could facilitate energy mediated by the energy-
coupling proteins of the system, Enzyme I and HPr, as well as explain the non-uniform
distribution of PTS proteins within the cytoplasmic membrane.
(b) HPr interactions
In some bacteria, such as firmicutes, HPr (or PtsH), the small PTS energy coupling protein, plays
a dominant role in the regulation of carbon metabolism83. However, in E. coli and other enteric
bacteria, interactions of HPr with other constituents for purposes of regulation are not well
established. Interactions of HPr with other cellular proteins that we report in our study
(Supplementary Table 3) makes physiological sense. In fact, the results suggest that HPr
interacts with a number of cellular constituents involved in carbon metabolism and protein
synthesis, and possibly other bioprocesses.
E. coli HPr is known to interact with, energize and be involved in the regulation of PTS
enzymes II complexes84. It also interacts with the E. coli central regulatory protein, the IIAGlc
(Crr) protein85, and the dihydroxyacetone kinase (DhaK)86,87. The interaction of HPr with IIAGlc
and DhaK are confirmed in our study. Other metabolic enzymes that appear to interact with HPr
include the critical glycolytic enzymes, 6-phosphofructokinase II (PfkB) and pyruvate kinase I
(PykF), glucosamine 6-phosphate deaminase (NagB) and 3-deoxy-D-manno-octulosonate-8-
phosphate synthase (KdsA). HPr also interacted with adenylate kinase (Adk) that interconverts
ADP and ATP88, as well as the iron storage and detoxification protein, bacterioferritin (Bfr).
The interactome data presented in Supplementary Table 3 also suggests that HPr plays a
significant role in ribosome-dependent protein biosynthesis89. Interactions relevant to this
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process include those with protein chain elongation factor (EF-Ts) (the Tsf protein) which plays
a critical role in the rate of translation90, and the predicted ribosome-associated σ54 modulation
protein (Hpf)91. Moreover, HPr found to interact with the 16S rRNA processing proteins, RimP
(YhbC) and RimM92. RimP is important for maturation of the 30S ribosomal subunit93. It
associates with this ribosomal subunit, but not with the 50S or 70S ribosomal complexes, and it
is essential for the formation of RNA pseudoknot94. RimM is another 16S rRNA processing
protein that interacted with HPr with comparable affinity. As well, we found HPr to be
associated with the ribosomal recycling factor, Frr95.
Several other proteins with HPr were also linked (Supplementary Table 3). For example,
YgiW is known in some bacteria (such as Aggregatibacter) to be a periplasmic stress protein,
which regulates biofilm formation96, while in Pseudomonas species, its ortholog is involved in
protection against heavy metals, oxidative stress, acid stress and reactive oxygen species97.
While HPr is predominantly present in the CY, and YgiW is believed to be periplasmic98, this
difference in location does not preclude the possibility of interaction since YgiW is made in the
CY prior to export to the PE. Moreover there are reports99,100 indicating that predominantly
cytoplasmic proteins can exist in the PE, and periplasmic proteins can exist in the CY.
Interactions within ABC transporters
In this section, we will discuss the data observed within the 12 individual ABC transport systems
(Supplementary Fig. 7b, c). The results comply with expectations since in many of these
systems, interactions between the ATPases, transmembrane subunits, and receptors have been
documented in previous studies51. The first system to be analyzed is the arginine transport
system, Art. As shown in Supplementary Fig. 7b, this system consists of a homodimeric
ATPase, ArtP, a heterodimeric channel forming membrane complex, ArtMQ, and one of two
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periplasmic amino acid binding receptors, I and J101-103. Our interactome data indicate that P
interacts with both M and Q, and that I interacts with J.
The next three systems portrayed in Supplementary Fig. 7b, c are specific for peptides104.
The dipeptide transport (Dpp) system (Supplementary Fig. 7c) showed extensive interactions
where both ATPases, DppD and DppF, interact with both membrane constituents, DppB and
DppC. All four of these interactions gave good Σ LLS scores, between 7 and 14. An interaction
between the membrane subunit, DppB, and the PE receptor, DppA, was also noted with a score
of 12. Interactions were also observed for the Gsi peptide transport system105, where the GsiA
glutathione ATPase interacted with both membrane constituents, GsiC and GsiD, which also
linked with each other. GsiC was also associated with the PE receptor, GsiB. Likewise,
examination of the Sap peptide uptake system106 revealed interactions between dissimilar
ATPase subunits SapDF, and integral membrane constituents SapB, SapC as well as SapBCF.
Additionally, the iron uptake system, FhuBCD107 (Supplementary Fig. 7b), showed
strong interactions between the homodimeric ATPase (FhuC2), and the homodimeric channel
forming complex (FhuB2). A much stronger interaction was measured between FhuB and the
periplasmic receptor, FhuD, while weaker with FhuC and FhuD. Likewise, for the glutamine
uptake system, the homodimeric ATPase, GlnQ, was found to interact with the homodimeric
membrane constituent, GlnP. In this case, no interaction with the binding receptor was
detected108.
The next system we examined appeared to be specific for polyamines (Supplementary
Fig. 7b). In the PotABCD system109, interactions of PotA with both PotB and C were observed,
and an interaction between the two integral membrane constituents, PotB and PotC, was also
observed with a much higher score. The Ydc putative polyamine uptake system showed only
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interactions between the homodimeric YdcT ATPase and the heterodimeric channel forming
constituents, YdcU and YdcV.
In the case of the leucine ABC transporter system, LIV-locus is comprised of two
periplasmic proteins, LivJ and LivK, IM permeases LivH and LivM, and cytoplasmic ATPases
LivG and LivF. While individual LIV ABC transporter components are responsible for the
uptake of branched amino acids, especially leucine, evidences110-112 suggest that the periplasmic
proteins of transport systems are able to use non-cognate permeases and ATPases to transport the
amino acids into the cytoplasm. Consistent with this proposed notion112, our interaction network
identified that ATPases (LivGF) interact with each other and with the PE protein LivK,
suggesting the requirement of these proteins in the transport of branched-chain amino acids into
the bacterial cell. Similar to the LIV system, interaction was observed between GltL ATPase and
GltJ, an integral membrane constituent113 of the aspartate/glutamate (GltIJKL) uptake system.
The last two systems depicted in Supplementary Fig. 7b are efflux systems, the first of
which (YbhFGRS) has not been characterized (TC# 3.A.1.105.15), but the second, LolCDE, is a
lipoprotein export system114 (TC# 3.A.1.125.1). There is no periplasmic receptor for either of
these systems, but interestingly, the Ybh exporter includes a MFP, YbhG. In this case,
interactions of the homodimeric YbhF ATPase, with the two dissimilar integral membrane
constituents, YbhR and YbhS, were documented as the interaction between these two membrane
components. Notably, both integral membrane components of this system interacted with YbhG
MFP. A similar case was observed for LolCDE lipoprotein export, where all three components of
this system were shown to interact, the two dissimilar membrane constituents interacting with a
much higher score than was observed for the interactions between the ATPase and the channel-
forming subunits.
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In fact, a quick examination of Supplementary Fig. 7b showed that only two of the 12
systems depicted have heterodimeric ATPases, only one system, the Art arginine transporter, has
two sequence-similar receptors, and only two of the systems illustrated (Fhu and Gln) have
homodimeric membrane constituents. Interestingly, only the Dpp system provided evidence for
interactions between the cytoplasmic heterodimeric ATPase subunits (DppDF; Supplementary
Fig. 7c) and the periplasmic receptor, DppA. This result could be of physiological significance,
either because the interactions occur in the CY before export of the peptide binding receptor, or a
portion of the ATPase is likely to transiently cross the cell membrane115.
Sec protein interaction with cytosolic ClpAPX factors
The IM Sec translocon (SecDEY) was found to interact with the cytosolic proteins (ClpAPX)
involved in degradation/folding. Regarding the validity of Sec interactions with cytosolic
proteins, we note that: (i) interactions between Sec and Clp systems are supported with multiple
lines of evidences and hence are categorized as high confidence (Supplementary Table 2); (ii)
cytosolic molecular chaperones dynamically regulate folding (preventing aggregation in the case
of Sec and promoting folding in the case of Tat) and, in some instances, guide the substrate from
the ribosome to the translocon116; (iii) ClpAPX helps with protein folding, particularly with the
cytoplasmic domains of membrane-inserted proteins; and (iv) bacterial protein homeostasis is
tightly coupled with the protein translocation machinery117, which could aid in degrading
misfolded CE protein precursors.
Bam-Tam interplay advances existing understanding of the system
Gram-negative bacteria require dedicated machinery for the assembly and trafficking of proteins
that are required for bacterial pathogenesis. At least 7 specialized secretion systems have been
developed (Types I-VI and the chaperone-usher pathway) that facilitate navigation and secretion
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32
of proteins across the bacterial CE. While most of these pathways assemble large protein
complexes, the type V secretion system - which encompasses the two-partner secretion system
and autotransporter pathway - uses a relatively simple mechanism to deliver autotransporter
proteins to the cell surface of the OM. The autotransporter superfamily is tightly linked to
virulence as the proteins it translocates play key roles in many pathogenic functions such as cell
adherence, biofilm formation, immune system evasion, and cytotoxin secretion118.
An archetypal premature autotransporter from the Type Va secretion pathway119 consists of
a signal peptide followed by the functional N-terminal passenger domain (NPD), and finally a C-
terminal -barrel domain that anchors the protein in the OM120. While the passenger domains
vary dramatically in sequence and size (20 to 400 kDa), they almost all contain a -helical
structure secreted through the C-terminal 12 stranded pore-forming -barrel (30 kDa) embedded
in the OM. After translocation, the passenger domains and the membrane embedded -domain
remain connected together within the PE by an -helical segment spanning the lumen of the -
barrel domain121. In this manner many passenger domains remain anchored to the cell surface.
However, some autotransporters release their passenger domains into the extracellular milieu
through a variety of mechanisms, including exogenous proteases or autocatalytic cleavage -
leaving the -helical segment occluding the pore118.
The translocation of the autotransporter’s passenger domain across the OM to the cell
surface occurs in a C- to N-terminal vectorial secretion, likely initiated by the formation of a
hairpin structure originating at the C-terminus of the passenger domain122. This transient hairpin
has been suggested to remain within the lumen of the -barrel domain while a labile strand of the
hairpin elongates through the pore until the passenger domain is completely translocated123.
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However, the hairpin model is controversial given the steric constraints within the narrow pore
formed by the 12 stranded -barrel domain121.
A second model suggests the BamA - the central component of the Bam complex -
contributes to the secretion of the passenger domain124. Recent structural reports of BamA
suggest an alternative mechanism where BamA sequentially incorporates -strands from the
nascent OMP into its own 16-stranded barrel via a lateral opening mechanism125-127. This -
augmentation model of OMP biogenesis implies the formation of a hybrid BamA-OMP complex
with a larger pore, which is compatible with the hairpin secretion model given the enlarged
lumen and could also facilitate the secretion of a partially folded passenger domain. In either
case, the energy required to secrete the passenger domain remains to be discovered. It has been
proposed that the progressive folding of the passenger domain at the cell surface drives the
translocation across the OM to prevent any retrograde slip into the PE128; however the secretion
may require additional factors to increase the efficiency of the translocation step that has been
observed for engineered passengers containing intrinsically disordered domains129,130.
Recently the membrane spanning translocation and assembly module (Tam) composed of
TamA and TamB has been implicated in autotransporter secretion within specific bacterial
lineages75,131, however their precise contributions in the type V secretion system assembly
pathway remains unknown. TamA is a 60 kDa OMP of the Omp85 family (BamA, FhaC
homologs125,132), consisting of an N-terminal periplasmic domain subdivided into 3 polypeptide
transport associated (POTRA) repeats preceding a 16-stranded C-terminal -barrel domain that
is embedded in the OM. The TamA structure from E. coli revealed a similar fold to the BamA
OMP-insertase133 (Supplementary Fig. 8a), and has been suggested to function as the conduit
required to translocate autotransporters across the OM.
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TamB is found in the same operon as TamA and encodes a 140 kDa IMP with no
characterized homolog. TamB consists of an amino-terminal transmembrane helix, a large
interdomain with no assigned gene ontology (GO) annotation, and a C-terminal DUF490
domain. Protease shaving assays and bioinformatic analyses of the transmembrane topology
indicate that TamB is localized within the periplasmic space75 and that the TamA and TamB
form a hetero-oligomeric complex crucial for the delivery of the autotransporter passenger
domain to the cell surface75.
To investigate the molecular mechanism employed by the Tam components, we
characterized the relative effect of each of the tamA and tamB deletion mutants using various
autotransporter proteins (Ag43, AidA, TibA and YadA) as part of the translocation model. These
selected autotransporters belong to phylogenetically diverse subfamilies134 including the
monomeric autotransporter group with both the PL1- (Ag43, AidA) and PL2- (TibA) subgroups
(type Va secretion system), as well as the trimeric autotransporter group (YadA; type Vc
secretion system). Our findings demonstrate that TamB is the only component of the Tam
complex essential in the secretion of autotransporters, as the tamB knockout prevents an
agglutination signature phenotype of the autotransporters (Fig. 4c-e). This gene deletion leads to
the delivery of non-functional autotransporters onto the cell surface, underlying a potential role
of TamB as a chaperone factor involved in the maturation process of the passenger domain.
Additionally, systematic proteomic and genetic approaches for both TamA and TamB
provided an important mechanistic insight on deciphering their molecular action in OM
biogenesis. Taken together, our findings report for the first time that autotransporter OMPs, such
as Ag43, AidA, TibA and YadA from the type Va (monomeric autotransporters) and Vc
(trimeric autotransporters) secretion systems, do not spontaneously self-assemble at the cell
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surface but instead require chaperone assistance to nucleate their folding in the PE prior to
delivery to the bacterial cell surface through a cooperative mechanism involving the Bam and
Tam machineries.
(i) TamA-TamB functionally associated with Bam
Physical association of TamA and TamB with Bam components as revealed through our affinity
purification and mass spectrometry (AP/MS) experiments led us to examine epistatic
relationships between the members of these component machineries. To confirm this, we
generated double mutants by conjugating the E. coli tamA or tamB query donor mutant strain
(marked with chloramphenicol) against the non-essential or hypomorphic alleles of the essential
bam recipient mutants (marked with kanamycin) using our established synthetic genetic array
procedure135. The colony growth and relative fitness of the resulting double mutants surviving
dual drug selection was then compared to their corresponding single mutants.
As with our proteomics experiments, we observed a strong synthetic sick lethal (SSL)
phenotype between tamA or tamB with bamA, and not to other bam components (Fig. 4b). This
SSL phenotype, confirmed here through manual conjugation screens between tamA or tamB with
bamA in the standard nutrient rich growth condition, was not tested in the large-scale envelope
biogenesis epistatic screens35 and has not been previously reported. Conversely, we were unable
to observe the SSL growth defect noted before35 between tamB and bamC or bamD. This could
be due to subtle differences in growth conditions, duration of incubation, nutrient availability on
the plates at the time of screening, or the limitations in sensitivity within the scoring and
thresholding procedures used, which aim to highlight genetic interactions (GIs) that stand out in
contrast to the distribution of scores across all tests.
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As in any other large-scale epistatic screens135, it is therefore imperative to perform small-
scale validation experiments as previously described136, to verify the putative GIs generated from
the high-throughput eSGA screens. Nevertheless, one interpretation of our confirmed result (Fig.
4b) is that TamA and TamB jointly participates in autotransporter biogenesis in a pathway
overlapping with BamA mediated export, which constitutes a dominant secretion pore for Type
V autotransporters.
(ii) Contribution of Ag43 and TamB to existing knowledge and its connection to potential
interactions identified from AP/MS
Our proteomic analysis confirmed the TamA-TamB interaction, originally suggested by blue
native polyacrylamide gel electrophoresis analysis75 to form a heterooligomeric Tam complex
(spanning the inner and outer membrane) with a set of unidentified components. Consistent with
the latter, we detected physical association with proteins that function in OMP biogenesis
(BamABCD) and autotransporter processing (Ag43; Fig. 4a). These interactions are likely to be
physiologically relevant as the secretion mechanism of autotransporters relies on several
pathways that form the basis of OM protein trafficking and biogenesis. Autotransporters are
translated into the cytoplasm and translocated across the IM into the PE in a signal peptide and
Sec-dependent manner137. The Bam complex is thought to be responsible for the insertion of the
-barrel domain into the OM by acting as a generic foldase-insertase complex dedicated to OM
proteins138-141. However, the specific role or strict requirement of the Bam complex during
passenger secretion has not yet been elucidated.
The localization of TamB to the periplasmic side of the IM further suggests that TamB
primes the maturation of the passenger domain prior to its translocation across the OM. This
model is supported by both our proteomic and genetic studies, which reveal strong physical and
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37
functional connectivity between the Tam and Bam machinery (Fig. 4a), suggesting a plausible
synergistic mechanism in the secretion and maturation of autotransporters. The interplay between
TamA and TamB with the ubiquitous Bam machinery likely facilitates or regulates the folding of
other dedicated protein clients as suggested by i) the BamA-TamA structural homology125,133;
(ii) our experimental data illustrating the multiple connections of the Tam complex with the
OMP biogenesis route; and (iii) the Bam-Tam epistatic genetic connectivity (synthetic lethality).
TamA has previously been reported to promote the secretion of the passenger domains
from the two autotransporter Ag43 and p1121 in E. coli and Citrobacter rodentium,
respectively75; however, consistent with other studies129,142 we were unable to reproduce the
Ag43 secretion defect in E. coli (Fig. 4c-g and Supplementary Fig. 8b-d). To validate our
initial observations, we characterized the secretion of three additional autotransporters (AidA,
TibA and YadA) that belong to different autotransporter subfamilies134, all of which provide a
robust indication of the innocuous effect of a tamA deletion on autotransporter secretion, while
supporting the key role of TamB for proper autotransporter maturation.
(iii) TamB deletion leads to the secretion of non-functional passenger domains
While the surface exposed and folded Ag43 NPD remains anchored to the OM via its C-terminal
-barrel domain, the mature fully translocated protein is normally clipped after the amino acid
D551 by an unidentified protease. This proteolysis results in the detachment of functional surface
domain (Ag43) only under thermal denaturation conditions (60 C)143. Hence, to exclude the
possibility of a minor segment of Ag43 NPD being detected at the cell surface due to a
hypothetical translocation defect in the tamB mutant, we confirmed the ability of the Ag43
passenger domain to fully secrete by exploiting the heat-shock release property of Ag43.
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38
Flow cytometry performed on 60 C heated E. coli cells showed a complete disappearance
of the passenger domain from the cell surface of wild-type (WT) and tamB mutant strains (Fig.
4e, f). The extracellular supernatant obtained from the heat-shock of both WT and tamB mutant
E. coli cells was analyzed by SDS-PAGE and MS, identifying the presence of Ag43 fragment
in both supernatant fractions and thus confirming the secretion of the entire Ag43 passenger
domain outside the cells (Supplementary Fig. 8c, e). Finally, the molecular mass of the secreted
Ag43 functional domain from both WT and tamB mutant strains were identical
(Supplementary Fig. 8d), indicating that the agglutination defect in tamB cells is not caused by
any differential posttranslational modification that would influence Ag43 function.
To identify potential folding defects associated with the passenger domains translocated at
the cell surface of the tamB mutants, we performed a stability assay by treating Ag43-expressing
WT and tamB E. coli cells with proteinase-K while monitoring the degradation of Ag43 in a time
dependent manner (Fig. 4g). Our data represent the instability of the amino-terminal domain of
Ag43 translocated at the cell surface from the tamB strain, which is more susceptible to
proteinase-K digestion as compared to the passenger domain secreted through the OM of the WT
strains. Taken together, our data strongly suggest that the Tam complex is not involved in the
passenger domain translocation of autotransporters, but rather involved in the folding process of
the passenger domain to be translocated onto the cell surface.
(iv) Secretion model for the autotransporters from the type Va pathway
Our results demonstrate that the Tam complex is an auxiliary machinery linked to the Bam
complex (Fig. 4h), contributing to OM biogenesis. TamA is an Omp85 protein with high
structural similarity to BamA that forms a heterodimer with TamB. The TamA/B complex
Nature Biotechnology: doi:10.1038/nbt.4024
39
protrudes 200Å from the inner-leaflet of the OM into the PE, with TamB harboring an elongated
shape previously suggested to form a ß-helical structure144.
First, the nascent autotransporter chain translocated into the PE in a Sec-dependant manner
binds with the Tam complex while it is targeted to the Bam machinery through its C-terminal β-
motif. Second, the 12-stranded membrane anchored domain from the autotransporter is
processed by the Bam complex to fold and insert the ß-barrel domain within the membrane. The
insertion of the membrane domain requires a lateral opening of BamA, thus BamA and the
autotransporter client form an hybrid pore composed of the insertion of the newly folded ß-
strands within the BamA barrel by ß-augmentation125,126. Third, the addition of newly folded ß-
strand within the hybrid pore enlarges the BamA exit pore126,127, allowing the translocation of the
passenger domain of the autotransporter. The TamB component then contributes to the proper
folding of the passenger domain while it translocates to the cell surface. Finally, the mature
autotransporter ultimately buds off from the BamA-client hybrid-pore to populate OM126.
Web portal details
We provided options to use our website (http://ecoli.med.utoronto.ca/membrane) as a resource to
download the entire set of interactions in excel table format, or to query individual protein
names, subcellular compartments or keywords. We also now allow users to access the interaction
data after specifying varying confidence filters:
1. High confidence (HC): interactions supported by multiple replicates, detergents, and
different experimental methods.
2. Medium confidence (MC): interactions supported by functional and/or physical
association evidence predicted by STRING145, GeneMANIA146, genomic context
methods9, or co-localization to the same compartment.
Nature Biotechnology: doi:10.1038/nbt.4024
40
Putative interactions not supported by any of the above additional criteria were removed from the
analysis. Moreover, users are provided with information encompassing protein complex
composition, subcellular localization, abundance, and the presence (predicted) of transmembrane
helices, ß barrels, and/or signal peptides. The browser also allows searching for interactions in
batch mode (e.g. multiple CEPs at once), where the user can define the confidence (e.g. ΣLLS)
score cut-off and exclusion/inclusion of common interactors. The results can be readily
visualized in Cytoscape by clicking on the icon presented in the browse “complex” section.
Numbers presented in the edges between proteins refers to the ΣLLS score.
Next, we have implemented a high-level browsing function, where by clicking on either a
protein, interaction, or complex, one can quickly survey all relevant CEPs, interactions and
complexes found in this study. Users have the flexibility to rapidly identify: (1) interaction
partners of any given CEP bait and with what confidence (ΣLLS score), (2) in which predicted
complex, a given CEP and its interacting proteins are present, (3) if a given CEP was
successfully recovered as a bait, (4) the protein pairs with “bait-prey” and “prey-prey”
associations, and (5) the type of models (i.e. Matrix, Spoke or Socio-Affinity) assigned to each
predicted complex. Users can also access the supporting mass spectral evidence and probability
scores. Finally, we now include a “help” documentation page to assist navigation of the database,
and downloading of both the complete dataset and associated supplementary tables to raise the
reader’s awareness of how the data is presented and accessed via our website.
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Supplementary Tables
Supplementary Table 1 | Classification of the E. coli proteome and target cell envelope protein
selection for AP/MS screens
Nature Biotechnology: doi:10.1038/nbt.4024
49
Supplementary Table 2 | Co-purifying protein pairs compiled from reference Ecoyc database
and from this study
Supplementary Table 3 | The physiological relevance and quality of PPIs by anecdotal
supporting evidence, drug sensitivity profiles, and two-hybrid screens
Supplementary Table 4 | Putative (or novel) CEP complexes and their subunits identified by the
core-attachment based clustering algorithm are indicated with their respective topology model
Supplementary Table 5 | Antibiotic susceptibility, evolutionary conservation, and paralogous
analyses on CEPs or complexes, and bacterial strains/plasmids used in this study
Nature Biotechnology: doi:10.1038/nbt.4024