Escherichia coli cell envelope proteome and its high-resolution fractionation in the absence of detergent. Analysis of the SILAC-labeled peptidisc library via PCP allows generation of over 4900 possible binary interactions out of >700,000 random associations. Using well-characterized membrane protein systems such as the SecY translocon, the Bam complex and the MetNI transporter, we demonstrate that our dataset is a useful resource for identifying transient and surprisingly novel protein interactions. For example, we discover a trans-periplasmic supercomplex comprising subunits of the Bam and Sec machineries, including membrane-bound chaperones YfgM and PpiD. We identify RcsF and OmpA as bone fide interactors of BamA, and we show that MetQ association with the ABC transporter MetNI depends on its N-terminal lipid anchor. We also discover NlpA as a novel interactor of MetNI complex. Most of these interactions are largely undetected by standard detergent-based purification. Together, the peptidisc workflow applied to the proteomic field is emerging as a promising novel approach to characterize membrane protein interactions under native expression conditions and without genetic manipulation.Protein-correlation-profiling (PCP), in combination with quantitative proteomics, has emerged as a high-throughput method for the rapid identification of dynamic protein complexes in native conditions. While PCP has been successfully applied to soluble proteomes, characterization of the membrane interactome has lagged, partly due to the necessary use of detergents to maintain protein solubility. Here, we apply the peptidisc, a ‘one-size fits all’ membrane mimetic, for the capture of the  Proteins control biological systems in a cell. While many perform their functions independently, the majority of proteins interact with others to achieve their full biological activity. Characterizing protein-protein interaction networks (the interactome) has traditionally been accomplished by methods such as affinity purification coupled to identification by mass spectrometry (AP/MS) , proteinWhile co-fractionation has been successfully applied to soluble proteomes, characterization of the membrane proteome has lagged. This is largely due to the hydrophobic nature of membrane proteins and their sequestration in the lipid membrane. To extract this water-insoluble proteome, it is necessary to solubilize the lipid bilayer with the aid of detergents or amphipathic co-polymers such as styrene maleic acid (SMA) . When mir  wrap around the solubilized membrane proteins. Reconstitution occurs spontaneously upon removal of detergent, incorporating both endogenous lipids and solubilized membrane proteins into detergent-free particles. The number of scaffolds adapts to fit the diameter of the protein target without bias toward large protein complexes. The end result is peptidisc particles that are stable, free of detergent effects, and soluble in aqueous solution  in the total absence of detergent. Application of the PCP workflow, which includes stable isotope labeled amino acids in cell culture (SILAC) and mass spectrometry (LC/MS-MS), allows us to precisely characterize the content of the peptidisc library across the various fractions. When the peptidisc library from the raw E. coli cell envelope interactome (‘CE’)   and two e (‘CE’) . We alsoGuided by the peptidisc interactome datalist, we select three well-characterized membrane protein complexes in order to discover novel interactions. With the Sec translocon, we validate association of SecY with the membrane chaperones YfgM and PpiD. This interaction can be isolated in detergent but only when all subunits are simultaneously over-produced. Remarkably, we also discover significant correlation between certain subunits of Sec and Bam complexes, suggesting an astonishing network of protein associations spanning across the entire bacterial cell envelope. We confirm this observation using SILAC AP/MS, thereby providing direct evidence for the Bam-Sec super-complex. Continuing with the Bam complex, we show that all five subunits are captured in peptidisc in addition to two other interactors - RcsF and OmpA. These interactions were previously inferred from genetic and indirect cross-linking experiments, but direct association was not formally demonstrated . AccordiAltogether, this work validates the peptidisc library workflow as an efficient method for capturing and stabilizing the membrane proteome into soluble particles. The method enables high-throughput detection of detergent-sensitive membrane protein interactions. When combined with rigorous experimental validation, the peptidisc interactome is revealing novel and transient interactions, many of them of fundamental importance to the transport process and biogenesis mechanism of the cell envelope.E. coli cell envelope, we employed three different non-ionic detergents  and one ionic detergent (DOC). Each was tested using E. coli crude membranes enriched for an inner membrane protein marker, MsbA. Upon removal of aggregate by ultracentrifugation, the solubilized membrane proteomes were incubated with Peptidisc peptide at the ratio 2:1 (g/g). Formation of peptidisc libraries was initiated by detergent dilution followed by filtration and concentration on a spin column with a cut-off of 100 kDa. The overall content of each library was then compared to the original detergent extract using SDS-PAGE and Coomassie blue staining . We compared the SEC fractionation profiles between the  library . The ove library  was anal library .To verify that individual membrane proteins and complexes were trapped in discrete peptidisc particles - rather than being non-specifically clustered together - we isolated MsbA from the peptidisc library via a Ni-NTA pulldown. Analysis by SDS-PAGE revealed that his-tagged MsbA is efficiently isolated from the peptidisc library preparation . Native We next prepared light and heavy isotopically labeled membrane proteomes and reconstituted them into peptidisc libraries. Both libraries were separated by high-resolution size exclusion chromatography using two silica-based BioSep4000 columns placed in tandem. The light fractions were pooled and an equivalent volume was added to each heavy fraction before trypsin digestion and electrospray mass spectrometry analysis. For each fraction, proteolytic peptides were identified and SILAC ratios were determined using Maxquant. Two biological replicates were performed, resulting in the identification of 1209 proteins across the 54 fractions . The SMA polymer directly solubilizes membrane lipids and captures proteins into styrene maleic acid lipid-protein nanoparticles (SMALPs). Followi (SMALPs). There w (SMALPs). Thus, STo determine if the peptidisc is able to capture membrane protein complexes, we compared the co-fractionation profiles of three well-characterized protein assemblies after encapsulating the membrane library in either SMALPs or peptidiscs . In all Binary protein interactions in the peptidisc library were predicted using PrInCE (Predicting Interactomes via Co-Elution), a software designed for analyzing PCP-SILAC datasets . PrInCE Using our predicted peptidisc interactome, we then generated a High Confidence subset of interactions based on two orthogonal high-throughput interactomes collected independently from this study  . These HE. coli is relatively small compared to the set of gold standard interactions derived from mammalian studies (E. coli cell envelope (CE) interactome recently published by As false positives are inherent to high-throughput interactome studies, it is important to validate computationally that the interactome, on aggregate, indeed resembles a collection of true, biological interactors. To do so, we first used our gold standard protein complexes to calculate the ratio of TPs to FPs, measured as precision (TP / (TP + FP)) . However studies , meaning studies . FurtherUsing these measures of biological plausibility, we found the peptidisc interactome to be enriched compared to random chance, and as shown in E. coli CE interactome  precision. However, this association is difficult to detect in detergent, unless all subunits are simultaneously over-produced in the membrane . We wereExperiments with the detergent extract shows that SecY is highly enriched after affinity pulldown . There iWe next applied the AP/MS workflow towards the protein BamA - the major subunit of the outer membrane-embedded Bam complex. Our interactome dataset identified the Sec ancillary subunits YidC and YajC as potential Bam interactors with high precision (>75%), as well as the cell surface-exposed lipoprotein RcsF . To explbona fide interactor of both RcsF and BamA in the cell context  MetQ forms a stable complex with MetNI and ii) NlpA (lipoprotein 28) is a novel interactor of the transporter , indicating that both methods effectively solubilize the membrane proteome . HoweverE. coli interactomes collected independently in a separate study (To determine the precision of the protein correlation profile (PCP) obtained with the peptidisc library, we had to separate a relatively small number of well-correlated protein pairs from a much larger background of non-interacting proteins. We used PrInCE, a machine learning bioinformatics tool written specifically for analyzing co-fractionation datasets . This gete study . The intte study . The numte study , which sParallel to this computational validation, we performed experiments to verify the ability of the peptidisc workflow to reveal novel or transient interactions. Our first validation target was the SecYEG complex. The pairwise peptidisc interaction list indicate a number of interactors at a high degree of precision >75%; , such asStrikingly, the peptidisc interaction datalist also revealed a network of pairwise interactions between subunits of the Sec and Bam complexes. Interactions between SecYEG and the BamA, BamC and BamD subunits in particular are present in our interaction list at high precision. This Sec-Bam interaction has been suggested previously based on coincident detection between membrane fractionation and Western blotting . In ordeOther BamA interactors identified in the peptidisc-PCP-SILAC workflow were RcsF and OmpA . The facE. coli. This novel finding therefore calls for a re-evaluation of the role of the MetQ lipid anchor for complex stability and substrate transport. As an additional original discovery based on the peptidisc interactome data list, we identify and demonstrate the interaction of the MetNI complex with the lipoprotein NlpA , ampicillin, kanamycin, and arabinose were purchased from GoldBio. Detergents n-dodecyl-β-d-maltoside (DDM) and octyl-β-D-glucoside (β-OG) were from Anatrace. Detergent N,N-dimethyldodecylamine N-oxide (LDAO) was from Sigma. Columns Biosep 4000 GFC/SEC were purchased from Phenomenex. Ni2+-NTA chelating Sepharose was obtained from Qiagen. Peptidisc peptides and biotinylated derivative Bio-Peptidisc (purity >90%) were obtained from Peptidisc Biotech Canada. All other chemicals were obtained from Fisher Scientific Canada. The genes yfgM or ppiD were inserted into pBAD33 encoding for a C-terminal 6x his-tag via the polymerase incomplete primer extension (PIPE) method . Those plasmids were employed to construct pBad33-MetQ and pBad22-hisMetNI. The metQ gene was amplified from pET21-MetQhis and inserted into pBAD33. Plasmid pBAD22-hisMetNI was constructed by sequentially inserting the metN and metI genes from pET19-hisMetNI into pBAD22. The MetQ C23A mutation was inserted into pBad33-MetQ by site-directed mutagenesis. All construct sequences were confirmed by Sanger sequencing (Genewiz). The gene msbA was inserted with a sequence encoding for a N-terminal 6x his-tag into the vector pET28 to form the plasmid pET28-msbA. The plasmids pBad22-HA-EYG and pBad22-his-EYG have been previously described  method  to form escribed .E. coli strain JW2806 (ΔlysA763::kan) was labeled with Lys4 (2H4-lysine), as previously described  (New England Biolabs) for 3 hr at 37°C after induction with 0.5 mM IPTG at an OD of 0.4–0.7 in LB medium supplemented with 25 µg/mL kanamycin. Cells were harvested by low speed centrifugation  and resuspended in SEC buffer . Resuspended cells were treated with 1 mM phenylmethylsulfonyl fluoride (PMSF) and lysed using a french press at 10,000 psi. Unbroken cell debris and other aggregates were removed by an additional low-speed centrifugation. The crude membrane fraction containing overexpressed MsbA was subsequently isolated by ultracentrifugation . The crude membrane fraction was resuspended in SEC Buffer to a concentration of ~20 mg/mL. To screen different detergents, aliquots of MsbA-containing crude membranes were solubilized in either 1% DDM, 3% β-OG, 1% DOC or 1% LDAO. Solubilizations were performed at 4°C for 1 hr. Insoluble material was then pelleted by ultracentrifugation . The detergent-solubilized extracts were subsequently trapped in peptidisc libraries as described below. To compare the efficiency of library capture between the different detergents assayed, aliquots of each detergent extract and each resultant peptidisc library were analyzed side by side on 15% SDS-PAGE followed by Coomassie Blue staining. Peptidisc-MsbA was subsequently isolated by Ni2+-chelating chromatography in SEC buffer, washed in 10 column volumes (CV) of Wash Buffer , and then eluted in ½ CV Elution Buffer . For purification of MsbA in DDM, the procedure was repeated except there was no addition of Peptidisc peptide to the solubilized crude membrane and a concentration of 0.02% DDM was maintained in all buffers during the dilution and purification steps.Plasmid pET28-Cells grown to OD ~0.9–1.2 were pelleted by low-speed centrifugation  and resuspended in 2 mL Buffer A. Cells were lysed by French press  and cell debris removed by an additional low-speed centrifugation step . Crude membrane was isolated by ultracentrifugation , and resuspended in Buffer A to a protein concentration of 20 mg/mL. The crude membrane was solubilized in 0.8% DDM, and isolated by ultracentrifugation. Solubilized crude membrane (100 µL at 10 mg/mL) was mixed with the Bio-Peptidisc peptide (350 µL at 6 mg/mL), and the mixture diluted to 10 mL ([DDM] ≅ 0.008%). The mixture was concentrated over a 100 kDa cut-off polysulfone filter (Sarstedt) to 500 µL, then diluted again to 5 mL in Buffer A ([DDM] ≅ 0.0008%). The library was concentrated to 250 µL  and left on ice until fractionation. For pull-down experiments, the libraries were concentrated to ≅1 mg/mL and placed on ice until subsequent use.The SMA polymer containing 2:1 styrene to maleic acid ratio was prepared following the procedure described in reference . In brieCell envelope protein libraries were fractionated by size exclusion chromatography as previously described . In briehisEYG, pBad22-hisMetNI and pBad22-hisBamA were chemically transformed into E. coli JW2806. Cells were grown overnight in M9 media supplemented with either 0.3 mg/mL Lys4  or 0.3 mg/mL light Lysine (for pBAD22). The next morning, the cultures were diluted 1/100 into fresh M9 media supplemented with either Lys4 or light lysine. Protein expression was induced with 0.02% arabinose once the cells had reached OD ~0.4–0.6. The cultures were then shifted to 25°C and grown overnight. Cells were harvested and resuspended in TSG buffer containing 1 mM PMSF before being lysed on a French Press . The membrane fraction was collected and resuspended in TSG  buffer, rather than in SEC buffer. Membranes were solubilized in 0.5% DDM for 15 min on ice. Solubilized material was clarified by ultracentrifugation . An aliquot of the detergent-solubilized material was purified as described above, except that all steps contained TSG buffer, with DDM and imidazole where necessary. The remainder of the detergent-soluble supernatant was mixed with a 4:1 excess of Peptidisc peptide and peptidisc libraries were prepared by the dilution and concentration method described above. The resultant library (~1 mL at 1 mg/mL) was purified as described above for MsbA, except that all steps contained TSG buffer (with imidazole when necessary), not SEC buffer. Eluted proteins were analyzed by 15% SDS-PAGE and visualized by Coomassie Blue staining. For mass spectrometry analysis, the detergent-purified ‘heavy’ and ‘light’ elutions fractions were pooled and acetone precipitated before being digested with trypsin and LysC. The samples were then STAGE tipped and analyzed by mass spectrometry. The ‘heavy’ and ‘light’ peptidisc elution fractions were pooled, denatured with 6M urea and digested with trypsin and LysC before STAGE tipping and analysis by mass spectrometry.Plasmids pBad22, pBad22-YfgM, pBad33-PpiD, and pBad33-YfgM PpiD were transformed into chemically competent BL21DE3 cells harboring the plasmid pBad22-HA-EYG. For expression of his-tagged SecYEG complex only, plasmid pBad22-his-EYG was transformed into BL21DE3 cells. Overnight cultures were prepared in LB media supplemented with appropriate antibiotics at the concentrations specified above. After an overnight incubation, the cultures were diluted 1:100 into fresh LB media with antibiotics. Protein expression was induced at OD ~0.4–0.6 by addition of 0.1% arabinose, and cultures were grown for a further 2 hr before harvesting. Cells were resuspended in TSG buffer before being lysed as described above. Membranes were prepared in TSG buffer and solubilized as described above. Solubilized material was clarified by ultracentrifugation  before incubation for 30 min with Ni-NTA affinity resin. Beads were washed in 10 CV TSG buffer + 0.02% DDM, then eluted in TSG buffer + 300 mM imidazole + 0.02% DDM. Eluted proteins were analyzed by 15% SDS-PAGE followed by either Coomassie Blue staining or a western blot using a SecY-specific antibody as previously described , and the membrane fraction was prepared and solubilized as described above. The solubilized material (~1 mL) was mixed with a 4:1 excess of Peptidisc peptide, and peptidisc libraries were prepared by the dilution and concentration method described above. The resultant library (~1 mL at 1 mg/mL) was incubated with Ni-NTA resin for 30 min at 4°C. The resin was washed with 10 CV of TSG buffer, then eluted in TSG buffer + 300 mM Imidazole. Eluted proteins were analyzed by 15% SDS PAGE and visualized by Coomassie Blue staining.Plasmids pBad33-MetQ and pBad33-MetQ C23A were transformed into chemically competent BL21DE3 cells containing the plasmid pET21-Purified peptides were analyzed using a quadrupole – time of flight mass spectrometer  on-line coupled to an Easy nano LC 1000 HPLC (ThermoFisher Scientific) using a Captive spray nanospray ionization source  including a 2-cm-long, 100 μm-inner diameter fused silica fritted trap column, 75 μm-inner diameter fused silica analytical column with an integrated spray tip . The trap column is packed with 5 μm Aqua C-18 beads  while the analytical column is packed with 1.9 μm-diameter Reprosil-Pur C-18-AQ beads . Buffer A consisted of 0.1% aqueous formic acid in water, and buffer B consisted of 0.1% formic acid in acetonitrile. Samples were resuspended in buffer A and loaded with the same buffer. Standard 45 min gradients were run from 0% B to 35% B over 90 min, then to 100% B over 2 min, held at 100% B for 15 min. Before each run the trap column was conditioned with 20 μL buffer A, the analytical – with 4 μL of the same buffer and the sample loading was set at 20 μL. When one column system was used the sample loading volume was set at 8 μL + sample volume. The LC thermostat temperature was set at 7°C. The Captive Spray Tip holder was modified similarly to an already described procedure  – the fuE. coli K12) plus common contaminants using the following parameters: peptide mass accuracy 40 parts per million; fragment mass accuracy 0.05 Da; trypsin enzyme specificity, fixed modifications - carbamidomethyl, variable modifications - methionine oxidation, deamidated N, Q and N-acetyl peptides. Proteins were quantified from one peptide identification. Only those peptides exceeding the individually calculated 99% confidence limit (as opposed to the average limit for the whole experiment) were considered as accurately identified.Analysis of Mass Spectrometry Data was performed using MaxQuant 1.5.3.30 . The seahttps://github.com/fosterlab/PrInCE-Matlab. Since PrInCE employs a naive Bayes classifier, a set of known interacting and non-interacting protein pairs are required to train the classifier, that is a gold standard set. We constructed a gold standard set of protein complexes by combining the 30S ribosome with membrane protein complexes given by the IntAct protein complex database . True positive interactions (TP) are between proteins present in the same gold standard complex, and false positive interactions (FP) are interactions between proteins present in the gold standard set but which are not members of the same complex. PrInCE calculates an interaction score for each protein pair, with higher scores indicating an interaction is more likely to be a true interaction, as measured by the proportion of gold standard TPs. Specifically, a protein pair’s interaction score is equal to the TP-to-FP ratio, measured as precision (TP/(TP +FP)) of all predicted interactions with a classifier score greater than that protein pair. Both the precision of the full list (50%) and the interaction score are directly related to an interaction false discovery rate (FDR), as FDR = 100% precision. For full implementation of PrInCE see Protein-protein interactions were predicted using PrInCE , a co-frUnlike other co-fractionation analyses, which associate protein pairs using external datasets such as gene co-citation , PrInCE We used a two-stage procedure to cluster pairwise interactions into complexes . A firstp, a ClusterONE parameter that models incompleteness in the network by assuming the existence of interactions outside of the given network; dens, a ClusterONE parameter that controls the minimum density of complexes; I, the single MCL parameter, which controls the granularity of MCL output; and S, which is the minimum interaction score of pairwise interactions fed into the clustering algorithm. The optimized values were p=5000, dens = 0.001, I = 20, and S = 0.50 .Since both ClusterONE and MCL have tunable parameters, we performed a grid search optimization to find the parameter set which maximized the matching ratio value. The optimized parameters were: i) fraction of protein pairs sharing at least one Gene Ontology (GO) term, ii) fraction of protein pairs with a positively correlated stress phenotypes as measured in the Tolerome database  fraction of protein pairs sharing at least one three-dimensional interacting domains (3did)  . Only GOE. coli cell envelope (CE) interactome  a detergent-solubilized size exclusion co-fractionation interactome (78984 interactions) and ii) an AP/MS dataset (499605 interactions). For methods of preparation, see As an additional computational validation, we determined the subset of our interactome that is also detected by two independent high-throughput interactomes. These validating interactomes are  In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.E. coli Membrane Interactome in Detergent-free Peptidiscs" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Carol V. Robinson (Reviewer #1).Thank you for submitting your work entitled "Profiling the eLife. Amongst the issues raised by the reviewers, they particularly emphasised the need for better treatment of false positive identifications.Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in Reviewer #1:eLife. The study is complementary, yet distinct from nanodiscs and SMALPS, and appears to have some unique advantages.The manuscript describes a new approach for extracting and maintaining interactions in peptidiscs. This a very interesting paper worthy of publication in Although the membrane proteome is still extracted with detergent prior to reconstitution results are similar to those obtained in SMALPs and nanodiscs. The number of unique insights obtained through the combination of methods applied here was however difficult to tease out. It seems that the most important one was that MetQ co-elutes as a complex with MetNI. MetQ appears to be the only substrate binding protein that is lipid modified at its N-terminus. This I thought particularly interesting.Of the SecYEG translocon interacting partners it as surprising to me that simultaneous over- production of the SecYEG core translocon with both PpiD and YfgM is necessary for detection of this interaction. It might suggest that original interactions which are thought to be multiple are disrupted by the initial detergent solubilisation step. Perhaps the authors could comment on this.Reviewer #2:E. coli. This is an ambitious study of potential interest that may contribute to future studies of PPI interactions of the membrane proteome. However, there are some serious problems with this study that in my opinion make it unsuitable in its current state for publication in eLife.In this report, Carlson and co-workers combine SILAC labeling, reconstitution of crude membranes using peptidiscs, size fractionation and mass-spec to characterize the interactome of the membrane proteins of From a technological perspective, the novelty is incremental: the methodologies have been used in the past, including the peptidiscs. If I understand correctly, the only novelty is the use of the peptidiscs instead of detergent or amphiphilic copolymers.From a biological perspective, all of the interactions that were used for validation have been previously described. The paper presents no new novel information.Examination of Supplementary file 3 reveals that the interactions are heavily biased towards some proteins that are highly represented, while others are completely missing. In addition, many well-characterized interactions are not detected, while other interactions, that seem non-specific false positives are "detected" .In addition, below are some specific concerns that question the validity of the findings:>The FepBCD complex is larger than MetINQ, yet the latter elutes at an earlier time. Along the same lines, the ATP synthase, which is considerably larger than both ABC transporters also elutes at a similar volume, as do BamABCD and NuoABCDEFGI. This is very suspicious. Can we be looking at aggregates? What is the void volume of this column?The suggestion that MetQ is anchored to the membrane is new and unexpected. It would greatly add to the paper if this suggestion is experimentally validated with the proper controls of an SBP that is not expected to be membrane anchored.In addition, why does this explain the co-elution with MetIN? This only suggests membrane association, and a stable transporter-SBP association.The methodology requires detergent extraction, unlike the use of amphiphilic copolymers. This possibly disturbs many of the endogenous interactions. This issue is not discussed."The end result of reconstitution is a membrane protein that displays increased stability": unsupported statement. How many cases were examined?From the width of the elution peaks of the SEC fractionation it appears that many of the interactions (the FPs) may stem from similar migrations rather than associations. This also likely explains the non-specific interactions that were detected.For removal of DDM, the authors use dilution and re-concentration in a centricon. This is a major flaw since DDM also gets concentrated in this process (this phenomenon is easily detected using sugar detection methods). So, the assumption that the preparation contains no (or little) detergent is probably wrong."Upon removal of aggregate": At what stage? what does this mean?How is the data MsbA data relevant? This study is supposed to provide proteomic information, so how is the behavior of a single protein relevant? One can relate to the other many advantages of DDM, but the behavior of MsbA is irrelevant. If the authors had chosen EmrE as their benchmark, would they extract the membranes using chloroform?"As expected, a large fraction of these proteins (591 proteins) are predicted, based on gene ontology terms, to be associated with the cell envelope". Considering that this is a membrane preparation, this is not a large fraction. The membrane proteome is supposed to be 1/3 of all proteins so this is a pretty mild enrichment factor. This is actually quite surprising and should be addressed by the authors. Is there some common theme to the non-membrane proteins that were identified? Is there a common theme to the membrane proteins that are missing?Subsection “Large membrane protein complexes are captured in the peptidisc library”: How many interactions were analyzed to support this statement?On what basis the three analyzed cases  were chosen? Was this choice arbitrary?The lack of rigorous analysis of any novel interaction is greatly missing.The 13 Type-I SBPs that were identified by the authors: if these are not membrane anchored (like MetQ) and do not stably interact with their ABC transporters, how is it that they were identified? In this respect, why were other Type II SBPs  not identified?Reviewer #3:The work by Carlson et al. describes a derivative of protein-correlation-profiling (PCP) for membrane complexomes based on stabilization of detergent-extracted membrane protein complexes by peptidiscs. Overall the paper is well written and good to follow and the experiments suited the needs.It is clearly laid out that better approaches are needed for proteomics-based identification of protein-protein interactions of membrane protein complexes. The downsides of current technologies are discussed well. The approach presented here may be an improvement over other existing methods but the based on the data presented, the improvement does not seem to be impressive. Saying this, it is surprising to see that the authors apparently claim that a precision of 50% is sufficient to call something an identified interaction. It is not surprising to see then, that only a small proportion of these 50% overlap with previous analyses and that the validating experiments validate only very few of the found interactions. Interestingly, the authors set a threshold of 80% and 75% precision, respectively, for the validation experiments. First of all, this is inconsistent within the validation experiments (75% vs. 80%) and secondly, this is inconsistent with the previously set threshold of 50%, which the authors don't seem to trust themselves. It would be very beneficial for the paper if a false discovery rate could be calculated and applied because the question clearly remains, which of the identified interactions are trustworthy and which ones not.Along the same lines, it would be good to show where the 340 overlapping interactions between this study and the one by Babu et al., 2017 rank on the graph in Figure 4C.In the validation experiments using MipA, the authors identify YajC and AtpF as potential interacting proteins. The authors should discuss whether this makes sense in the light of the membrane and complex topology of these proteins.A major current problem is the stabilization and identification of membrane protein complexes that bridge inner and outer membranes of Gram negative bacteria. The paper would benefit from presenting and discussion the subcellular localization of the identified complexes.[Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]E. coli membrane interactome captured in Peptidisc libraries" for further consideration at eLife. Your revised article has been favorably evaluated by Richard Aldrich as the Senior Editor, a Reviewing Editor, and three reviewers.Thank you for resubmitting your work entitled "Profiling the The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:Summary of paper:E. coli membrane proteome. Although other methods are available, the authors make the argument that the peptidisc approach holds advantages since it uses a membrane mimetic that is broadly applicable to membrane proteins of various sizes and to large complexes. In addition, the peptidisc scaffold protein also traps endogenous lipids that may be important for protein interactions and function. The authors benchmark their findings against interactome analysis provided via alternative approaches and demonstrate that their method is comparable, if not superior, especially for multi-component large complexes. The peptidisc interaction analysis validates known interactions but also identifies an unexpected stable interaction between the methionine substrate binding protein (MetQ) and its cognate ABC transporter (MetNI). The findings and the approach lay the foundations for future work examining the newly-identified interactions and the application of the approach to membrane-interactome analysis of other organisms.The authors report on the application of their recently developed peptidisc approach to identify protein-protein interaction of the Opinion:eLife. Major revisions are needed to address the outstanding issues listed below.The problem of identifying novel membrane protein interactions is important and interesting, and the development of new and simpler approaches is pertinent. However, in its present state, even after the previous round of revisions, it is unclear whether the peptidisc approach presents sufficient novelty to justify publication in Major issues:1) The main claim of the authors is that their approach is a useful tool to identify novel interactions of the membrane proteome. However, of the newly-identified interactions they validate a single one (MetQ-MetIN), which is also not entirely surprising based on two recent publications from the Rees lab. It would greatly benefit the paper, and convince the reader of the usefulness of the approach, if the authors show a few more examples that demonstrate novel and unpredicted interactions.2) The authors use 0.8-1% detergent (DDM) for 1 hour, yet claim this is a short time. However, most membrane proteins are fully extracted in a shorter time. Along the same lines, the authors show that the SEC and profiles obtained in DDM and in peptidiscs are similar. Since the use of detergent alone is simpler than that of the peptidiscs, it remains unclear how the presented approach is advantageous. To support this claim, the authors need to show a complete and rigorous comparison between DDM and peptidiscs. How many of the interactions identified by the peptidiscs approach would be lost if detergent only is used? The authors are encouraged to equally minimize the detergent exposure time in these experiments.3) The authors use a detergent (DDM) concentration that at the conditions used  is likely above the CMC. As a result, the detergent is probably concentrated along with the peptidisc library. The authors need to measure the DDM concentration during, and at the end of, the concentration step to convince the reader that their approach is indeed superior to the use of detergent alone.4) The authors prepare membrane fractions using ultracentrifugation, rather than density gradients or floatation assays. This leads to sedimentation of large proteins, aggregates, and complexes that are unrelated their analysis and confound the interpretation of the data. The authors must explain why they have chosen this approach and demonstrate that it does not undermine their analysis.5) Several of the concerns raised by the reviewers during the initial round of submission remain inadequately answered. For example, the identification of 13 Type-I SBPs remains unexplained. The authors need to directly and clearly address this and other concerns raised by the reviewers.6) The paper suffers from overstatement and inaccurate citations.7) The supplementary tables should be reformatted. Now they are unintelligible. [Editors’ note: the author responses to the first round of peer review follow.]Reviewer #1:The manuscript describes a new approach for extracting and maintaining interactions in peptidiscs. This a very interesting paper worthy of publication in eLife. The study is complementary, yet distinct from nanodiscs and SMALPS, and appears to have some unique advantages.We thank the reviewer for highlighting the novelty of our study. We have made substantial revisions to improve the quality of the manuscript.Although the membrane proteome is still extracted with detergent prior to reconstitution results are similar to those obtained in SMALPs and nanodiscs. The number of unique insights obtained through the combination of methods applied here was however difficult to tease out.There are important differences in the results we obtain in our side-by-side comparison between the peptidisc and SMALPs libraries. Although the solubilization efficiencies of the peptidisc and SMALPs are comparable , we observe dramatic differences in terms of the stability of multi-subunit membrane complexes. We compared the coelution profiles for three well-known complexes – the Bam complex, the respiratory chain complex and the ATP synthase complex – in both SMALPs and peptidiscs . In every case, the complexes are preserved in the peptidisc but largely dissociated in SMALPs. We have clarified this important point in the manuscript.The nanodisc was not used in this report. We have, however, recently shown that – unlike the nanodisc – the peptidisc is a universal, “one-size-fits-all” reconstitution scaffold, able to reconstitute membrane proteins of differing sizes and topologies without needing addition of exogenous lipids . In addition to streamlining the library reconstitution process, use of the peptidisc rather than the nanodisc also mitigates possible bias during the reconstitution process based on protein size. We have commented on these points in our revised Discussion.It seems that the most important one was that MetQ co-elutes as a complex with MetNI. MetQ appears to be the only substrate binding protein that is lipid modified at its N-terminus. This I thought particularly interesting.We agree with the reviewer. The finding that MetQ is lipidated and that it co-elutes with MetNI is an important and novel discovery. In this resubmission, we incorporate new data to rigorously validate these observations. Specifically: i) we validate the stability of the MetNIMetQ interaction by SILAC AP-MS  and ii) we demonstrate the importance of MetQ lipidation by demonstrating that a non-lipidated MetQ mutant does not interact with MetNI . We are also including an additional novel observation which we missed in our first submission. Our pairwise interaction list identifies NlpA (Lipoprotein 28) as a potential interactor of MetQ. We found this particularly interesting in light of a previous report which showed through complementation assays that NlpA may be able to deliver Methionine to the MetNI transporter in a MetQ-deleted strain, albeit at low efficiency . Notably, a physical interaction between MetNI-Q and NlpA has not been reported to date in the literature. Our new MetNI SILAC AP-MS data now reveal significant enrichment of NlpA along with MetQ . This observation, along with our PCP-SILAC data, provide strong evidence that a physical interaction between MetNI-Q and NlpA does exist in the cell. Altogether, this further validate the peptidisc in untargeted interactomics studies.Of the SecYEG translocon interacting partners it as surprising to me that simultaneous over- production of the SecYEG core translocon with both PpiD and YfgM is necessary for detection of this interaction. It might suggest that original interactions which are thought to be multiple are disrupted by the initial detergent solubilisation step. Perhaps the authors could comment on this.We agree with the reviewer. We are not discounting the dissociative effects of detergent on the YfgM-PpiD-SecYEG interaction. We note that Digitonin, rather than DDM, was employed in the original paper which identified this complex . It is therefore possible that an interaction between YfgM-SecYEG and/or PpiD-SecYEG would be preserved in Digitonin but dissociated in DDM (as done in our study). However, it is equally possible that strong interaction largely depends on correct stoichiometric ratio between components as our data seem to indicate . We have modified the manuscript to include a comment on this point.Reviewer #2:In this report, Carlson and co-workers combine SILAC labeling, reconstitution of crude membranes using peptidiscs, size fractionation and mass-spec to characterize the interactome of the membrane proteins of E. coli. This is an ambitious study of potential interest that may contribute to future studies of PPI interactions of the membrane proteome. However, there are some serious problems with this study that in my opinion make it unsuitable in its current state for publication in eLife.We agree with the reviewer that our initial version had certain flaws and accordingly, we have substantially revised the manuscript including additional computational and experimental validations, as well clarification both in the text and our responses above and below. However, as the reviewer also points out, this study potentially represents a major technical advance in the field of membrane interactomics (as such we submitted it as a “Tools and Resources” article).From a technological perspective, the novelty is incremental: the methodologies have been used in the past, including the peptidiscs. If I understand correctly, the only novelty is the use of the peptidiscs instead of detergent or amphiphilic copolymers.The principle novelty of our study is the combination of the peptidisc – a novel membrane mimetic system our laboratory developed recently - with the PCP-SILAC workflow. We contend that our study provides a significant technological and methodological advance for the following key reasons:i) The peptidisc solves a crucial problem in the design of detergent free protein libraries because it enables “flexible”, streamlined reconstitution of membrane proteins and complexes of varying topologies and sizes . This is in marked contrast to the nanodisc system, which may bias reconstitution toward membrane proteins and complexes of a certain size, based on the length of the scaffold protein used.ii) From our results with SMA as shown in Figure 3 and Figure 3—figure supplement 1, we can say that the peptidisc is a comparatively gentle technique that is better suited for examining native protein interactions. Many membrane protein complexes which are preserved in the peptidisc are dissociated by the SMA polymer. The reviewer is referred to our response to point 2 from reviewer #1 which addresses this topic in detail.From a biological perspective, all of the interactions that were used for validation have been previously described. The paper presents no new novel information.E. coli, because it is tethered to the membrane by an N-terminal lipid anchor. We further show that this lipidation is required for stable association between MetQ and MetNI. Furthermore, we identify an interaction and validate NlpA (Lipoprotein 28) as an interactor of the MetNI-Q complex . These are novel discoveries with profound implications for our understanding of the mechanism of MetNI-Q mediated Methionine import in bacteria. These data are nicely validating our peptidisc interactomic approach.We are presenting this study without emphasis on discovery of novel interactions, but rather as a way to rapidly assess a cell membrane interactome. Nevertheless – as stated in our response to Point 3 from reviewer #1 – we are including new data with this resubmission which reveal novel insights into the ABC transporter MetNI-MetQ. Specifically, we reveal that MetQ is a unique SBP in Examination of Supplementary file 3 reveals that the interactions are heavily biased towards some proteins that are highly represented, while others are completely missing.We agree with the reviewer that our interactome contains highly represented proteins. This likely reflects, at least in part, the true biological promiscuity of some proteins. Proteins participating in medium-to-large complexes can have dozens of interacting partners. For example, the most represented protein in our interactome, P0AEH1 (YaeL), which we detect in 114 interactions, is listed as occurring in 106 interactions in the most recent BioGRID database (https://thebiogrid.org/4261736).e.g. the number of proteins in >100 interactions as a fraction of all unique proteins in the interactome, is comparable to other interactomes: we find 1.6% of unique proteins in >100 interactions (9/562), compared to 0.4%, 1%, 0.2%, and 3% . While some interactions for highly represented proteins will be false positives, we aimed to control for the false positive rate with multiple computational validations. Regarding missing interactions and controlling for false positives, please see our response just below.In our interactome, the proportion of very highly represented proteins, In addition, many well-characterized interactions are not detected, while other interactions, that seem non-specific false positives are "detected" .E. coli,  captures 40 of the 993 known protein interactions we use as reference in our study.We agree with the reviewer that, ideally, we would capture all well-characterized interactions. Unfortunately, this is beyond the current state of interactome studies. For example, taking the CORUM database as the “full” human interactome of >80,000 interactions, well-cited interactome studies  and  capture only 3% and 7% of pairwise CORUM interactions, respectively. Similarly for Some proteins (hence interactions) are missing because they are low abundance or otherwise hard to detect with mass spectrometry (e.g. poor ionization). Importantly, though, many of the interactions we do detect are indeed well-characterized interactions, such as the ATP synthase complex, the Bam complex, and the respiratory chain complex .i) calculating the TP-to-FP ratio as measured against our reference E. coli complexes; ii) calculating how many interactions share GO terms, binding domains, and have correlated expression levels, which are measures of true, biologically interacting proteins; and iii) cross-validating our detected interactions against independent datasets, namely the cell envelope (CE) interactome  and the two validating interactomes.Because it is difficult to capture known interactions, a common strategy of interactome studies (and one we take here) is to accept non-specific false positives as the cost of detecting true interactors. It is therefore very important to estimate and report the proportion of false positives. We took pains to do that here by In addition, below are some specific concerns that question the validity of the findings:The FepBCD complex is larger than MetINQ, yet the latter elutes at an earlier time. Along the same lines, the ATP synthase, which is considerably larger than both ABC transporters also elutes at a similar volume, as do BamABCD and NuoABCDEFGI. This is very suspicious. Can we be looking at aggregates? What is the void volume of this column?Size exclusion chromatography was performed using two BioSep4000 columns (Phenomenex) connected in tandem. The total volume of the column was 18 mL and the void volume was 6 mL. The void volume is represented as the zero on the x-axis of all co-elution graphs in the manuscript. Thus, any proteins eluting past the zero point in our dataset are fully soluble. We have clarified this last point in legend of Figure 3 as it appears first.We were also puzzled by the elution behaviour of some protein complexes on the silica resin. Our main hypothesis to explain this behaviour is that certain membrane protein complexes may retain significant amounts of phospholipids, which increase the apparent molecular weight of the complex. These additional lipids may also affect the chromatographic behaviour of the proteins during size exclusion chromatography on silica – specifically, by broadening elution peaks and thus decreasing the resolution of the column. Given the broadness of the peaks we observed, we were concerned about detecting “false positive” interactions caused by co-elution of noninteracting proteins. To filter out false positive interactions caused by spurious co-elution, we incorporated information from a co-expression dataset (M3D) .The suggestion that MetQ is anchored to the membrane is new and unexpected. It would greatly add to the paper if this suggestion is experimentally validated with the proper controls of an SBP that is not expected to be membrane anchored.Reviewer #1 also asked for confirmation and validation of this important finding. Please see our response to Point 3 from reviewer #1 above. Briefly, we co-expressed MetNI with MetQ and validated the stability of the interaction by showing that MetQ co-purifies with his-tagged MetNI. In this experiment, we noticed that MetQ is localized to the membrane fraction, an observation which supports our suggestion that MetQ is lipidated.To explore the importance of MetQ lipidation for interaction with MetNI, we constructed a mutant – MetQ C23A – in which the Cysteine residue required for lipidation is replaced with an Alanine. Although this mutant is localized to the membrane fraction, it does not interact with MetNI, thus demonstrating the importance of MetQ lipidation for formation of the MetNI-Q complex. This series of experiments reveals the ability of the peptidisc-PCP-SILAC workflow for capturing and identifying novel, unexplored aspects of membrane protein complexes that are not evident using other experimental methods.In addition, why does this explain the co-elution with MetIN? This only suggests membrane association, and a stable transporter-SBP association.Our experiments using the non-lipidated MetQ C23A mutant reveal the importance of MetQ lipidation for its interaction with MetNI – please see the point above.The methodology requires detergent extraction, unlike the use of amphiphilic copolymers. This possibly disturbs many of the endogenous interactions. This issue is not discussed.We use minimal non-denaturing detergent, and importantly we rapidly remove it by replacing it with peptidisc, so that dissociation of complexes caused by detergent exposure is meant to be minimized. However, it is true that some complexes will be missed or destroyed. We are explaining this unavoidable caveat in the Discussion of the manuscript. A possible way around to this problem would to construct peptidisc libraries using three different detergents with partially overlapping solubilization solubilities .Regarding the amphipatic SMA co-polymer, we present data that rather indicate that SMA is not a “soft” solubilization agent. As shown Figure 3, some large complexes which can be isolated in the peptidisc are instead bracked apart when the membrane is solubilized with the amphiphilic SMA polymer. This drawback clearly limits the applicability of SMA as a solubilization agent for interactomics studies."The end result of reconstitution is a membrane protein that displays increased stability": unsupported statement. How many cases were examined?This statement is supported by our previous publication, as well as by unpublished results from our laboratory , in which we show a major increase in membrane protein stability upon reconstitution in peptidisc.From the width of the elution peaks of the SEC fractionation it appears that many of the interactions (the FPs) may stem from similar migrations rather than associations. This also likely explains the non-specific interactions that were detected.This is a caveat inherent to any PCP technique, and we agree that the elution peaks in this study are wider than we would have liked. However, the consequences of the wide peaks are testable: if they’re too wide, then true interacting proteins will be swamped out by spurious co-fractionating proteins, and we will be unable to predict true positives better than false positives. We did not find that this was the case. That is, we were able to predict many interactions that, through computational validation, are of comparable quality to other interactomes. Additionally to mitigate this effect, we use two columns arranged in tandem and we incorporate expression data to avoid spurious interaction identification. That is, interactions are predicted using our peptidisc co-fractionation data and the M3D expression database. In this resubmission, we have also incorporated interaction data from AP-MS studies to provide a high-confidence list of interactions. Altogether, while we agree that the wide elution peaks are in issue, we believe we’ve sufficiently accounted for them, given that our interactome looks relatively “high quality” .For removal of DDM, the authors use dilution and re-concentration in a centricon. This is a major flaw since DDM also gets concentrated in this process (this phenomenon is easily detected using sugar detection methods). So, the assumption that the preparation contains no (or little) detergent is probably wrong.The detergent is diluted below its CMC, so that monomeric detergent molecules can pass through the centricon filter. We cannot exclude the possibility that small remnant, yet non-solubilizing amounts of detergent are trapped in the peptidiscs."Upon removal of aggregate": At what stage? what does this mean?Removal of aggregated material occurs during ultracentrifugation immediately following detergent solubilization of the membrane. We have clarified this in the manuscript.How is the data MsbA data relevant? This study is supposed to provide proteomic information, so how is the behavior of a single protein relevant? One can relate to the other many advantages of DDM, but the behavior of MsbA is irrelevant. If the authors had chosen EmrE as their benchmark, would they extract the membranes using chloroform?The MsbA data is important because it shows that our peptidisc library method traps individual membrane proteins and complexes into discrete peptidisc particles, rather than non-specifically clustering them together. To show this, we overproduced a model protein (i.e. MsbA) and subsequently purified it from the library. From this, we observed efficient isolation of MsbA homo-dimer – free from contaminants – and perfectly soluble, as evaluated on SDS-PAGE and CN-PAGE respectively. This data confirms that proteins are not randomly captured into peptidiscs. We have clarified this important point in the manuscript.> "As expected, a large fraction of these proteins (591 proteins) are predicted, based on gene ontology terms, to be associated with the cell envelope". Considering that this is a membrane preparation, this is not a large fraction. The membrane proteome is supposed to be 1/3 of all proteins so this is a pretty mild enrichment factor. This is actually quite surprising and should be addressed by the authors. Is there some common theme to the non-membrane proteins that were identified? Is there a common theme to the membrane proteins that are missing?The peptidisc libraries employed in this study were prepared from crude cell membranes. The only enrichment performed to prepare the membranes was an ultracentrifugation step following cell lysis. While ultracentrifugation leads to sedimentation of cell membranes, it is well documented that large, soluble cellular complexes – such as GroEL and ribosomes – also pellet along membranes . We have clarified this point in the manuscript.Furthermore, it is well known that membrane proteins suffer from radically decreased identification rates in mass spectrometry experiments, which would further decrease the number of identified membrane proteins. The common theme among the non-membrane proteins we identify were large, soluble complexes that co-sediment with the membrane fraction .Subsection “Large membrane protein complexes are captured in the peptidisc library”: How many interactions were analyzed to support this statement?We looked at all pairwise comparisons between complex members of the three complexes . This gave 48 pairwise comparisons in the SMALP condition  and 55 comparisons in the peptidisc condition .On what basis the three analyzed cases  were chosen? Was this choice arbitrary?The validation targets were picked in large part due to their relevance with existing projects in our laboratory (Sec and ABC). The proteins YfgM and PpiD are relatively uncharacterized ancillary subunits of the bacterial Sec translocon, which is a major focus of study in our lab. As a result, many of the materials were already available to conduct these experiments (i.e. a stable SecYEG expression construct). The complex MetNI was chosen as a validation target due to the surprising observation that it co-elutes with the SBP MetQ (see above points for details). We included data on the scaffold protein MipA in our earlier submission. We now feel that inclusion of this data detracts from the main focus of our study, and have therefore removed it in this resubmission.> The lack of rigorous analysis of any novel interaction is greatly missing.Since our first submission, we have validated the MetNI-Q interaction, and we have demonstrated the importance of MetQ lipidation in this complex. This is an important and novel biological observation. Additionally, our newly included SILAC AP-MS experiment reveals the lipoprotein NlpA as an interactor of MetNI-Q. The MetNI-Q-NlpA association has only been characterized to a very limited extent in the current literature. We have now validated this novel interaction using AP-MS, which further validate the validity of the peptidisc-PCP approach.However, we would like to stress out that our major aim was to demonstrate that our method is suitable for predicting interactomes from membrane proteins. Also, PCP-SILAC, while useful for identifying novel interactions, is a powerful tool for comparative interactomics. While we did not expand this study to a third label, the work still remains a valid proof of concept for subsequent analysis based in detergent-free fractionation of a membrane preparations.The 13 Type-I SBPs that were identified by the authors: if these are not membrane anchored (like MetQ) and do not stably interact with their ABC transporters, how is it that they were identified? In this respect, why were other Type II SBPs  not identified?Type I SBPs have weak affinity for their cognate transporters, which may result in their co-sedimentation with the membrane fraction during ultracentrifugation following cell lysis. However, as reported by other biochemists including ourselves using the maltose transporter, this affinity is too weak to resist the dissociative effects of detergent during membrane solubilization. This interaction is also not restored until the transporter in stabilized in a very specific conformational state requiring non-hydrolyzable ATP analogs. This likely explains why most soluble Type 1 SBPs are eluted at the end of the fractionations and are fully dissociated from their cognate transporters.Reviewer #3:The work by Carlson et al. describes a derivative of protein-correlation-profiling (PCP) for membrane complexomes based on stabilization of detergent-extracted membrane protein complexes by peptidiscs. Overall the paper is well written and good to follow and the experiments suited the needs.It is clearly laid out that better approaches are needed for proteomics-based identification of protein-protein interactions of membrane protein complexes. The downsides of current technologies are discussed well. The approach presented here may be an improvement over other existing methods but the based on the data presented, the improvement does not seem to be impressive. Saying this, it is surprising to see that the authors apparently claim that a precision of 50% is sufficient to call something an identified interaction. It is not surprising to see then, that only a small proportion of these 50% overlap with previous analyses and that the validating experiments validate only very few of the found interactions.We agree with the reviewer that high level of false positives is an unwanted feature of interactomes. However, as explained above in response to reviewer #2, we view false positives as the cost of detecting true interactors in a high-throughput study. We believe the number of false positives needs to be estimated and controlled for, and we took care to do both of these. Further, one of the major aims in this study was to provide a larger list that researchers can reduce as needed if a higher confidence set of interactions is required. We provide two ways for researchers to refine the full published list (by interaction score and the “high confidence” subset) and we have revised our Discussion to better explain this to the reader less familiar with global interactome analysis. We believe this is the preferred method since it gives researchers more choice. That is, it is better to publish lower-scoring interactions and give researchers the option to use them or not, as opposed to not publishing them at all.E. coli reference complexes as the true interactome,  has a precision of 32%. Therefore, we note that the proportion of false positives in our study is not unusually high compared to other interactome studies.We agree that 50% precision is low, but we note that it is in the same range as other interactome studies. Defining true and false positives as we do in our study, and using the CORUM database as the true interactome, other well-cited studies  and  have precisions of 31% and 65%, respectively. Using our E. coli interactomes is significantly larger than expected by random chance . Therefore, we believe the overlap between our interactome and the E. coli interactome in  is meaningful and supports the validity of our conclusions.We agree that the overlap between our interactome and other interactomes is small in magnitude. However, we believe this does not invalidate our interactome for two reasons. First, because the full interactome of a species is estimated to be large  and interactome studies sample a small, random subset of it, overlap between interactome studies tends to be small. For example, two well-cited, high-throughput human interactome studies with 31,000 and 14,000 interactions, respectively, only have 408 interactions in common , a much smaller relative overlap than our interactome and . Second, although small, the overlap between our interactome and other Interestingly, the authors set a threshold of 80% and 75% precision, respectively, for the validation experiments. First of all, this is inconsistent within the validation experiments (75% vs. 80%) and secondly, this is inconsistent with the previously set threshold of 50%, which the authors don't seem to trust themselves.The reviewer raises a good point that, by design, using a threshold of 50% estimated precision produces an interactome with a large number of false positives. We chose this threshold because we wanted to provide a comprehensive list with fewer false negatives, at the at the cost of more false positives. Importantly and as mentioned above, we now explicitly say this in the Discussion to inform the reader. Further, we trust these interactions in proportion to their estimated precision level. Therefore we wanted to validate interactions with a higher estimated precision and to provide interpretable graphs, we used a higher cut-off for the validation experiments. Finally, from the immediately previous comment, 50% precision is in the range of well-cited interactome papers , respectively).It would be very beneficial for the paper if a false discovery rate could be calculated and applied because the question clearly remains, which of the identified interactions are trustworthy and which ones not.The estimated false discovery rate for the peptidisc interactome can be calculated from the precision (FDR = 100% – precision). We agree this is an important point, and to make this connection explicit we now include the FDR calculation in the text.Along the same lines, it would be good to show where the 340 overlapping interactions between this study and the one by Babu et al., 2017 rank on the graph in Figure 4C.Interactions that overlap between our study and the interactome published in Babu et al., 2018 tend to be higher scoring than non-overlapping interactions. We added this information to Figure 4C, and we now discuss this in the text.In the validation experiments using MipA, the authors identify YajC and AtpF as potential interacting proteins. The authors should discuss whether this makes sense in the light of the membrane and complex topology of these proteins.MipA is a lipoprotein anchored on the periplasmic side of the outer membrane. Both YajC (part of the Sec complex) and AtpF (part of the F1F0 ATPase) are inner membrane proteins with exposed loops in the periplasm. Therefore, these interactions do make sense in light of the topologies of these proteins. Please note that – as stated above in response to reviewer #2 – we have removed the MipA validation experiment in this resubmission. In light of the additional in-depth validation data we are presenting on the MetNI-Q complex, we feel that inclusion of the MipA data detracts from the main focus of the manuscript.A major current problem is the stabilization and identification of membrane protein complexes that bridge inner and outer membranes of Gram negative bacteria. The paper would benefit from presenting and discussion the subcellular localization of the identified complexes.We identify a number of interactions that bridge between the inner and outer membranes. We observe interactions between the lipoprotein Pal (outer membrane), the periplasmic protein TolB and the inner membrane proteins TolQ and TolR. More in-depth characterization of these interactions is currently underway in our laboratory. As suggested by reviewer #1, we have amended Figure 1 so that the reader readily understand our study deals with a complex network of interactions taking place across a double cell membrane.[Editors' note: the author responses to the re-review follow.][…] The problem of identifying novel membrane protein interactions is important and interesting, and the development of new and simpler approaches is pertinent. However, in its present state, even after the previous round of revisions, it is unclear whether the peptidisc approach presents sufficient novelty to justify publication in eLife. Major revisions are needed to address the outstanding issues listed below.eLife, especiallygiven the scarcity of proteomic methods that effectively enable analysis of membrane protein interactions in aqueous solution. In this response, we are including additional new data to unequivocally demonstrate the superiority of the peptidisc approach relative to detergent for stabilizing and detecting novel interactions. We are showing the following:We thank the editor and reviewers for highlighting the importance of our study. We strongly feel that our work is sufficiently novel for publication in i) We identify a novel interactor of the MetNI transporter, the lipoprotein NlpA. Using SILAC AP/MS, we demonstrate that this interaction is preserved in peptidisc and undetected in detergent .ii) We discover that MetQ is a lipidated SBP and we demonstrate the importance of this membrane anchor for association to the MetNI transporter . The role of MetQ lipidation has been overlooked in earlier studies .iii) We show that YfgM and PpiD are two membrane-embedded chaperones that interact strongly with the Sec translocon and that a proper stoichiometry is important for detection of this association . Similar to what we observe with MetNI-NlpA, these interactions are difficult to capture in detergent , which may explain why these interactions have been considered transient and detected only indirectly in the past .iv) Continuing with the Sec translocon, our pairwise interaction dataset reveals an astonishing correlation with the Bam complex. We confirm this observation using SILAC AP/MS . There significant enrichment of the BamB and BamC subunits and here also, these interactions are not detected when the same workflow is performed in detergent. Further work will be necessary to understand the interface of association, but the identification of this trans-membrane super-complex has profound implications for our understanding of outer membrane protein biogenesis. In further support, while our manuscript was under revision, a study from the Collinson group was released in bioRvix.org. Their work reports the initial isolation and a low resolution EM structure of a Bam-Sec translocon (https://doi.org/10.1101/589077). This second independent study from a leader in the field of Sec-mediated protein translocation further underscores the importance the Bam-Sec interaction we have identified.v) Continuing with the Bam complex, we show that all 5 subunits are fully captured in peptidisc in addition to two other interactors – RcsF and OmpA . These interactors were previously inferred from genetic and in vivo cross-linking experiments but never biochemically isolated . Accordingly, we show that the BamA-RcsF-OmpA interaction is less apparent in detergent than in peptidisc .We further explain these findings in our point-by-point response below. We also provide a “compare manuscript” document to show the extend of the changes we have made.Major issues:1) The main claim of the authors is that their approach is a useful tool to identify novel interactions of the membrane proteome. However, of the newly-identified interactions they validate a single one (MetQ-MetIN), which is also not entirely surprising based on two recent publications from the Rees lab. It would greatly benefit the paper, and convince the reader of the usefulness of the approach, if the authors show a few more examples that demonstrate novel and unpredicted interactions.E. coli. The recent papers from the Rees lab (Biol Chem 2015 and PNAS 2018) report on the interaction of MetQ-MetNI and high-resolution crystal structure of the complex. However, their work employs a truncated non-lipidated MetQ mutant, which was engineered to artificially increase its affinity to MetNI . Thus, our findings are novel and furthermore, they call for a re-evaluation of the role of MetQ lipid anchor for complex stability and substrate transport. In our Results, we present data showing the importance of MetQ lipidation. We co-express his-tagged MetNI with either wildtype MetQ or mutant MetQ-C23A with a disrupted lipobox. We find that only wild-type MetQ co-purifies with MetNI, thus demonstrating the importance of MetQ lipidation for the formation of the MetNI-Q complex.Our findings on MetNI-Q are novel because they reveal the importance of MetQ lipidation for association to the MetNI transporter. The role of MetQ lipidation for mediating this interaction has never been reported before, and this lipidation turns out to be a unique case among the Type I family of ABC transporters in As an additional response to the concern of novelty – and to highlight the advantage of the peptidisc over detergent – we report for the first time an interaction of the MetNI transporter with the lipoprotein NlpA . This interaction was identified in our pairwise interaction dataset with a high degree of confidence (>80%). Using SILAC AP/MS, we provide direct evidence that MetNI is interacting with NlpA. Interestingly, the same experiment in detergent fails to detect this interaction. Therefore, we confirm using two different methods (AP/MS and PCP-SILAC) that our Peptidisc workflow is able to reveal novel protein interactions. Interestingly, an earlier study back in 2003 has suggested that NlpA can deliver methionine to the MetNI transporter in a MetQ-deleted strain (7). This biological finding supports the relevance of the physical interaction between NlpA and MetNI.To provide additional examples that interactions are preserved in the peptidisc, we employ the Sec translocon. Our peptidisc interaction list reveal a number of known and unknown interactors of the Sec translocon at high degree of precision (>75%). These include the membrane-bound chaperones YfgM and PpiD. To validate these interactions, we express the SecY complex in SILAC conditions and performed AP/MS in both detergent and peptidisc. The interactions between the SecY complex and the membrane-bound YfgM and PpiD are well preserved in peptidisc but are largely disrupted in detergent.Our peptidisc data list also reveals surprising correlation between the Sec translocon subunits and the outer membrane Bam complex. Interactions between SecYEG and the BamA, BamC and BamD subunits in particular are present in our interaction list at high precision. Our SILAC AP/MS data now provides direct supporting additional evidence for this remarkable interaction. There is also strong enrichment of the porin OmpA along BamB and BamC subunits. As above with YfgM and PpiD, the interaction between the Sec translocon and the Bam complex is not evident when the same experiments are performed in detergent.The BamABCDE complex is also employed as a third validation target. We expressed BamA in SILAC labeling conditions and performed AP/MS in both detergent and peptidisc. As expected, we observe enrichment of all subunits of the Bam complex in peptidisc, which is not the case in detergent. We also observe enrichment of the inner membrane proteins YidC and YajC, both of which are ancillary subunits of the Sec translocon. This later result corroborates our earlier AP/MS results with the Sec translocon.Analysis of the Bam pairwise interaction dataset indicates two other interactors, RcsF and OmpA. An interaction of the Bam complex-RcsF has been previously reported using indirect methods such as genetic complementation and in vivo photo-crosslinking, but has not been biochemically isolated . Consistent with these previous studies, both OmpA and RcsF are significantly enriched along with BamA in our peptidisc SILAC AP/MS. When the same BamA pulldown experiment is performed in detergent, only the BamC and BamD subunits appear enriched – suggesting that the Bam complex is somewhat labile in detergent as previously reported (8). Additionally, the only other interactor that is significantly enriched is RcsF but not OmpA. These observations highlight again the advantages of the peptidisc method for preserving and identifying transient interactions.2) The authors use 0.8-1% detergent (DDM) for 1 hour, yet claim this is a short time. However, most membrane proteins are fully extracted in a shorter time. Along the same lines, the authors show that the SEC and profiles obtained in DDM and in peptidiscs are similar. Since the use of detergent alone is simpler than that of the peptidiscs, it remains unclear how the presented approach is advantageous.The time exposure (0.8-1% DDM for 1 hour) correspond to the initial solubilization step, which is performed at high protein and lipid content. The subsequent fractionation step is performed without detergent. This treatment is therefore short compared to the time exposure that occurs during fractionation in detergent conditions . We have revised the manuscript to clarify this point.We show in Figure 2 that the protein profiles in detergent DDM and in peptidisc are visually similar, as assessed by SDS-PAGE and Coomassie staining. However, mass spectrometry analysis of the detergent and peptidisc samples reveals significant differences, that is 125 IDs in detergent versus 162 IDs in peptidisc for the most protein-rich fraction. This lesser identification number in detergent is likely due to the acetone precipitation step that is required before MS analysis. Since peptidisc does not require removal of detergent before MS, the acetone step in omitted, and the method yield higher protein ID number which is especially important when dealing with low abundance or hard to detect membrane proteins.To support this claim, the authors need to show a complete and rigorous comparison between DDM and peptidiscs. How many of the interactions identified by the peptidiscs approach would be lost if detergent only is used? The authors are encouraged to equally minimize the detergent exposure time in these experiments.As mentioned above, we have included side-by-side comparisons to support our claim that the peptidisc is superior to detergent. Specifically, we have included SILAC-based AP/MS experiments to compare in a quantitative manner the detergent and peptidisc methods. From these additional datasets, it is evident that the peptidisc preserves interactions that are dissociated or simply lost due to the detergent treatment.3) The authors use a detergent (DDM) concentration that at the conditions used  is likely above the CMC. As a result, the detergent is probably concentrated along with the peptidisc library. The authors need to measure the DDM concentration during, and at the end of, the concentration step to convince the reader that their approach is indeed superior to the use of detergent alone.The peptidisc library is water-soluble since its membrane protein content can be fractionated by size-exclusion chromatography in the complete absence of detergent . We also show that the peptidisc library can be visualized on native-PAGE, in contrast to the detergent sample that forms large aggregate at the top of the gel during migration . This experimental evidence allows to conclude that detergent has been removed and replaced by the peptidisc during the reconstitution step using filtration. Evidently, the pore of the filtration device is large enough to allow detergent monomers to pass through. The dynamic behavior of detergent micelles has well known and the auto-assembly process that occur upon removal of detergent during filtration is well documented with the nanodisc.4) The authors prepare membrane fractions using ultracentrifugation, rather than density gradients or floatation assays. This leads to sedimentation of large proteins, aggregates, and complexes that are unrelated their analysis and confound the interpretation of the data. The authors must explain why they have chosen this approach and demonstrate that it does not undermine their analysis.E. coli inner membrane traditionally employs fractionation using density gradient centrifugation after initial ultracentrifugation . These additional steps serve to remove outer membranes, peripherally associated proteins and large complexes such as ribosomes (10). We did not perform these additional fractionation steps because we wanted to cover and preserve as many cell envelope protein-protein interactions as possible; that is, we wanted to preserve peripheral and outer membrane proteins along with inner membrane proteins in our peptidisc libraries. We note that the peptidisc library preparations were fractionated by size exclusion chromatography to remove protein aggregates.We agree, purification of the We did not find evidence that ribosomes undermine our analysis. In fact, large complexes such as the ribosomes are important markers for the downstream bioinformatic analysis, since these complexes are employed as gold standard reference. Our machine learning approach uses these gold standard references as training labels, and more training data provides a better-calibrated classifier and thereby improves our ability to predict interacting proteins. Filtering these nonmembrane complexes from the data would prevent their use as training data, thus reducing our ability to give confidence on our interacting membrane protein dataset.5) Several of the concerns raised by the reviewers during the initial round of submission remain inadequately answered. For example, the identification of 13 Type-I SBPs remains unexplained. The authors need to directly and clearly address this and other concerns raised by the reviewers.The abundant Type I SBPs have weak affinity for their cognate transporters, yet this affinity (and abundance) is sufficiently high to allow their co-sedimentation with the membrane fraction during initial ultracentrifugation. This explains their presence in our crude membranes stock which as mentioned above, are not purified by density gradient ultracentrifugation. The Type I SBP-transporter complex are unable to survive the downstream detergent solubilization and reconstitution steps, except when a lipid anchor exists – as is the case with MetQ. Thus, the vast majority of Type I SBPs are fully dissociated from their cognate transporters and therefore elute in the late fractions of the size exclusion chromatography.6) The paper suffers from overstatement and inaccurate citations.We have edited the manuscript to limit over-statements and we have asked expert co-authors on this manuscript to verify accuracy of the citations.7) The supplementary tables should be reformatted. Now they are unintelligible.This manuscript went thought two rounds of revisions which has led to inclusion of many additional tables. We have re-formatted some of these supplementary tables to improve clarity.References:Escherichia coli MetNIQ ABC transporter-substrate binding protein complex. Biol Chem 396, 1127-1134,1) Nguyen, P. T., Li, Q. W., Kadaba, N. S., Lai, J. Y., Yang, J. G., and Rees, D. C. (2015) The contribution of methionine to the stability of the 2) Nguyen, P. T., Lai, J. Y., Lee, A. T., Kaiser, J. T., and Rees, D. C. (2018) Noncanonical role for the binding protein in substrate uptake by the MetNI methionine ATP Binding Cassette (ABC) transporter. Proc Natl Acad Sci U S A 115, E10596-E10604.Escherichia coli. J Proteome Res 10, 1848-1859.3) Maddalo, G., Stenberg-Bruzell, F., Götzke, H., Toddo, S., Björkholm, P., Eriksson, H., Chovanec, P., Genevaux, P., Lehtiö, J., Ilag, L. L., and Daley, D. O. (2011) Systematic analysis of native membrane protein complexes in Escherichia coli. J Biol Chem 289, 19089-19097.4) Götzke, H., Palombo, I., Muheim, C., Perrody, E., Genevaux, P., Kudva, R., Müller, M., and Daley, D. O. (2014) YfgM is an ancillary subunit of the SecYEG translocon in 5) Hart, E. M., Gupta, M., Wühr, M., and Silhavy, T. J. (2019) The Synthetic Phenotype of Δ. MBio 10.6) Konovalova, A., Perlman, D. H., Cowles, C. E., and Silhavy, T. J. (2014)Transmembrane domain of surface-exposed outer membrane lipoprotein RcsF is threaded through the lumen of β-barrel proteins. Proc Natl Acad Sci U S A 111, E4350-4358.Escherichia coli specific for L- and D-methionine is the prototype for a new family within the ABC superfamily. Arch Microbiol 180, 88-100,7) Zhang, Z., Feige, J. N., Chang, A. B., Anderson, I. J., Brodianski, V. M., Vitreschak, A. G., Gelfand, M. S., and Saier, M. H. (2003) A transporter of 8) Gu, Y., Li, H., Dong, H., Zeng, Y., Zhang, Z., Paterson, N. G., Stansfeld, P. J., Wang, Z., Zhang, Y., Wang, W., and Dong, C. (2016) Structural basis of outer membrane protein insertion by the BAM complex. Nature 531, 64-69,Escherichia coli cell envelope. J Biol Chem 280, 34409-34419.9) Stenberg, F., Chovanec, P., Maslen, S. L., Robinson, C. V., Ilag, L. L., von Heijne, G., and Daley, D. O. (2005) Protein complexes of the Escherichia coli peripheral inner membrane proteome. Mol Cell Proteomics 12, 599-610,10) Papanastasiou, M., Orfanoudaki, G., Koukaki, M., Kountourakis, N., Sardis, M. F., Aivaliotis, M., Karamanou, S., and Economou, A. (2013) The


The number of datasets compiled from literature increased from 45 to 141 in ccPDB 2.0. Similarly, the number of protein structures used for creating datasets also increased from ~74 000 to ~137 000 (PDB March 2018 release). ccPDB 2.0 provides the same web services and flexible tools which were present in the previous version of the database. In the updated version, links of the number of methods developed in the past few years have also been incorporated. This updated resource is built on responsive templates which is compatible with smartphones  and large screen gadgets. In summary, ccPDB 2.0 is a user-friendly web-based platform that provides comprehensive as well as updated information about datasets.ccPDB 2.0 ( Therefore, to combat this situation, the number of sequence-based tools has been developed in the past few decades    and maindb.org/) , 46 and db.org/)  for idendb.org/)  for genedb.org/)  for geneBroadly, datasets created/compiled in ccPDB can be divided into two categories: (i) datasets for structure/function annotation at the protein level and (ii) datasets for annotation of protein at the residue level. In the case of structural annotation at the protein level, the overall function of a protein is estimated like the prediction of ATP-, RNA- and DNA-binding proteins. In case of residue level annotation, we predict the function of each residue in a protein, like the prediction of ATP-, RNA- and DNA-interacting residues in a protein.A ccPDB 2.0 back end is built using Apache HTTP server 2.2 and MySQL server 5.1.47 and front end using HTML, PHP 5.2.9 and JAVA scripts. We used an HTML5 web template for making a website compatible with mobile and tablet. The abovementioned technologies were used as they are platform independent and open source.ccPDB 2.0 is an updated and comprehensive database which maintains existing datasets obtained from the published literature and datasets derived from the PDB files. Besides, ccPDB 2.0 also allows a user to create its own customized datasets using PDB’s latest data. Functioning of the database can be broadly classified into three major sections. Details of these sections are mentioned below.This section maintains the experimentally validated and published datasets collected from the literature after an extensive search. This section was present in the previous version too. In the current database, we have included the datasets published in the past 7 years . We haveThis is an important module of the ccPDB database that allows users to create customized dataset as per their requirement. This module enables the user to create any type of dataset from the latest release of PDB (March 2018 release). This kind of dataset is very useful for benchmarking different methods and developing new method as the performance of a method largely depends on the dataset size. Different type of datasets compiled from PDB is listed in http://webs.iiitd.edu.in/raghava/ccpdb/. ccPDB 2.0 provides the same web services which were earlier present in the first version of this database. The website is compatible with different platforms like desktop, smartphone and tablet. We have also retained the previous version of the ccPDB which is maintained at http://crdd.osdd.net/raghava/ccpdb/.ccPDB 2.0 is freely available at The ccPDB 2.0 database is an updated version of ccPDB which was created to facilitate the user to access the latest information related to protein structural annotation and function. The first version of ccPDB contains very few datasets which were collected from the literature. In the previous version there was a total of 45 datasets compiled from 37 studies. However, the updated database maintains information of 141 experimentally validated datasets compiled from around 68 studies. There were a total 407 200 chains present at the protein level and 1 928 972 chains at residue level in comparison to 340 864 and 6904 chains at protein and residue level, respectively, present in the previous version. We added a new data type `Metal and Ions Interacting Residue’ which was not there in the earlier version.In the first version of ccPDB, there were only two datasets for regular and nine for irregular secondary structure prediction whereas in the updated version there are 10 and 13 datasets for regular and irregular secondary structure prediction, respectively. The number of datasets for protein–nucleotide interaction in the first version was four; however, in the updated version it has changed to 22. We also included 13 protein–metal ions and protein–acid radical ligand dataset in the updated version which was previously not present. In the previous version, the dataset number of DNA/RNA-interacting proteins at protein level was 5 and 7 at residue level whereas in the updated version it has increased to 14 and 13, respectively. Likewise, in the updated version dihedral angle prediction dataset has risen from 1 to 20, protein crystallization dataset has increased from 3 to 12, bacterial protein interaction dataset has increased from 4 to 6 and surface accessibility prediction dataset has increased from 1 to 3. This tremendous growth of datasets in the literature clearly shows the importance of protein structural annotation and function and how it could lead to better understanding of the role of proteins in various biological and cellular processes.In order to provide ready-to-use datasets for developing new prediction methods, we compiled information of 70 customized datasets from PDB using standard protocols. Datasets were assembled using the latest PDB March 2018 release which consists of ~137 000 PDB tertiary structures which is nearly double to the files present in the first version of ccPDB which consists of around 75 000 PDB structures. Due to an increase in the number of PDB structures, there was a considerable growth in the number of chains in the updated version. In the previous version, there was a total 17 731 chains at the protein level and 66 368 chains at residue level whereas in the updated version the number of chains at protein level increases to 33 488 and at residue level it changed to 139 902. The number of protein chains for regular secondary structure has been increased from 5877 to 17 608. Likewise, for irregular secondary structure there was an enormous increase in the number of protein chains for different beta turns, gamma turns, psiloop and hairpin. DNA- and RNA-interacting protein chains were increased to 560 and 415 from 417 and 282, respectively, in the updated version.http://webs.iiitd.edu.in/raghava/ccpdb/collect.php. Comparison of the statistics between ccPDB and ccPDB 2.0 is given in We also observed an increase in the number of ligand-interacting proteins and metal-interacting proteins in the updated version of ccPDB. To assist the scientific community, we created the dataset for some of the widely used protein-interacting ligands and metals. A user can download these datasets by clicking on the desired dataset present at our website ccPDB 2.0 also allows the user to create its own customized dataset in six simple steps using the `CREATION OF DATASET’ module See . These cThis study does not require any ethical clearance or any consent to participate.All data including datasets are freely available to scientific community and can be downloaded from the webserver.P.A., S.P., R.K., V.K. and P.K.R. collected and compiled the datasets. P.A. and H.S. performed the experiments. P.A., R.K., V.K.P. and S.P. developed the web interface. P.A. and G.P.S.R. analyzed the data and prepared the manuscript. G.P.S.R. conceived the idea and coordinated the project. All authors read and approved the final paper.