We expect that a comprehensive definition of protein complexes will ultimately aid our understanding of disease relations among proteins. In line with expectation, our map shows markedly increased coverage of disease‐linked proteins, especially for proteins linked to ciliopathies, a broad spectrum of human diseases characterized by cystic kidneys, obesity, blindness, intellectual disability, and structural birth defects (Hildebrandt et al , 2011 ). We highlight both known and novel complexes relevant to ciliopathies and, moreover, experimentally validate multiple new protein subunits of ciliary complexes, using in vivo assays of cilia structure and function in vertebrate embryos. Additionally, we distribute our results to the community in a simple and easy to navigate website: http://proteincomplexes.org/ . The scale and accuracy of this human protein complex map thus provides avenues for greater understanding of protein function and better disease characterization.

Here, we describe our construction of a more accurate and comprehensive global map of human protein complexes by re‐analyzing these three large‐scale human protein complex mass spectrometry experimental datasets. We built a protein complex discovery pipeline based on supervised and unsupervised machine learning techniques that first generates an integrated protein interaction network using features from all three input datasets and then employs a sophisticated clustering procedure which optimizes clustering parameters relative to a training set of literature‐curated protein complexes. While generating the complex map, we re‐analyzed AP‐MS datasets to identify > 15,000 high‐confidence protein interactions not reported in the original networks. This re‐analysis substantially increased the overlap of protein interactions across the datasets and revealed entire complexes not identified by the original analyses. Importantly, the integrated protein interaction network and resulting complexes outperform published networks and complex maps on multiple measures of performance and coverage, and represent the most comprehensive human protein complex map currently available. Moreover, the framework we employ can readily incorporate future protein interaction datasets.

Over the past year, three large‐scale mass spectrometry‐based protein interaction mapping efforts in particular have greatly expanded the set of known human protein interactions, namely BioPlex (Huttlin et al , 2015 ), Hein et al (Hein et al , 2015 ), and Wan et al (Wan et al , 2015 ), collectively comprising 9,063 mass spectrometry shotgun proteomics experiments. The three resulting datasets are notable for representing independent surveys of human protein complexes by distinct methods (AP‐MS vs. CF‐MS), in distinct samples (different cells and tissues), and in the case of the two AP‐MS datasets, using distinct choices of affinity‐tagged bait proteins. The datasets are complementary in other aspects as well: The two AP‐MS interaction sets are each sampled from a single choice of immortalized cancer cell line grown in rich cell culture medium and thus represent deep, but condition‐ and cell type‐specific, views of the interactome network. The AP‐MS networks sample only a fraction of human proteins as “baits” and are limited to interactions which contain a bait protein, which is expressed recombinantly as a fusion to an affinity purification moiety (green fluorescent proteins (GFP) for Hein et al or FLAG‐HA for BioPlex). These strategies resulted in 23,744 and 26,642 protein interactions for BioPlex and Hein et al , respectively. In contrast, the CF‐MS experiments sampled endogenous proteins in their native state without genetic manipulation, but with only partial purification, relying instead on repeat observation of co‐eluting proteins across samples and separations to increase confidence in the interactions. The resulting 16,655 protein interactions reflect the biases expected for well‐observed proteins, tending toward more abundant, soluble proteins. Additionally, the Wan et al interactome required all interactions to have evidence in at least two sampled metazoan species; thus, only evolutionarily conserved human proteins are represented. As a consequence, none of these three datasets is individually comprehensive; nonetheless, we expect them to present highly complementary, potentially overlapping views of the network of core human protein complexes. There is thus an opportunity to integrate these over 9,000 published mass spectrometry experiments in order to create a single, more comprehensive map of human protein complexes.

A fundamental aim of molecular biology is to understand the relationship between genotype and phenotype of cellular organisms. One major strategy to understand this relationship is to study the physical interactions of the proteins responsible for carrying out the core functions of cells, since interacting proteins tend to be linked to similar phenotypes and genetic diseases. Accurate maps of protein complexes are thus critical to understanding many human diseases (Goh et al , 2007 ; Lage et al , 2007 ; Wang & Marcotte, 2010 ). Technical advances in the field of proteomics, including large‐scale human yeast two‐hybrid assays (Rual et al , 2005 ; Rolland et al , 2014 ), affinity purification/mass spectrometry (AP‐MS) (Hein et al , 2015 ; Huttlin et al , 2015 ), and co‐fractionation/mass spectrometry (CF‐MS) (Havugimana et al , 2012 ; Kristensen et al , 2012 ; Kirkwood et al , 2013 ; Wan et al , 2015 ), have enabled the partial reconstruction of protein interaction networks in humans and other animals, markedly increasing the coverage of protein–protein interactions across the human proteome. Such efforts are largely ongoing, as we still lack a comprehensive map of human complexes, and we have only partial understanding of the composition, formation, and function for the majority of known complexes. Prior high‐throughput protein interaction assays in yeast and humans have generally tended to show limited overlap (von Mering et al , 2002 ; Gandhi et al , 2006 ; Hart et al , 2006 ; Yu et al , 2008 ), suggesting that interactions from different studies tend to be incomplete, possibly error‐prone, but also orthogonal.

Results

Overlap between three recent high‐throughput animal protein interaction datasets is modest, but can be greatly increased by a re‐analysis of the data Protein interaction networks from various sources often show minimal overlap (von Mering et al, 2002; Gandhi et al, 2006; Hart et al, 2006). We therefore first sought to measure the overlap of proteins and interactions between three recently published protein interaction datasets from BioPlex (Huttlin et al, 2015), Hein and colleagues (Hein et al, 2015), and Wan and colleagues (Wan et al, 2015). The BioPlex network is the result of 2,594 AP‐MS experiments from HEK293T cells. Similarly, the Hein et al network is the result of 1,125 AP‐MS experiments from HeLa cells. In both screens, the authors considered only interactions between the affinity‐tagged bait protein and the co‐precipitated “prey” proteins, corresponding to a “spoke” model of interactions (Fig 1A). The Wan et al network is derived from a CF‐MS analysis of nine organisms, comprising 6,387 MS experiments. Figure 1.Re‐analysis of published AP‐MS experiments improves overlap among protein interaction networks Graphical schematic of spoke model applied to AP‐MS datasets. In the spoke model, all interactions must include a bait protein. Venn diagram of overlap between published large‐scale protein interaction networks BioPlex (AP‐MS), Hein et al (AP‐MS), and Wan et al (CF‐MS). Protein interactions in BioPlex and Hein et al were generated from a spoke model. Graphical schematic of matrix model applied to AP‐MS datasets. In the matrix model, interactions are allowed between prey proteins. Venn diagram of overlap between protein interaction networks where a weighted matrix model was applied to BioPlex and Hein et al. Sizes of weighted matrix model protein interaction networks were kept constant with published networks (for this analysis only while the full networks were used for integration). Note an increase in the overall number of overlapping interactions when compared to (B). Diagram of protein complex discovery workflow. Three protein interaction networks, BioPlex, Hein et al, and Wan et al, were combined into an integrated protein complex network and clustered to identify protein complexes. Parameters for the SVM and clustering algorithms were optimized on a training set of literature‐curated complexes and validated on a test set of complexes. We observe reasonable overlap in terms of the proteins identified within each published network, ranging between 30 and 68% of the proteins between individual networks (Table EV1). However, the overlap among protein interactions was more limited, ranging between ~3 and ~6% overlap (Fig 1B and Table EV1). There are generally three accepted reasons for the limited overlap commonly observed between large‐scale protein interaction maps (von Mering et al, 2002): (i) the interaction networks sample different portions of the interactome (e.g., differences in cell types and baits), (ii) the experimental methods used are biased toward discovery of certain classes of interactions (e.g., soluble vs. membrane protein interactions) and therefore are complementary to the other methods, and (iii) the experimental methods produce false‐positive interactions. To further probe the reason for the limited observed overlap, we next considered whether the spoke model interpretation of the AP‐MS experiments was partly responsible. By only considering interactions between bait proteins and their preys, spoke models are heavily reliant on the baits selected for experimentation, and also ignore evidence for repeated precipitation of intact complexes across baits. Traditionally, spoke models have shown higher accuracy when compared to the alternative full “matrix” model interpretation (Fig 1C) (Bader & Hogue, 2002). However, the discrimination between true and false protein interactions can be dramatically improved by computing confidence scores for prey–prey interactions when applying a matrix model (Hart et al, 2007; Wang et al, 2009) or a hybrid spoke–matrix model (e.g., socio‐affinity index) (Gavin et al, 2006) to AP‐MS data. In order to reinterpret the AP‐MS datasets using a matrix model while effectively discriminating true‐ and false‐positive interactions, as well as suppressing “frequent flyer” co‐purifying proteins, we applied a hypergeometric distribution‐based error model to the AP‐MS datasets, calculating P‐values for pairs of proteins that were significantly co‐precipitated more often than random across AP‐MS experiments, herein referred to as weighted matrix model. Figure EV1 illustrates a hypothetical example of protein interactions scored using the weighted matrix model and effectively discriminating true from false positives. We then ranked each protein pair according to its calculated P‐value and selected the top N pairs for each AP‐MS dataset, where N is the number of interactions reported in the original published interaction networks (23,744 BioPlex interactions and 26,642 Hein et al interactions). This reinterpretation of AP‐MS experiments using a weighted matrix model substantially increased the amount of overlap among the three interaction networks, which rose to between 10 and 15%, as plotted in Fig 1D (see also Table EV1). This result indicates that there are thousands of interactions captured by the AP‐MS experiments that were not previously identified and confirms a far greater consistency among the underlying mass spectrometry datasets, arguing that a combined analysis of the datasets could considerably improve coverage of the complete human protein interactome. Click here to expand this figure. Figure EV1.Hypothetical example of co‐complex interactions being scored by weighted matrix model Graphical depiction of six AP‐MS experiments highlighting the purification of two mutually exclusive complexes A‐B‐C‐D and A‐E. Experiments 1, 2, and 3 co‐purify interactions with bait proteins A, D, and E, respectively. Experiments 4, 5, and 6 show non‐specific interactions for the complexes or sub‐complexes. For the purposes of this example, proteins A, B, C, D, and E are only observed in these six experiments out of a set of 50 experiments (arbitrarily defined). Presence–absence matrix for the six AP‐MS experiments and five proteins described in (A). This matrix represents what the experimenter observes after mass spectrometry analysis. Calculation of weighted matrix model score for protein pairs in highlighted complexes. The True Interaction column represents whether the pair of proteins is co‐complex or not. The Spoke Model column represents the predictions made by the traditional spoke model. Note the spokes model's false‐negative prediction of interaction B&C. The Number of co‐purifications column represents the number of experiments for which the pair of proteins is co‐purified. The Weighted Matrix Model column represents the −1*log(P‐val) of the hypergeometric test given the experimental overlap value, each protein's total number of observed experiments, and the total number of experiments (non‐depicted) arbitrarily defined as 50. The panel also shows likely clusters of co‐complex interactions using three levels of confidence, high, medium, and low. Note, the high‐ and medium‐confidence networks do not show the false‐positive interactions D&E, C&E, or B&E but do capture the true‐positive prey–prey interaction B&C.

Integrating the large‐scale proteomics datasets into a human protein–protein interaction network Based on the notion that considering this large and diverse set of experiments jointly should increase the ability to discriminate between true and false protein interactions, we next asked whether integrating all three large‐scale datasets would outperform the individual networks in terms of identifying true human protein interactions. We employed a formal machine learning framework to combine evidence from the thousands of individual mass spectrometry experiments in the three large‐scale datasets. Our approach was specifically designed to address the limited network overlap described above, using the weighted matrix model to increase interaction coverage while preserving accuracy. We expected the orthogonal techniques employed, CF‐MS and AP‐MS, to complement each other, where CF‐MS captures stable interactions among endogenous proteins in diverse cells and tissues, while AP‐MS captures a large collection of interactions with differing biophysical characteristics. The three datasets also sample very different portions of the human interactome in terms of cell type and bait selection, which we similarly expected to contribute to a more comprehensive map. Figure 1E outlines the pipeline used for protein complex discovery. We first generated a feature matrix using the raw untrained published features from BioPlex, Hein et al, and Wan et al as well as the new weighted matrix model features, in the form of a negative log hypergeometric P‐value capturing the specificity and extent to which pairs of proteins co‐precipitated across many AP‐MS baits. Rows in the feature matrix represented pairs of proteins and columns represented measured numerical estimates of protein pairs’ interaction potentials based on the different experiments. All protein interaction features were calculated from raw experimental data, and to avoid any circularity, no features trained on our gold standard were used (see Materials and Methods). We also labeled protein pairs according to their support by a gold standard, literature‐curated set of human protein complexes [the CORUM protein complex database (Ruepp et al, 2010)]. We assigned a positive label if both proteins were seen in the same complex, a negative label if both proteins were observed in the literature‐curated set but not in the same complex, and an “unknown” label for all other pairs. A support vector machine (SVM) classifier was trained using the labeled feature matrix, then applied to all protein pairs, assigning each pair an SVM confidence score, indicating the level of support for that pair of proteins to participate in the same complex. This classification step thus resulted in an integrated human protein–protein interaction network, in which the nodes are proteins identified in any of the three experimental datasets, and the edges between nodes represent co‐complex interactions weighted proportionally to the SVM score. As an initial estimate of the quality of the integrated human protein interactions, we calculated their precision and recall by reconstructing a set of 15,687 gold standard, literature‐curated co‐complex interactions omitted from the training procedure. While networks generated using features from only one of the three datasets showed high precision for high‐confidence interactions, they quickly dropped in precision in the higher recall range (Fig 2A). In contrast, the integrated network demonstrated substantial improvements to performance, with a precision of 80% over just under half of the benchmark interactions. Additionally, adding the weighted matrix model features to the published interactions greatly improved the performance, indicating that the weighted matrix model features capture new information beyond spoke features and serve as a rich source of evidence supporting true protein interactions (Figs 2A, and EV2A and B). Figure 2.Integration of the three large‐scale protein complex datasets substantially improves both precision and recall of known human protein interactions Precision–recall curves calculated on a leave‐out set of protein interactions from literature‐curated complexes for different combinations of predictive protein interaction features. The integration of all three datasets outperforms all other networks. Also, note a substantial improvement in performance when the weighted matrix model features are used (no MatrixModel, blue vs. integrated, orange). Performance of parameter optimization for MCL and Newman two‐stage clustering procedures. Each data point represents a set of parameters and is evaluated based on the resulting clusters similarity to both training and test sets of complexes using the F‐Grand measure (see Materials and Methods). Final parameter sets were selected based only on F‐Grand measure for the training set. Precision–recall curves evaluating protein interactions on leave‐out set before (integrated) and after (hu.MAP) clustering procedure. Note an improvement in performance after clustering suggests the clustering procedure successfully removed false‐positive interactions. Distribution of protein interactions in the final protein interaction network based on input evidence. Note the weighted matrix model interactions produce many high‐confident interactions. Also, the “Multiple” category shows predominately high‐confident interactions, which validates the integration of multiple datasets mitigating false positives. Protein interactions from our complex map substantially overlap with other protein interaction datasets across a variety of experimental types. Click here to expand this figure. Figure EV2.Performance evaluation of networks with alternative feature sets and hu.MAP compared to leave‐out set of co‐complex interactions and complexes Precision–recall curves calculated using leave‐out set of co‐complex interactions to evaluate networks trained on BioPlex only and BioPlex + weighted matrix model features. Note improvement of performance when weighted matrix model features are included. Precision–recall curves calculated using leave‐out set of co‐complex interactions to evaluate networks trained on Hein only and Hein + weighted matrix model features. Note improvement of performance when weighted matrix model features are included. Precision–recall curves calculated using leave‐out set of co‐complex interactions to evaluate networks trained on all features (integrated, orange), all features except HumanNet features SC‐LC, SC‐CC, CE‐LC, and CE‐CC (dashed blue), and all features except HumanNet (green). Note negligible performance loss when HumanNet features are excluded. Comparison of hu.MAP and published complex maps to leave‐out set of complexes using precision–recall product measure (Song & Singh, 2009 Comparison of hu.MAP and published complex maps to leave‐out set of complexes using F‐weighted k‐clique score. Distribution of number of subunits for complexes in hu.MAP. Previous studies using proteomics data for interaction identification saw gains in performance when non‐physical data (co‐expression, co‐citation, etc.) were included in training. Specifically, Wan et al (2015) included HumanNet (Lee et al, 2011) features (only for protein pairs when there was also evidence in the co‐fractionation data), which showed a boost in performance. Since we used features from Wan et al, we wanted to test the value of the non‐physical data in our pipeline. Figure EV2C shows precision–recall curves for interaction networks trained without literature‐based evidence from HumanNet as well as a network trained without all of HumanNet. Negligible performance loss is observed when HumanNet is removed, suggesting large‐scale human protein interaction datasets have reached a sufficient point where adding in supporting non‐physical interaction information is no longer necessary to support protein interaction discovery.

Clustering pairwise interactions reveals human protein complexes A hallmark of protein complexes is that their component proteins should frequently be co‐purified in independent separations and affinity purifications. This trend manifests as densely connected regions of the interaction network, which we sought to identify by applying a two‐stage clustering procedure. In the first stage of clustering, we applied the ClusterOne algorithm (Nepusz et al, 2012), which identifies large, dense sub‐networks of the full protein interaction network. Importantly, ClusterOne allows proteins to participate in more than one sub‐network as dictated by the data, as proteins frequently participate in more than one complex (Wan et al, 2015). In the second stage, we separately applied MCL (Enright et al, 2002) and Newman's hierarchical clustering method (Newman, 2004) to further refine the sub‐networks produced by ClusterOne. As with many unsupervised machine learning techniques, clustering algorithms have adjustable parameters for optimizing their performance. We therefore used a parameter sweep strategy to identify choices of parameters that best recapitulated known complexes. We evaluated each parameter combination by comparing the resulting protein clusters to our literature‐curated training set of protein complexes and selected the top‐ranking parameter combination. As the comparison of protein complexes to a gold standard set is not a fully solved problem, we first developed an objective scoring framework for complex‐level precision and accuracy, called k‐cliques as we describe in the Materials and Methods. This method allows us to compare predicted sets of complexes to a gold standard to evaluate their similarity on a global level. We computed the performance in terms of reconstructing known complexes for each of > 1,000 different clustering algorithm parameter combinations, varying the SVM confidence threshold for the input pairwise protein interactions, the ClusterOne density and overlap options, and the inflation option for MCL. The top‐scoring sets of clusters for the two second‐stage clustering methods, MCL and Newman's hierarchical method, were of similarly high quality when evaluated relative to the training set of complexes (Fig 2B). These two top‐scoring cluster sets also showed the top‐ranking scores when compared to the literature‐curated leave‐out test set for their respective clustering methods, serving to validate the parameter optimization method. As the two top‐scoring cluster sets identified many distinct specific complexes and sub‐complexes, we combined these two top‐scoring definitions of complexes in order to provide a more comprehensive view of the myriad of physical protein assemblies in human cells. The resulting fully integrated human protein complex map, called hu.MAP, consists of 4,659 complexes, 56,735 unique co‐complex interactions and 7,777 unique proteins (Tables EV2 and EV3).

The integrated map improves pairwise interaction performance, identifies new interactions, and is strongly supported by independent protein interaction datasets We wished to assess the quality of the integrated map of human protein complexes by multiple, independent approaches. First, because the process of network clustering entails removing interactions between proteins that are inconsistent with the defined complexes, we might expect the resulting clustered network to be more accurate than the pre‐clustered network. Indeed, the final interaction network shows improved precision and recall (Fig 2C), indicating that the clustering step is preferentially removing false positives from the original network. Next, during the course of identifying protein interactions and complexes, we withheld a leave‐out set of literature‐curated complexes to serve as a final, fully independent test set. We compared these data to the derived map and to previously published complex maps, using two different comparison measures (Fig EV2D and E). For both the k‐clique metric and the precision–recall product measure (Song & Singh, 2009), we observed a dramatic improvement in performance over the Wan et al and BioPlex maps (note: Hein reported only interactions, not complexes). We also observe in Fig EV2F a broad distribution of complexes with various numbers of subunits in our map, with 2,991 (64% of the total) having greater than two subunits, suggesting that our clustering procedure is capable of identifying the full range of complex sizes. A survey of evidence supporting each interaction in the map showed multiple lines of evidence supported many pairwise interactions (Fig 2D). This further supports the notion that the underlying datasets are orthogonal and that integrating them provides substantial improvement on discriminating true and false protein interactions. Remarkably, however, we observed tens of thousands of interactions in the map supported only by weighted matrix model features, 15,454 of them having very high confidence (SVM score > ~0.27, see Materials and Methods). Thus, considering prey–prey interactions in the AP‐MS datasets dramatically enhanced the identification of human protein interactions. Finally, in order to assess the quality of the final map independently of both the test and training set complexes, we further evaluated our complex map with several of the largest remaining available human protein interaction datasets. We observed highly significant overlap with protein interactions from different experimental methods, including yeast two‐hybrid assays (Rolland et al, 2014), additional unpublished BioPlex AP‐MS experiments (BioPlex), and cross‐linking mass spectrometry performed on human cell lysate (Liu et al, 2015) (Fig 2E). Thus, comparisons with independent datasets strongly support the high quality of the derived protein complexes, as measured by multiple metrics of performance, considering interactions both pairwise and setwise, and even considering interactions measured independently by multiple different technologies. The significant overlap of our complex map with these other datasets also points toward the potential value of integrating these datasets using the pipeline described here to further improve coverage of the human protein interactome.

Prey–prey interactions reveal a large, synaptic bouton complex, isolated from HEK cells The thousands of additional high‐confidence interactions contributed by prey–prey co‐purification patterns led us next to consider their value in our protein complex discovery pipeline. In particular, we asked whether weighted matrix model edges could independently identify complexes, or whether they only served to support observed bait–prey associations. We thus searched for complexes in the map that were supported predominantly with weighted matrix model interactions. Figure 3A summarizes AP‐MS experiments for four example complexes. Three of these complexes—the exosome complex, eukaryotic initiation factor 3 (eIF3) complex, and the 19S proteasome—were supported by both spoke edges and weighted matrix model edges, showing high complementarity between the two sets of interactions. This support was evident in the strong interaction density both between bait proteins and between bait and prey proteins within each complex. In contrast, the fourth complex shown in Fig 3A is a newly identified complex by our pipeline that surprisingly has limited density between bait proteins, but substantial, high‐specificity density in the prey region of the matrix. Notably, the four bait proteins that each precipitates nearly all 60 subunits of this complex largely do not co‐precipitate each other. Figure 3.Weighted matrix model edges identify large synaptic bouton complex Presence/absence matrix of BioPlex AP‐MS experiments as rows and pulled down proteins as columns for four complexes identified in our complex map. The Exosome, eIF3 Complex, and 19S Proteasome all have multiple bait–bait interactions whereas the novel synaptic bouton complex does not have bait–bait interactions but does have substantial density in the non‐bait region of the matrix. This density is identified by the weighted matrix model and highlights the model's ability to discover protein complexes. RNA expression profiles of proteins in the synaptic bouton complex across different tissues sampled by the Human Protein Atlas. This shows the complex is highly specific for cerebral cortex tissue. No less than six replicates were used for each tissue type. Boxes indicate median (inner band), first quartile (bottom) and third (top) quartile. Whiskers indicate 1.5 interquartile range. Dots indicate outliers. Correlation coefficient distributions of Allen Brain Map tissue expression profiles between synaptic bouton complex proteins and random set of proteins. This shows coherence of expression among proteins in the complex suggesting a functional module. Significantly enriched Gene Ontology annotations for proteins in the synaptic bouton complex shows enrichment for neuron development and synaptic transmission. We performed annotation enrichment analysis to establish functional connections between member proteins of this novel complex. Strikingly, the proteins identified in this complex are highly specific for cerebral cortex tissue, as measured by Human Protein Atlas tissue expression data (Uhlén et al, 2015) (Fig 3B). We additionally observed high brain‐region‐specific co‐expression among members of the complex, unlike as for random protein pairs, in the Allen Brain Map microarray dataset (Hawrylycz et al, 2012) (Fig 3C). The complex includes subunits of the SNARE complex, a known physically associated set of proteins involved in synaptic vesicles (Südhof, 1995). Consistent with this trend, we found a strong enrichment of Gene Ontology terms (GO) (Ashburner et al, 2000) among members of the complex specific to neurotransmission and neuron migration (Fig 3D). Thus, there is good correspondence between this complex and known interacting protein complexes at the synaptic bouton, the presynaptic axon terminal region containing synaptic vesicles, and the location of neuronal connections. We next wanted to consider the possibility that the set of synaptic bouton complex proteins were co‐precipitating due to their enclosure in a membrane‐bound organelle rather than making physical co‐complex interactions. We therefore looked at the expected number of proteins we would find given the purification of a synaptic vesicle. Out of the 131 proteins annotated with the GO term Synaptic Vesicle (GO:0008021), 66 are found in complexes in hu.MAP, but only 12 are found in the synaptic bouton complex. This low recovery of synaptic vesicle proteins suggests that the synaptic bouton complex is not exclusively vesicle‐bound components. Rather surprisingly, the AP‐MS experiments that support this complex were all performed with HEK293T cells. HEK293T cells were first reported to be derived from human embryonic kidney tissue (Graham et al, 1977), and therefore, it is puzzling as to why a complex comprised of cerebral cortex‐specific proteins showed such a strong signal in kidney‐derived cells. However, re‐analyses of HEK293T cell origins suggest that they were originally mis‐annotated and actually derive from adjacent human embryonic adrenal tissue, rather than embryonic kidney cells (Shaw et al, 2002; Lin et al, 2014), and thus exhibit many neuronal properties (Shaw et al, 2002). The possibility remains open that the protein complex identified here could also have additional roles in the body. Nevertheless, this complex exemplifies the value of prey–prey interactions for discovering protein complexes.

The integrated map markedly improves coverage of disease‐linked protein complexes A key application of more accurate human protein complex maps will be to highlight and characterize biologically important protein modules, especially those relevant to human disease. We thus next evaluated the map in reference to a variety of localization, functional, and disease annotation datasets. First, we annotated proteins in hu.MAP with information about their human tissue expression patterns from the Human Protein Atlas (Uhlén et al, 2015). We observed a substantial portion of proteins in our map expressed across all assayed tissues, suggesting our map captures many core processes in human cells (Fig 4A), although many tissue‐specific complexes appear to be identified as well, as for the example of the synaptic bouton complex in Fig 3. Figure 4.hu.MAP consists of predominately core human complexes and covers a large fraction of disease genes Complex map coverage of Human Protein Atlas RNA tissue specificity classifications showing majority of complexes are ubiquitously expressed and likely core cellular machinery. Fraction of complexes with significantly enriched annotation terms (g:Profiler hypergeometric test with FDR (Benjamini–Hochberg) correction on each complex and further corrected at an FDR of 5% given a set of shuffled complexes; see Materials and Methods) from various ontologies. Protein coverage of high‐level Disease Ontology terms and cilia‐related annotations for complex map as well as three published maps (Wan et al, BioPlex, Boldt et al) and three published interaction network (Hein et al, Gupta et al and Boldt et al). We next evaluated the fraction of complexes with significantly enriched annotations [FDR‐corrected hypergeometric test; g:Profiler (Reimand et al, 2016)] from the Gene Ontology, Reactome, CORUM, OMIM, KEGG, and HPA annotation databases (Ashburner et al, 2000; Ruepp et al, 2010; Kanehisa et al, 2014; Amberger et al, 2015; Uhlén et al, 2015; Fabregat et al, 2016). While nearly all of the complexes in hu.MAP (4646/4659) have at least one significantly enriched annotation when searched individually (see Table EV4 for full list of each complexes’ significantly enriched annotation terms), in order to better estimate annotation enrichments considering the > 4,000 distinct complexes being tested and the non‐uniform complex size distribution (Fig EV2F), we additionally estimated the occurrence of significant enrichment by chance after permuting protein memberships in complexes while maintaining the observed distribution of complex sizes. Figure EV3 shows the distribution of the largest ‐log(P‐values) (i.e., most significant annotation) for each complex for both hu.MAP and the shuffled complexes. Figure 4B reports the set of hu.MAP complexes with a significantly enriched annotation at a false discovery rate of 5% with respect to the shuffled set of complexes. Greater than 40% (1,880 out of 4,659) of the complexes had at least one significantly enriched annotation term, demonstrating the biological pertinence of complexes in the map, well in excess of shuffled complexes of the same sizes. While many complexes of size 2 and size 3 were included in the set of 1,880 annotation‐enriched complexes, larger complexes were increasingly more likely to show functional enrichment, with 1,514 enriched complexes containing three or more subunits. Click here to expand this figure. Figure EV3.Distribution of most significant annotation for each complex in hu.MAP and shuffled complexes P‐value) for each complex in hu.MAP and shuffled complexes as calculated by g:Profiler. hu.MAP has a substantially higher number of complexes with highly enriched annotations than permuted complexes. This distribution was used to calculate the false discovery rate of 5% in Fig We selected the largest −log(‐value) for each complex in hu.MAP and shuffled complexes as calculated by g:Profiler. hu.MAP has a substantially higher number of complexes with highly enriched annotations than permuted complexes. This distribution was used to calculate the false discovery rate of 5% in Fig 4 B. Knowledge that a protein interacts with a disease‐associated protein greatly increases the probability that the first protein is linked to the same disease (Dudley et al, 2005; Fraser & Plotkin, 2007; Lage et al, 2007; McGary et al, 2007; Ideker & Sharan, 2008). Thus, we expect an important application of this map will be to enable the discovery of candidate disease genes. In order to estimate this strategy's potential, we compared the map's coverage of known disease‐associated proteins with other published networks. Figure 4C shows the fraction of proteins annotated in the Online Mendelian Inheritance in Man (OMIM) disease gene database, mapped according to eight high‐level Disease Ontology (DO) terms (Schriml et al, 2012) for four complex maps [i.e., hu.MAP, Wan et al, BioPlex, and a targeted cilia map from Boldt et al (2016)]. We also evaluated three interaction networks (which serve to increase its proteome coverage) specifically the full Hein et al interaction network and the two targeted interaction networks of Gupta et al (2015) (centrosomal) and Boldt et al (cilia). hu.MAP shows substantially higher coverage than the other networks for nearly all high‐level DO terms, covering ~46% of the annotated human disease‐associated proteins.

New components of ciliary protein complexes One specific class of diseases in particular stood out, namely diseases related to defective cilia, known as ciliopathies. Cilia are microtubule‐based cellular protrusions that are critical for cell‐to‐cell signaling (Eggenschwiler & Anderson, 2007; Oh & Katsanis, 2012) and proper embryonic development (Goetz & Anderson, 2010; Oh & Katsanis, 2012). Cilia assembly and maintenance are highly regulated processes whose disruption can lead to debilitating birth and early childhood disorders, including Joubert syndrome, Meckel syndrome, Bardet–Biedl syndrome, orofaciodigital syndrome, and polycystic kidney disease. Although many ciliopathies share clinical presentations such as kidney and liver dysfunction, other clinical features and their severity can vary considerably across individuals (Gerdes et al, 2009; Tobin & Beales, 2009; Hildebrandt et al, 2011). The resulting confounding array of clinical features, an absence of cures, and limited but expensive treatments all lead ciliopathies to collectively represent a major health burden (Tobin & Beales, 2009). Protein complexes are integral to many ciliary and centrosomal processes and have major roles in ciliopathies (Gupta et al, 2015; Boldt et al, 2016). To more directly assess hu.MAP's relevance to ciliopathies, we measured its coverage of ciliopathy‐associated proteins (OMIM‐annotated proteins mapped onto the mid‐level Disease Ontology term “ciliopathy”) and known ciliary proteins [literature‐curated as the SysCilia “Gold Standard” (van Dam et al, 2013)] (Fig 4C). For both ciliopathy‐associated and ciliary proteins, we observed a substantial increase in coverage over other general networks and complex maps, with hu.MAP covering > 50% of ciliary proteins. Additionally, when compared directly to the more targeted networks and complex map of Gupta et al and Boldt et al, our map is comparable with regard to “ciliopathy” proteins and exceeds coverage with regard to SysCilia proteins (Fig 4C). Due to our map being proteome‐wide, we anticipate similar levels of performance across many other specific disease types. An examination of individual complexes enriched with ciliary proteins highlighted both known and novel ciliary components. hu.MAP reconstructed multiple known ciliary protein complexes including the Intraflagellar Transport particles A and B (IFT‐A and IFT‐B) (Piperno & Mead, 1997; Cole et al, 1998), the Bardet–Biedl‐linked BBSome (Nachury et al, 2007), the CPLANE ciliogenesis and planar polarity effector complex (Toriyama et al, 2016), and the CEP290‐CP110 complex (Tsang et al, 2008) (Fig 5). In all, the map contains 234 complexes and sub‐complexes involving 158 ciliary proteins (Table EV5), many associated with ciliopathies (den Hollander et al, 2006; Beales et al, 2007; Chetty‐John et al, 2010; Walczak‐Sztulpa et al, 2010; Schaefer et al, 2014; Toriyama et al, 2016). Moreover, we observed many of these complexes to also contain additional uncharacterized proteins. These novel proteins represent excellent candidates for ciliary roles including potential links to ciliopathies and points to the broad use of hu.MAP for associating uncharacterized genes to disease phenotypes based on co‐complex interactions. We therefore next focused on detailed experiments to characterize novel proteins’ in vivo functions and subcellular localization in developing vertebrate embryos. Figure 5.Select complexes in the map are strongly linked to human ciliopathies et al, 2015 2008 et al, 2011 et al, 2015 et al, 2016 Cystic kidney phenotype represented by polycystic kidneys from patient with OFD1 variant, adapted from Chetty‐John et al ( 2010 Digit malformations represented by polydactyly of Bardet–Biedl syndrome patient with LZTFL1 (BBS17) variant, adapted from Schaefer et al ( 2014 Short‐rib phenotype represented by chest narrowing of Jeune asphyxiating thoracic dystrophy individual with IFT80 variant, adapted from Beales et al ( 2007 Maculopathy represented by retinitis pigmentosa of Senior–Loken syndrome patient with mutation in WDR19 (Coussa et al, 2013 Eight complexes are highlighted with ciliopathy‐linked subunits (bold outlines), predicted ciliopathy subunits (dashed outlines), and their association with four representative ciliopathy phenotypes (A–D). We predict links to ciliopathies for uncharacterized proteins (green) that are co‐complex with known ciliopathy genes. All edges to ciliopathy phenotypes are mapped from OMIM (Amberger) or direct from literature (Krock & Perkins,; Keady; Chang; Toriyama).

Observation of an 18‐subunit ciliopathy‐linked complex enriched in centrosomal proteins Among the ciliary complexes, we identified a large, 18‐subunit complex in which eight subunits were already linked to ciliopathies and 14 members were known to localize to the centrosome centriolar satellites (Figs 5 and 6A). A second 8‐member complex was observed interacting with subunits of the first complex, also containing centrosome‐localized and ciliopathy‐linked proteins (Fig 6A). Figure 6B plots the AP‐MS observations that supported the discovery of these complexes. We observed strong evidence for physical associations among members in each complex, with many edges supported by our weighted matrix model as well as affinity purification of substantial portions of each complex by bait proteins from multiple datasets. Centrosomes are the microtubule organizing centers of cells, with dual roles in chromosomal movement and organization of the ciliary microtubule axonemes. Thus, the marked enrichment of centrosomal/centriolar satellite and ciliopathy proteins in these two complexes strongly implicates a relationship between centriolar satellites and ciliary‐related disease. Figure 6.Oro‐facial‐digital syndrome 1 (OFD1) interaction partners are centriole and centriolar satellite proteins, suggesting new components of ciliary basal bodies Network of ciliopathy complex and closely interacting centrosomal complex. Edge weights represent SVM confidence scores where gray are intracomplex edges and purple are inter‐complex edges. Color of nodes follows Fig 5 conventions. Matrix of AP‐MS evidence supporting both complexes. The matrix shows strong support for interactions within each complex. Bait proteins that are members of either complex are labeled on the left. Experimental validation of ciliary proteins using multi‐ciliated epithelial cells in Xenopus laevis. Localization assays for the three uncharacterized proteins in the OFD1 complex confirm that all three proteins localize to basal bodies at the base of the cilia in a manner similar to known components of the complex. Scale bars: 1 μm. Each image is representative of nine cells from three different embryos. Three of the 18 proteins in the larger complex were completely uncharacterized (WDR90, CCDC138 and KIAA1328), so we determined the subcellular localization of tagged versions of these proteins as a direct experimental test of the map's prediction. We expressed proteins in this complex as GFP fusions in multi‐ciliated cells (MCCs) of embryos of the frog Xenopus laevis, as these cells provide an exceptional platform for studying vertebrate ciliary cell biology in vivo (Brooks & Wallingford, 2012; Werner & Mitchell, 2012; Toriyama et al, 2016). Serving as positive controls, known centrosomal components, including PIBF1, localized strongly and specifically to basal bodies and co‐localized with the basal body marker Centrin4 (Fig 6C). WDR90, CCDC138, and KIAA1328 each localized strongly and specifically to basal bodies, strongly supporting their participation in centrosomal and ciliary biology, and validating the map's predictions.