Human Protein Complexes Research Articles

Hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000 proteomic experiments.

Macromolecular protein complexes carry out most functions in the cell including essential functions required for cell survival. Unfortunately, we lack the subunit composition for all human protein complexes. To address this gap we integrated >25,000 mass spectrometry experiments using a machine learning approach to identify > 15,000 human protein complexes. We show our map of protein complexes is highly accurate and more comprehensive than previous maps, placing ∼75% of human proteins into their physical contexts. We globally characterize our complexes using protein co-variation data (ProteomeHD.2) and identify co-varying complexes suggesting common functional associations. Our map also generates testable functional hypotheses for 472 uncharacterized proteins which we support using AlphaFold modeling. Additionally, we use AlphaFold modeling to identify 511 mutually exclusive protein pairs in hu.MAP3.0 complexes suggesting complexes serve different functional roles depending on their subunit composition. We identify expression as the primary way cells and organisms relieve the conflict of mutually exclusive subunits. Finally, we import our complexes to EMBL-EBI's Complex Portal ( https://www.ebi.ac.uk/complexportal/home ) as well as provide complexes through our hu.MAP3.0 web interface ( https://humap3.proteincomplexes.org/ ). We expect our resource to be highly impactful to the broader research community.

Predicting human and viral protein variants affecting COVID-19 susceptibility and repurposing therapeutics

The COVID-19 disease is an ongoing global health concern. Although vaccination provides some protection, people are still susceptible to re-infection. Ostensibly, certain populations or clinical groups may be more vulnerable. Factors causing these differences are unclear and whilst socioeconomic and cultural differences are likely to be important, human genetic factors could influence susceptibility. Experimental studies indicate SARS-CoV-2 uses innate immune suppression as a strategy to speed-up entry and replication into the host cell. Therefore, it is necessary to understand the impact of variants in immunity-associated human proteins on susceptibility to COVID-19. In this work, we analysed missense coding variants in several SARS-CoV-2 proteins and their human protein interactors that could enhance binding affinity to SARS-CoV-2. We curated a dataset of 19 SARS-CoV-2: human protein 3D-complexes, from the experimentally determined structures in the Protein Data Bank and models built using AlphaFold2-multimer, and analysed the impact of missense variants occurring in the protein–protein interface region. We analysed 468 missense variants from human proteins and 212 variants from SARS-CoV-2 proteins and computationally predicted their impacts on binding affinities for the human viral protein complexes. We predicted a total of 26 affinity-enhancing variants from 13 human proteins implicated in increased binding affinity to SARS-CoV-2. These include key-immunity associated genes (TOMM70, ISG15, IFIH1, IFIT2, RPS3, PALS1, NUP98, AXL, ARF6, TRIMM, TRIM25) as well as important spike receptors (KREMEN1, AXL and ACE2). We report both common (e.g., Y13N in IFIH1) and rare variants in these proteins and discuss their likely structural and functional impact, using information on known and predicted functional sites. Potential mechanisms associated with immune suppression implicated by these variants are discussed. Occurrence of certain predicted affinity-enhancing variants should be monitored as they could lead to increased susceptibility and reduced immune response to SARS-CoV-2 infection in individuals/populations carrying them. Our analyses aid in understanding the potential impact of genetic variation in immunity-associated proteins on COVID-19 susceptibility and help guide drug-repurposing strategies.

A nanobody-based strategy for rapid and scalable purification of human protein complexes.

The isolation of proteins in high yield and purity is a major bottleneck for the analysis of their three-dimensional structure, function and interactome. Here, we present a streamlined workflow for the rapid production of proteins or protein complexes using lentiviral transduction of human suspension cells, combined with highly specific nanobody-mediated purification and proteolytic elution. Application of the method requires prior generation of a plasmid coding for a protein of interest (POI) fused to an N- or C-terminal GFP or ALFA peptide tag using a lentiviral plasmid toolkit we have designed. The plasmid is then used to generate human suspension cell lines stably expressing the tagged fusion protein by lentiviral transduction. By leveraging the picomolar affinity of the GFP and ALFA nanobodies for their respective tags, the POI can be specifically captured from the resulting cell lysate even when expressed at low levels and under a variety of conditions, including detergents and mild denaturants. Finally, rapid and specific elution of the POI (in its tagged or untagged form) under native conditions is achieved within minutes at 4 °C, using the engineered SUMO protease SENPEuB. We demonstrate the wide applicability of the method by purifying multiple challenging soluble and membrane protein complexes to high purity from human cells. Our strategy is also directly compatible with many widely used GFP-expression plasmids, cell lines and transgenic model organisms. Finally, our method is faster than alternative approaches, requiring only 8 d from plasmid to purified protein, and results in substantially improved yields and purity.

Recognition of methamphetamine and other amines by trace amine receptor TAAR1.

Trace amine-associated receptor 1 (TAAR1), the founding member of a nine-member family of trace amine receptors, is responsible for recognizing a range of biogenic amines in the brain, including the endogenous β-phenylethylamine (β-PEA)1 as well as methamphetamine2, an abused substance that has posed a severe threat to human health and society3. Given its unique physiological role in the brain, TAAR1 is also an emerging target for a range of neurological disorders including schizophrenia, depression and drug addiction2,4,5. Here we report structures of human TAAR1-G-protein complexes bound to methamphetamine and β-PEA as well as complexes bound to RO5256390, a TAAR1-selective agonist, and SEP-363856, a clinical-stage dual agonist for TAAR1 and serotonin receptor 5-HT1AR (refs. 6,7). Together with systematic mutagenesis and functional studies, the structures reveal the molecular basis of methamphetamine recognition and underlying mechanisms of ligand selectivity and polypharmacology between TAAR1 and other monoamine receptors. We identify a lid-like extracellular loop 2 helix/loop structure and a hydrogen-bonding network in the ligand-binding pockets, which may contribute to the ligand recognition in TAAR1. These findings shed light on the ligand recognition mode and activation mechanism for TAAR1 and should guide the development of next-generation therapeutics for drug addiction and various neurological disorders.

HPC-Atlas: Computationally Constructing A Comprehensive Atlas of Human Protein Complexes

A fundamental principle of biology is that proteins tend to form complexes to play important roles in the core functions of cells. For a complete understanding of human cellular functions, it is crucial to have a comprehensive atlas of human protein complexes. Unfortunately, we still lack such a comprehensive atlas of experimentally validated protein complexes, which prevents us from gaining a complete understanding of the compositions and functions of human protein complexes, as well as the underlying biological mechanisms. To fill this gap, we built Human Protein Complexes Atlas (HPC-Atlas), as far as we know, the most accurate and comprehensive atlas of human protein complexes available to date. We integrated two latest protein interaction networks, and developed a novel computational method to identify nearly 9000 protein complexes, including many previously uncharacterized complexes. Compared with the existing methods, our method achieved outstanding performance on both testing and independent datasets. Furthermore, with HPC-Atlas we identified 751 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-affected human protein complexes, and 456 multifunctional proteins that contain many potential moonlighting proteins. These results suggest that HPC-Atlas can serve as not only a computing framework to effectively identify biologically meaningful protein complexes by integrating multiple protein data sources, but also a valuable resource for exploring new biological findings. The HPC-Atlas webserver is freely available at http://www.yulpan.top/HPC-Atlas.

CACO: A core-attachment method with cross-species functional ortholog information to detect human protein complexes.

Protein complexes play an essential role in living cells. Detecting protein complexes is crucial to understand protein functions and treat complex diseases. Due to high time and resource consumption of experiment approaches, many computational approaches have been proposed to detect protein complexes. However, most of them are only based on protein-protein interaction (PPI) networks, which heavily suffer from the noise in PPI networks. Therefore, we propose a novel core-attachment method, named CACO, to detect human protein complexes, by integrating the functional information from other species via protein ortholog relations. First, CACO constructs a cross-species ortholog relation matrix and transfers GO terms from other species as a reference to evaluate the confidence of PPIs. Then, a PPI filter strategy is adopted to clean the PPI network and thus a weighted clean PPI network is constructed. Finally, a new effective core-attachment algorithm is proposed to detect protein complexes from the weighted PPI network. Compared to other thirteen state-of-the-art methods, CACO outperforms all of them in terms of F-measure and Composite Score, showing that integrating ortholog information and the proposed core-attachment algorithm are effective in detecting protein complexes. CACO is available at https://csuligroup.com/CACO/ and https://github.com/CSUBioGroup/CACO.

In silico induced effect of N-ε-lysine acetylation on microtubule stability and subsequent interaction of microtubule-associated proteins.

Plant systems have been considered valuable models for addressing fundamental questions of microtubule (MT) organization due to their considerable practical utility. Protein acetylation is a very common protein modification, and therate of acetylation can be modulated in cells in different biological states, and these changes can be detected at a molecular level. Here, we focused on K40, K112, and K394 residues as putative acetylation sites, which were shown to exist in both plants and mammals. Such residual effect of acetylation causes critical but unclear effect on MT stability. In turn, it was shown that acetylation indirectly affects the probability of interaction with different MAPs (Microtubule-associated proteins). In a multiscale study using an all-atom force field to reproduce several lattice-forming elements found on the surface the microtubule, we assembled a fragment of a plant microtubule composed of nine tubulins and used it as a model object along with the existing human complex. Triplets of tubulins assembled in a lattice cell were then simulated for both human and plant protein complexes, using a coarse-grained force field. We then analyzed the trajectories and identified some critical deformations of the MAP interaction surface. The initial coordinates were used to investigate the structural scenario in which autophagy-related protein 8 (ATG8) was able to interact with the MT fragment.

RPA engages telomeric G-quadruplexes more effectively than CST.

G-quadruplexes (G4s) are a set of stable secondary structures that form within guanine-rich regions of single-stranded nucleic acids that pose challenges for DNA maintenance. The G-rich DNA sequence at telomeres has a propensity to form G4s of various topologies. The human protein complexes Replication Protein A (RPA) and CTC1-STN1-TEN1 (CST) are implicated in managing G4s at telomeres, leading to DNA unfolding and allowing telomere replication to proceed. Here, we use fluorescence anisotropy equilibrium binding measurements to determine the ability of these proteins to bind various telomeric G4s. We find that the ability of CST to specifically bind G-rich ssDNA is substantially inhibited by the presence of G4s. In contrast, RPA tightly binds telomeric G4s, showing negligible changes in affinity for G4 structure compared to linear ssDNAs. Using a mutagenesis strategy, we found that RPA DNA-binding domains work together for G4 binding, and simultaneous disruption of these domains reduces the affinity of RPA for G4 ssDNA. The relative inability of CST to disrupt G4s, combined with the greater cellular abundance of RPA, suggests that RPA could act as a primary protein complex responsible for resolving G4s at telomeres.

Molecular recognition of morphine and fentanyl by the human μ-opioid receptor

Morphine and fentanyl are among the most used opioid drugs that confer analgesia and unwanted side effects through both G protein and arrestin signaling pathways of μ-opioid receptor (μOR). Here, we report structures of the human μOR-G protein complexes bound to morphine and fentanyl, which uncover key differences in how they bind the receptor. We also report structures of μOR bound to TRV130, PZM21, and SR17018, which reveal preferential interactions of these agonists with TM3 side of the ligand-binding pocket rather than TM6/7 side. In contrast, morphine and fentanyl form dual interactions with both TM3 and TM6/7 regions. Mutations at the TM6/7 interface abolish arrestin recruitment of μOR promoted by morphine and fentanyl. Ligands designed to reduce TM6/7 interactions display preferential G protein signaling. Our results provide crucial insights into fentanyl recognition and signaling of μOR, which may facilitate rational design of next-generation analgesics.

Human mitochondrial protein complexes revealed by large-scale coevolution analysis and deep learning-based structure modeling.

Recent development of deep-learning methods has led to a breakthrough in the prediction accuracy of 3D protein structures. Extending these methods to protein pairs is expected to allow large-scale detection of protein-protein interactions (PPIs) and modeling protein complexes at the proteome level. We applied RoseTTAFold and AlphaFold, two of the latest deep-learning methods for structure predictions, to analyze coevolution of human proteins residing in mitochondria, an organelle of vital importance in many cellular processes including energy production, metabolism, cell death and antiviral response. Variations in mitochondrial proteins have been linked to a plethora of human diseases and genetic conditions. RoseTTAFold, with high computational speed, was used to predict the coevolution of about 95% of mitochondrial protein pairs. Top-ranked pairs were further subject to modeling of the complex structures by AlphaFold, which also produced contact probability with high precision and in many cases consistent with RoseTTAFold. Most top-ranked pairs with high contact probability were supported by known PPIs and/or similarities to experimental structural complexes. For high-scoring pairs without experimental complex structures, our coevolution analyses and structural models shed light on the details of their interfaces, including CHCHD4-AIFM1, MTERF3-TRUB2, FMC1-ATPAF2 and ECSIT-NDUFAF1. We also identified novel PPIs (PYURF-NDUFAF5, LYRM1-MTRF1L and COA8-COX10) for several proteins without experimentally characterized interaction partners, leading to predictions of their molecular functions and the biological processes they are involved in. Data of mitochondrial proteins and their interactions are available at: http://conglab.swmed.edu/mitochondria. Supplementary data are available at Bioinformatics online.

A virus–target host proteins recognition method based on integrated complexes data and seed extension

BackgroundTarget drugs play an important role in the clinical treatment of virus diseases. Virus-encoded proteins are widely used as targets for target drugs. However, they cannot cope with the drug resistance caused by a mutated virus and ignore the importance of host proteins for virus replication. Some methods use interactions between viruses and their host proteins to predict potential virus–target host proteins, which are less susceptible to mutated viruses. However, these methods only consider the network topology between the virus and the host proteins, ignoring the influences of protein complexes. Therefore, we introduce protein complexes that are less susceptible to drug resistance of mutated viruses, which helps recognize the unknown virus–target host proteins and reduce the cost of disease treatment.ResultsSince protein complexes contain virus–target host proteins, it is reasonable to predict virus–target human proteins from the perspective of the protein complexes. We propose a coverage clustering-core-subsidiary protein complex recognition method named CCA-SE that integrates the known virus–target host proteins, the human protein–protein interaction network, and the known human protein complexes. The proposed method aims to obtain the potential unknown virus–target human host proteins. We list part of the targets after proving our results effectively in enrichment experiments.ConclusionsOur proposed CCA-SE method consists of two parts: one is CCA, which is to recognize protein complexes, and the other is SE, which is to select seed nodes as the core of protein complexes by using seed expansion. The experimental results validate that CCA-SE achieves efficient recognition of the virus–target host proteins.

Unphosphorylated Form of the PAQosome Core Subunit RPAP3 Binds Ribosomal Preassembly Complexes to Modulate Ribosome Biogenesis.

The PAQosome (particle for arrangement of quaternary structure) is a 12-subunit HSP90 co-chaperone involved in the biogenesis of several human protein complexes. Two mechanisms of client selection have previously been identified, namely, the selective recruitment of specific adaptors and the differential use of homologous core subunits. Here, we describe a third client selection mechanism by showing that RPAP3, one of the core PAQosome subunits, is phosphorylated at several Ser residues in HEK293 cells. Affinity purification coupled with mass spectrometry (AP-MS) using the expression of tagged RPAP3 with single phospho-null mutations at Ser116, Ser119, or Ser121 reveals binding of the unphosphorylated form to several proteins involved in ribosome biogenesis. In vitro phosphorylation assays indicate that the kinase CK2 phosphorylates these RPAP3 residues. This finding is supported by data showing that pharmacological inhibition of CK2 enhances the binding of RPAP3 to ribosome preassembly factors in AP-MS experiments. Moreover, the silencing of PAQosome subunits interferes with ribosomal assembly factors' interactome. Altogether, these results indicate that RPAP3 phosphate group addition/removal at specific residues modulates binding to subunits of preribosomal complexes and allows speculating that PAQosome posttranslational modification is a mechanism of client selection.

Purification and Characterization of Human DNA Ligase IIIα Complexes After Expression in Insect Cells.

With improvements in biophysical approaches, there is growing interest in characterizing large, flexible multi-protein complexes. The use of recombinant baculoviruses to express heterologous genes in cultured insect cells has advantages for the expression of human protein complexes because of the ease of co-expressing multiple proteins in insect cells and the presence of a conserved post-translational machinery that introduces many of the same modifications found in human cells. Here we describe the preparation of recombinant baculoviruses expressing DNA ligase IIIα, XRCC1, and TDP1, their subsequent co-expression in cultured insect cells, the purification of complexes containing DNA ligase IIIα from insect cell lysates, and their characterization by multi-angle light scattering linked to size exclusion chromatography and negative stain electron microscopy.

Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks.

Characterization of protein complexes, i.e. sets of proteins assembling into a single larger physical entity, is important, as such assemblies play many essential roles in cells such as gene regulation. From networks of protein-protein interactions, potential protein complexes can be identified computationally through the application of community detection methods, which flag groups of entities interacting with each other in certain patterns. Most community detection algorithms tend to be unsupervised and assume that communities are dense network subgraphs, which is not always true, as protein complexes can exhibit diverse network topologies. The few existing supervised machine learning methods are serial and can potentially be improved in terms of accuracy and scalability by using better-suited machine learning models and parallel algorithms. Here, we present Super.Complex, a distributed, supervised AutoML-based pipeline for overlapping community detection in weighted networks. We also propose three new evaluation measures for the outstanding issue of comparing sets of learned and known communities satisfactorily. Super.Complex learns a community fitness function from known communities using an AutoML method and applies this fitness function to detect new communities. A heuristic local search algorithm finds maximally scoring communities, and a parallel implementation can be run on a computer cluster for scaling to large networks. On a yeast protein-interaction network, Super.Complex outperforms 6 other supervised and 4 unsupervised methods. Application of Super.Complex to a human protein-interaction network with ~8k nodes and ~60k edges yields 1,028 protein complexes, with 234 complexes linked to SARS-CoV-2, the COVID-19 virus, with 111 uncharacterized proteins present in 103 learned complexes. Super.Complex is generalizable with the ability to improve results by incorporating domain-specific features. Learned community characteristics can also be transferred from existing applications to detect communities in a new application with no known communities. Code and interactive visualizations of learned human protein complexes are freely available at: https://sites.google.com/view/supercomplex/super-complex-v3-0.

Human Protein Complex-Based Drug Signatures for Personalized Cancer Medicine.

Disease signature-based drug repositioning approaches typically first identify a disease signature from gene expression profiles of disease samples to represent a particular disease. Then such a disease signature is connected with the drug-induced gene expression profiles to find potential drugs for the particular disease. In order to obtain reliable disease signatures, the size of disease samples should be large enough, which is not always a single case in practice, especially for personalized medicine. On the other hand, the sample sizes of drug-induced gene expression profiles are generally large. In this study, we propose a new drug repositioning approach (HDgS), in which the drug signature is first identified from drug-induced gene expression profiles, and then connected to the gene expression profiles of disease samples to find the potential drugs for patients. In order to take the dependencies among genes into account, the human protein complexes (HPC) are used to define the drug signature. The proposed HDgS is applied to the drug-induced gene expression profiles in LINCS and several types of cancer samples. The results indicate that the HPC-based drug signature can effectively find drug candidates for patients and that the proposed HDgS can be applied for personalized medicine with even one patient sample.

Multi-scale top-down approach for modelling epileptic protein-protein interaction network analysis to identify driver nodes and pathways

Protein - Protein Interaction Network (PPIN) analysis unveils molecular level mechanisms involved in disease condition. To explore the complex regulatory mechanisms behind epilepsy and to address the clinical and biological issues of epilepsy, in silico techniques are feasible in a cost- effective manner. In this work, a hierarchical procedure to identify influential genes and regulatory pathways in epilepsy prognosis is proposed. To obtain key genes and pathways causing epilepsy, integration of two benchmarked datasets which are exclusively devoted for complex disorders is done as an initial step. Using STRING database, PPIN is constructed for modelling protein-protein interactions. Further, key interactions are obtained from the established PPIN using network centrality measures followed by network propagation algorithm -Random Walk with Restart (RWR). The outcome of the method reveals some influential genes behind epilepsy prognosis, along with their associated pathways like PI3 kinase, VEGF signaling, Ras, Wnt signaling etc. In comparison with similar works, our results have shown improvement in identifying unique molecular functions, biological processes, gene co-occurrences etc. Also, CORUM provides an annotation for approximately 60% of similarity in human protein complexes with the obtained result. We believe that the formulated strategy can put-up the vast consideration of indigenous drugs towards meticulous identification of genes encoded by protein against several combinatorial disorders.

The PAQosome, a novel molecular chaperoning machine for assembly of human protein complexes and networks

The human proteome comprises the products of more than 20,000 genes, most being arranged in complexes and networks that underlie essential cell functions. The Particle for Arrangement of Quaternary structure (PAQosome), formerly known as the R2TP/Prefoldin‐like complex, is to our knowledge the only molecular chaperoning machine designed by the evolution to assemble large protein complexes and regulatory networks in humans. The human PAQosome is a 12‐subunit complex made of two multisubunit modules, namely R2TP (RUVBL1, RUVBL2, RPAP3 and PIH1D1) and Prefoldin‐like (URI1, UXT, PDRG1, PFDN2, PFDN6 and the newly discovered ASDURF), as well as two other accessory proteins (POLR2E and WDR92). Several PAQosome clients/substrates have previously been identified, including all three nuclear RNA polymerases, various small nucleolar ribonucleoproteins (snoRNPs) and small nuclear RNPs (snRNPs), all six members of the phosphatidylinositol 3‐kinase‐related kinase (PIKK) family and others. Client specificity appears to be regulated by a number of different mechanisms, including the use of alternative core PAQosome subunits that recognize different clients directly, and the action of specificity adaptors that connect the PAQosome core to specific clients. Here, we present data identifying additional PAQosome clients, some being recruited through novel mechanisms that will be discussed. For example, and similar to what has been reported for axonemal dynein complexes involved in cilium motility, we now identify cytoplasmic dynein complexes that are responsible for cargo transport along microtubules as new clients of the PAQosome. Our recent data also indicate that specific phosphorylation of PAQosome subunits is at play for recognition of an important newly‐identified client, the pre‐ribosome, which is recruited to the PAQosome along with multiple ribosome biogenesis factors. Detailed mechanisms of assembly of some client complexes as well as the effect of mutations causing genetic diseases on complex/network integrity and PAQosome function will be presented. Characterization of the PAQosome is important not only to elucidate a newly‐recognized basal mechanism of gene regulation in eukaryotes but also to explore new research avenues that can lead to development of novel therapeutic and diagnostic tools.

IoMT-Based Association Rule Mining for the Prediction of Human Protein Complexes

The inspiring increase in the Internet-enabling devices has influenced health industry due to the nature of these devices where they offer health related information swiftly. One of the prominent characteristics of these devices is to provide physicians with effective diagnosis of sensitive diseases. Internet of Medical Things (IoMT) is a means of connecting medical devices to computing nodes with the help of Internet for affording real-time communications between patients and clinicians to understand the interaction of human protein complexes. A secure and correct protein complex prediction plays an important job in perceiving the principal method of various cellular determinations and to elucidate the functionality of different un-annotated proteins. Different experimental schemes have been evolved to accomplish this task, however, these schemes have high error rates and are not efficient in terms of time, cost, privacy, and security. To tackle these limitations, numerous computational models have been developed that consider a protein complex as a dense sub-graph and utilize some basic topological properties such as density and degree statistics as a feature set for protein complex prediction. Different kinds of sub-graph structures, e.g., ring, star, linear, and hybrid have also been found in Protein-Protein Interaction Network (PPIN), therefore, more advance topological properties may be helpful to predict these structures. Moreover, the amino acid sequence of protein determines its formation, thus, the sequence information is important for predicting the interacting property among proteins in a secure way. In this study, we have computed basic as well as advance topological features by considering the interaction network of human protein complexes in the IoMT environment. In addition, biological features, i.e., discrete wavelet coefficients, length, and entropy from amino acid sequences of proteins have been computed. The supervised learning method based on association rules such as Partial Tree (PART) and Non-Nested Generalized Exemplars (NNGE) are trained to identify human protein complexes on the basis of integrated topological and biological properties. The 10-fold cross validation is exercised to measure the proposed methods. Experimental results show that association rule learners with integrated features outperform other complex mining algorithms, i.e., probabilistic Bayesian Network (BN), and Random Forest, in terms of accuracy and efficiency in addition to provide privacy.

Upstream ORF-Encoded ASDURF Is a Novel Prefoldin-like Subunit of the PAQosome.

The PAQosome is an 11-subunit chaperone involved in the biogenesis of several human protein complexes. We show that ASDURF, a recently discovered upstream open reading frame (uORF) in the 5' UTR of ASNSD1 mRNA, encodes the 12th subunit of the PAQosome. ASDURF displays significant structural homology to β-prefoldins and assembles with the five known subunits of the prefoldin-like module of the PAQosome to form a heterohexameric prefoldin-like complex. A model of the PAQosome prefoldin-like module is presented. The data presented here provide an example of a eukaryotic uORF-encoded polypeptide whose function is not limited to cis-acting translational regulation of downstream coding sequence and highlights the importance of including alternative ORF products in proteomic studies.

Systematic Discovery of Endogenous Human Ribonucleoprotein Complexes

SUMMARYRNA-binding proteins (RBPs) play essential roles in biology and are frequently associated with human disease. Although recent studies have systematically identified individual RNA-binding proteins, their higher-order assembly into ribonucleoprotein (RNP) complexes has not been systematically investigated. Here, we describe a proteomics method for systematic identification of RNP complexes in human cells. We identify 1,428 protein complexes that associate with RNA, indicating that more than 20% of known human protein complexes contain RNA. To explore the role of RNA in the assembly of each complex, we identify complexes that dissociate, change composition, or form stable protein-only complexes in the absence of RNA. We use our method to systematically identify cell-type-specific RNA-associated proteins in mouse embryonic stem cells and finally, distribute our resource, rna.MAP, in an easy-to-use online interface (rna.proteincomplexes.org). Our system thus provides a methodology for explorations across human tissues, disease states, and throughout all domains of life.