The Function, Structure, and Origins of the ER Membrane Protein Complex

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Abstract

The endoplasmic reticulum (ER) is the site of membrane protein insertion, folding, and assembly in eukaryotes. Over the past few years, a combination of genetic and biochemical studies have implicated an abundant factor termed the ER membrane protein complex (EMC) in several aspects of membrane protein biogenesis. This large nine-protein complex is built around a deeply conserved core formed by the EMC3–EMC6 subcomplex. EMC3 belongs to the universally conserved Oxa1 superfamily of membrane protein transporters, whereas EMC6 is an ancient, widely conserved obligate partner. EMC has an established role in the insertion of transmembrane domains (TMDs) and less understood roles during the later steps of membrane protein folding and assembly. Several recent structures suggest hypotheses about the mechanism(s) of TMD insertion by EMC, with various biochemical and proteomics studies beginning to reveal the range of EMC's membrane protein substrates.

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Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes.

Anders Krogh, Björn A Larsson, Gunnar von Heijne … (2001)

We describe and validate a new membrane protein topology prediction method, TMHMM, based on a hidden Markov model. We present a detailed analysis of TMHMM's performance, and show that it correctly predicts 97-98 % of the transmembrane helices. Additionally, TMHMM can discriminate between soluble and membrane proteins with both specificity and sensitivity better than 99 %, although the accuracy drops when signal peptides are present. This high degree of accuracy allowed us to predict reliably integral membrane proteins in a large collection of genomes. Based on these predictions, we estimate that 20-30 % of all genes in most genomes encode membrane proteins, which is in agreement with previous estimates. We further discovered that proteins with N(in)-C(in) topologies are strongly preferred in all examined organisms, except Caenorhabditis elegans, where the large number of 7TM receptors increases the counts for N(out)-C(in) topologies. We discuss the possible relevance of this finding for our understanding of membrane protein assembly mechanisms. A TMHMM prediction service is available at http://www.cbs.dtu.dk/services/TMHMM/. Copyright 2001 Academic Press.

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Molecular chaperones in protein folding and proteostasis.

F Ulrich Hartl, Andreas Bracher, Manajit Hayer-Hartl (2011)

Most proteins must fold into defined three-dimensional structures to gain functional activity. But in the cellular environment, newly synthesized proteins are at great risk of aberrant folding and aggregation, potentially forming toxic species. To avoid these dangers, cells invest in a complex network of molecular chaperones, which use ingenious mechanisms to prevent aggregation and promote efficient folding. Because protein molecules are highly dynamic, constant chaperone surveillance is required to ensure protein homeostasis (proteostasis). Recent advances suggest that an age-related decline in proteostasis capacity allows the manifestation of various protein-aggregation diseases, including Alzheimer's disease and Parkinson's disease. Interventions in these and numerous other pathological states may spring from a detailed understanding of the pathways underlying proteome maintenance.

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Highly accurate protein structure prediction for the human proteome

Kathryn Tunyasuvunakool, Jonas Adler, Zachary Wu … (2021)

Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure 1 . Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold 2 , at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective. AlphaFold is used to predict the structures of almost all of the proteins in the human proteome—the availability of high-confidence predicted structures could enable new avenues of investigation from a structural perspective.

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Author and article information

Journal

Title: Annual Review of Biochemistry

Abbreviated Title: Annu. Rev. Biochem.

Publisher: Annual Reviews

ISSN (Print): 0066-4154

ISSN (Electronic): 1545-4509

Publication date Created: June 21 2022

Publication date (Print): June 21 2022

Volume: 91

Issue: 1

Pages: 651-678

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[1 ]Medical Research Council Laboratory of Molecular Biology, Cambridge, United Kingdom;

Article

DOI: 10.1146/annurev-biochem-032620-104553

PubMed ID: 35287476

SO-VID: 2d1a4355-9826-4a35-9830-ab351d503511

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The Function, Structure, and Origins of the ER Membrane Protein Complex

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Most cited references 137

Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes.

Molecular chaperones in protein folding and proteostasis.

Highly accurate protein structure prediction for the human proteome

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