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In vivo aspects of protein folding and quality control
Open accessAbstract
BACKGROUND: Proteins are synthesized on ribosomes as linear chains of amino acids and must fold into unique three-dimensional structures to fulfill their biological functions. Protein folding is intrinsically error-prone, and how it is accomplished efficiently represents a problem of great biological and medical importance. During folding, the nascent polypeptide must navigate a complex energy landscape. As a result, misfolded molecules may accumulate that expose hydrophobic amino acid residues and thus are in danger of forming potentially toxic aggregates. To ensure efficient folding and prevent aggregation, cells in all domains of life express various classes of proteins called molecular chaperones. These proteins receive the nascent polypeptide chain emerging from the ribosome and guide it along a productive folding pathway. Because proteins are structurally dynamic, constant surveillance of the proteome by an integrated network of chaperones and protein degradation machineries, the proteostasis network (PN), is required to maintain protein homeostasis in a range of external and endogenous stress conditions. ADVANCES: Over the past decade, we have gained substantial new insight into the overall behavior of the PN and the molecular mechanics of its components. Advances in structural biology and biophysical approaches have allowed chaperone mechanisms to be interrogated at an unprecedented level of detail. Recent work has provided fascinating insight into the process of protein folding on the ribosome and revealed how highly allosteric chaperones such as the heat shock protein 70 (Hsp70), Hsp90, and chaperonin systems modulate the folding energy landscapes of their protein clients. Studies of chaperone systems from bacteria and eukaryotes have revealed common principles underlying the organization of chaperone networks in different domains of life. Recently, we have begun to appreciate the relative complexity of eukaryotic chaperones and are starting to understand how eukaryotes deal with the challenge of folding a large proteome enriched in multidomain proteins. At the cellular level, the response of the PN to conformational stress, aging, and diseases of aberrant protein folding has been an area of intense investigation. Importantly, the capacity of the PN declines during aging and this leads to dysfunction of specific cell types and tissues, rendering the organism susceptible to chronic diseases. Among these, neurodegenerative syndromes associated with protein aggregation are increasingly prevalent in the aging human population. Notably, the accumulation of toxic protein aggregates is both a consequence and a cause of PN decline, driving a vicious cycle that ultimately leads to proteostasis collapse. OUTLOOK: A new view of protein folding is emerging, whereby the energy landscapes that proteins navigate during folding in vivo may differ substantially from those observed during refolding in vitro. From the ribosome through to the major chaperone systems, the nascent protein interacts with factors that modulate its folding pathway. Future work should focus on obtaining the high-resolution structural and kinetic information necessary to define the pathways of protein folding during translation, and in association with molecular chaperones. Organisms have evolved various mechanisms to deal with misfolded and aggregated proteins to maintain proteostasis. It is becoming increasingly clear that besides removing these proteins by degradation, cells also strategically sequester them into transient or stable aggregates, often in defined cellular locations. Much remains to be understood about how this cellular decision-making occurs at a molecular level and how dysregulation of these mechanisms leads to proteotoxicity. From a medical perspective, the intimate relationship between proteostasis and disease, aging, and neurodegeneration makes components of the PN logical drug targets, with the goal of promoting healthy aging. Pharmacological manipulation of the PN will require a detailed understanding of how the network responds to perturbation and how its different components cooperate. Molecular chaperones are key players in the cellular proteostasis network and serve to maintain a balanced proteome.: They promote the folding of newly synthesized proteins, function in conformational maintenance, and prevent potentially toxic off-pathway aggregation. Chaperones also cooperate with other components of the proteostasis network, such as the proteasome system and autophagy, in the removal of terminally misfolded and aggregated proteins through proteolytic degradation.
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Cites
Principles that Govern the Folding of Protein Chains
Anfinsen, C. B. (1973) · Science · doi:10.1126/science.181.4096.223 · Cited by 7,096
Inclusion body formation reduces levels of mutant huntingtin and the risk of neuronal death
Arrasate, M., Mitra, S., Schweitzer, E. S., Segal, M. R., & Finkbeiner, S. (2004) · Nature · doi:10.1038/nature02998 · Cited by 1,979
HSP90 at the hub of protein homeostasis: emerging mechanistic insights
Taipale, M., Jarosz, D. F., & Lindquist, S. (2010) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2918 · Cited by 1,818
Identification and characterization of essential genes in the human genome
Wang, T., Birsoy, K., Hughes, N. W., Krupczak, K. M., Post, Y., Wei, J. J., Lander, E. S., & Sabatini, D. M. (2015) · Science · doi:10.1126/science.aac7041 · Cited by 1,617
The Biology of Proteostasis in Aging and Disease
Labbadia, J., & Morimoto, R. I. (2015) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-060614-033955 · Cited by 1,472
Biological and Chemical Approaches to Diseases of Proteostasis Deficiency
Powers, E. T., Morimoto, R. I., Dillin, A., Kelly, J. W., & Balch, W. E. (2009) · Annual Review of Biochemistry · doi:10.1146/annurev.biochem.052308.114844 · Cited by 1,149
Misfolded proteins partition between two distinct quality control compartments
Kaganovich, D., Kopito, R., & Frydman, J. (2008) · Nature · doi:10.1038/nature07195 · Cited by 932
Quantitative Analysis of Hsp90-Client Interactions Reveals Principles of Substrate Recognition
Taipale, M., Krykbaeva, I., Koeva, M., Kayatekin, C., Westover, K. D., Karras, G. I., & Lindquist, S. (2012) · Cell · doi:10.1016/j.cell.2012.06.047 · Cited by 879
GGGGCC repeat expansion in C9orf72 compromises nucleocytoplasmic transport
Freibaum, B. D., Lu, Y., Lopez-Gonzalez, R., Kim, N. C., Almeida, S., Lee, K.-H., Badders, N., Valentine, M., Miller, B. L., Wong, P. C., et al. (2015) · Nature · doi:10.1038/nature14974 · Cited by 841
Widespread macromolecular interaction perturbations in human genetic disorders
Sahni, N., Yi, S., Taipale, M., Fuxman Bass, J. I., Coulombe-Huntington, J., Yang, F., Peng, J., Weile, J., Karras, G. I., Wang, Y., et al. (2015) · Cell · doi:10.1016/j.cell.2015.04.013 · Cited by 594
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Chiti, F., & Dobson, C. M. (2017) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-061516-045115 · Cited by 2,629
The proteostasis network and its decline in ageing
Hipp, M. S., Kasturi, P., & Hartl, F. U. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0101-y · Cited by 1,642
Self-citationRosenzweig, R., Nillegoda, N. B., Mayer, M. P., & Bukau, B. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0133-3 · Cited by 1,285
Ambadipudi, S., Biernat, J., Riedel, D., Mandelkow, E., & Zweckstetter, M. (2017) · Nature Communications · doi:10.1038/s41467-017-00480-0 · Cited by 782
N-degron and C-degron pathways of protein degradation
Varshavsky, A. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1816596116 · Cited by 544
Proteostasis in cardiac health and disease
Henning, R. H., & Brundel, B. J. J. M. (2017) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2017.89 · Cited by 193
Loss of TAX1BP1-Directed Autophagy Results in Protein Aggregate Accumulation in the Brain
Sarraf, S. A., Shah, H. V., Kanfer, G., Pickrell, A. M., Holtzclaw, L. A., Ward, M. E., & Youle, R. J. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.10.041 · Cited by 143
Increased fidelity of protein synthesis extends lifespan
Martinez-Miguel, V. E., Lujan, C., Espie--Caullet, T., Martinez-Martinez, D., Moore, S., Backes, C., Gonzalez, S., Galimov, E. R., Brown, A. E. X., Halic, M., et al. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.08.017 · Cited by 137
Integrating the DNA damage and protein stress responses during cancer development and treatment
Gorgoulis, V. G., Pefani, D., Pateras, I. S., & Trougakos, I. P. (2018) · The Journal of Pathology · doi:10.1002/path.5097 · Cited by 108
Advances in targeted degradation of endogenous proteins
Röth, S., Fulcher, L. J., & Sapkota, G. P. (2019) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-019-03112-6 · Cited by 92
Upregulation of α-synuclein following immune activation: Possible trigger of Parkinson's disease
Kasen, A., Houck, C., Burmeister, A. R., Sha, Q., Brundin, L., & Brundin, P. (2022) · Neurobiology of Disease · doi:10.1016/j.nbd.2022.105654 · Cited by 78
Swovick, K., Firsanov, D., Welle, K. A., Hryhorenko, J. R., Wise, J. P., George, C., Sformo, T. L., Seluanov, A., Gorbunova, V., & Ghaemmaghami, S. (2021) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra120.002301 · Cited by 73
Premature aging in mice with error-prone protein synthesis
Shcherbakov, D., Nigri, M., Akbergenov, R., Brilkova, M., Mantovani, M., Petit, P. I., Grimm, A., Karol, A. A., Teo, Y., Sanchón, A. C., et al. (2022) · Science Advances · doi:10.1126/sciadv.abl9051 · Cited by 68
Mitochondrial stress-dependent regulation of cellular protein synthesis
Topf, U., Uszczynska-Ratajczak, B., & Chacinska, A. (2019) · Journal of Cell Science · doi:10.1242/jcs.226258 · Cited by 52
Gavriilidis, C., Laredj, L., Solinhac, R., Messaddeq, N., Viaud, J., Laporte, J., Sumara, I., & Hnia, K. (2018) · Nature Cell Biology · doi:10.1038/s41556-017-0024-9 · Cited by 56
Kraushar, M. L., Krupp, F., Harnett, D., Turko, P., Ambrozkiewicz, M. C., Sprink, T., Imami, K., Günnigmann, M., Zinnall, U., Vieira-Vieira, C. H., et al. (2021) · Molecular Cell · doi:10.1016/j.molcel.2020.11.037 · Cited by 49
Fuqua, J. D., Mere, C. P., Kronemberger, A., Blomme, J., Bae, D., Turner, K. D., Harris, M. P., Scudese, E., Edwards, M., Ebert, S. M., et al. (2019) · The FASEB Journal · doi:10.1096/fj.201900766r · Cited by 47
Joshi, B. S., Youssef, S. A., Bron, R., de Bruin, A., Kampinga, H. H., & Zuhorn, I. S. (2021) · iScience · doi:10.1016/j.isci.2021.103282 · Cited by 31
CRYPTOCHROMES promote daily protein homeostasis
Wong, D. C. S., Seinkmane, E., Zeng, A., Stangherlin, A., Rzechorzek, N. M., Beale, A. D., Day, J., Reed, M., Peak‐Chew, S. Y., Styles, C. T., et al. (2022) · The EMBO Journal · doi:10.15252/embj.2021108883 · Cited by 30
Assessing and enhancing foldability in designed proteins
Listov, D., Lipsh‐Sokolik, R., Rosset, S., Yang, C., Correia, B. E., & Fleishman, S. J. (2022) · Protein Science · doi:10.1002/pro.4400 · Cited by 24
Tollefson, M. R., Gogal, R. A., Weaver, A. M., Schaefer, A. M., Marini, R. J., Azaiez, H., Kolbe, D. L., Wang, D., Weaver, A. E., Casavant, T. L., et al. (2023) · Human Genetics · doi:10.1007/s00439-023-02559-9 · Cited by 20
A3D 2.0 update for the prediction and optimization of protein solubility
Pujols, J., Iglesias, V., Santos, J., Kuriata, A., Kmiecik, S., & Ventura, S. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-1859-2_3 · Cited by 19
Sequestration of TDP-43 216-414 Aggregates by Cytoplasmic Expression of the proSAAS Chaperone
Peinado, J. R., Chaplot, K., Jarvela, T. S., Barbieri, E. M., Shorter, J., & Lindberg, I. (2022) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.2c00156 · Cited by 16
Structural recognition and stabilization of tyrosine hydroxylase by the J-domain protein DNAJC12
Tai, M. D. S., Ochoa, L., Flydal, M. I., Velasco-Carneros, L., Muntaner, J., Santiago, C., Gamiz-Arco, G., Moro, F., Jung-KC, K., Gil-Cantero, D., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57733-6 · Cited by 16
Chaperoning the Mononegavirales: Current Knowledge and Future Directions
Latorre, V., Mattenberger, F., & Geller, R. (2018) · Viruses · doi:10.3390/v10120699 · Cited by 14
Murvai, N., Kalmar, L., Szalaine Agoston, B., Szabo, B., Tantos, A., Csikos, G., Micsonai, A., Kardos, J., Vertommen, D., Nguyen, P. N., et al. (2020) · Cells · doi:10.3390/cells9081856 · Cited by 13
Ferrero, E., Di Gregorio, E., Ferrero, M., Ortolan, E., Moon, Y.-A., Di Campli, A., Pavinato, L., Mancini, C., Tripathy, D., Manes, M., et al. (2023) · Human Genetics · doi:10.1007/s00439-023-02572-y · Cited by 9
Nucleosome proteostasis and histone turnover
Arrieta, A., & Vondriska, T. M. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.990006 · Cited by 4
AI-Assisted Native Proteomics: Delineating Ribosomal Protein Conformations Pre- and Post-Assembly
Zhang, W., Sun, C., Xu, Z., & Xu, W. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.5c01431 · Cited by 3
Metadata
- Authors
- Balchin, D., Hayer-Hartl, M., & Hartl, F. U.
- Venue
- Science
- Published
- 2016-07
- Volume
- 353
- Issue
- 6294
- Pages
- aac4354-aac4354
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2473711714
- Cited by
- 1,439
- References
- 26
- FWCI
- 59.89
- Type
- journal article
- Language
- en
- Open access
- green
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