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• W2986996910 abstract "Computational simulations of protein folding can be used to interpret experimental folding results, to design new folding experiments, and to test the effects of mutations and small molecules on folding. However, whereas major experimental and computational progress has been made in understanding how small proteins fold, research on larger, multidomain proteins, which comprise the majority of proteins, is less advanced. Specifically, large proteins often fold via long-lived partially folded intermediates, whose structures, potentially toxic oligomerization, and interactions with cellular chaperones remain poorly understood. Molecular dynamics based folding simulations that rely on knowledge of the native structure can provide critical, detailed information on folding free energy landscapes, intermediates, and pathways. Further, increases in computational power and methodological advances have made folding simulations of large proteins practical and valuable. Here, using serpins that inhibit proteases as an example, we review native-centric methods for simulating the folding of large proteins. These synergistic approaches range from Gō and related structure-based models that can predict the effects of the native structure on folding to all-atom-based methods that include side-chain chemistry and can predict how disease-associated mutations may impact folding. The application of these computational approaches to serpins and other large proteins highlights the successes and limitations of current computational methods and underscores how computational results can be used to inform experiments. These powerful simulation approaches in combination with experiments can provide unique insights into how large proteins fold and misfold, expanding our ability to predict and manipulate protein folding. Computational simulations of protein folding can be used to interpret experimental folding results, to design new folding experiments, and to test the effects of mutations and small molecules on folding. However, whereas major experimental and computational progress has been made in understanding how small proteins fold, research on larger, multidomain proteins, which comprise the majority of proteins, is less advanced. Specifically, large proteins often fold via long-lived partially folded intermediates, whose structures, potentially toxic oligomerization, and interactions with cellular chaperones remain poorly understood. Molecular dynamics based folding simulations that rely on knowledge of the native structure can provide critical, detailed information on folding free energy landscapes, intermediates, and pathways. Further, increases in computational power and methodological advances have made folding simulations of large proteins practical and valuable. Here, using serpins that inhibit proteases as an example, we review native-centric methods for simulating the folding of large proteins. These synergistic approaches range from Gō and related structure-based models that can predict the effects of the native structure on folding to all-atom-based methods that include side-chain chemistry and can predict how disease-associated mutations may impact folding. The application of these computational approaches to serpins and other large proteins highlights the successes and limitations of current computational methods and underscores how computational results can be used to inform experiments. These powerful simulation approaches in combination with experiments can provide unique insights into how large proteins fold and misfold, expanding our ability to predict and manipulate protein folding. To function, structured proteins need to reproducibly fold to a unique three-dimensional structure in a biologically reasonable timescale. The observation that proteins reliably fold despite having astronomical numbers of possible conformations has been the impetus behind decades of experimental and theoretical folding studies (1Onuchic J.N. Luthey-Schulten Z. Wolynes P.G. Theory of protein folding: the energy landscape perspective.Annu. Rev. Phys. Chem. 1997; 48 (9348663): 545-60010.1146/annurev.physchem.48.1.545Crossref PubMed Google Scholar, 2Dill K.A. MacCallum J.L. The protein-folding problem, 50 years on.Science. 2012; 338 (23180855): 1042-104610.1126/science.1219021Crossref PubMed Scopus (775) Google Scholar, 3Gruebele M. Dave K. Sukenik S. Globular protein folding in vitro in vivo.Annu. Rev. Biophys. 2016; 45 (27391927): 233-25110.1146/annurev-biophys-062215-011236Crossref PubMed Scopus (0) Google Scholar). However, protein-folding pathways and folding intermediates are of interest not only in fundamental biophysics. Partially folded states expose surfaces that are normally buried. If these states are populated for extended periods of time, they may be recognized by elements of the cell's protein quality control machinery that can assist in folding or target proteins for degradation (4Hebert D.N. Molinari M. In and out of the ER: protein folding, quality control, degradation, and related human diseases.Physiol. Rev. 2007; 87 (17928587): 1377-140810.1152/physrev.00050.2006Crossref PubMed Scopus (439) Google Scholar, 5Hartl F.U. Hayer-Hartl M. Converging concepts of protein folding in vitro in vivo.Nat. Struct. Mol. Biol. 2009; 16 (19491934): 574-58110.1038/nsmb.1591Crossref PubMed Scopus (747) Google Scholar, 6Labbadia J. Morimoto R.I. The biology of proteostasis in aging and disease.Annu. Rev. Biochem. 2015; 84 (25784053): 435-46410.1146/annurev-biochem-060614-033955Crossref PubMed Scopus (546) Google Scholar, 7Dubnikov T. Ben-Gedalya T. Cohen E. Protein quality control in health and disease.Cold Spring Harb. Perspect. Biol. 2017; 9 (27864315)a02352310.1101/cshperspect.a023523Crossref PubMed Scopus (29) Google Scholar). Further, in many protein-folding diseases, pathological mutant proteins populate partially folded nonnative conformations and these conformations may result in nonnative protein-protein associations or oligomerization. A detailed understanding of protein folding and misfolding pathways thus has the potential to aid in the development of therapeutic interventions that prevent misfolding or reduce the population of intermediates. Alternately, in diseased cells, drugs could be designed that promote misfolding and drive cells into apoptosis. A challenge to developing such a detailed understanding is posed by the transient nature of the intermediates. Even the most long-lived folding intermediates rarely persist beyond timescales of a few minutes at most. Experimentally, transient intermediate states have been detected and characterized using spectroscopic methods such as fluorescence and CD as well as by small-angle X-ray scattering and other scattering methods, and, although technical advances have allowed the detection of rare and excited states by NMR (8Jiang Y. Kalodimos C.G. NMR studies of large proteins.J. Mol. Biol. 2017; 429 (28728982): 2667-267610.1016/j.jmb.2017.07.007Crossref PubMed Scopus (33) Google Scholar, 9Sekhar A. Kay L.E. An NMR view of protein dynamics in health and disease.Annu. Rev. Biophys. 2019; 48 (30901260): 297-31910.1146/annurev-biophys-052118-115647Crossref PubMed Scopus (28) Google Scholar) and X-ray crystallography (10Hekstra D.R. White K.I. Socolich M.A. Henning R.W. Šrajer V. Ranganathan R. Electric-field-stimulated protein mechanics.Nature. 2016; 540 (27926732): 400-40510.1038/nature20571Crossref PubMed Scopus (75) Google Scholar), these molecularly detailed methods may not be widely available, or such experiments may not be sufficient to detect folding intermediates. By contrast, computer simulations provide a detailed picture of protein folding not easily accessible to experiment. Specifically, protein-folding simulations can provide valuable, detailed, testable data on how proteins fold and misfold and may be used to formulate hypotheses on how protein folding might be manipulated. In the last decade, special purpose supercomputers such as ANTON (11Piana S. Klepeis J.L. Shaw D.E. Assessing the accuracy of physical models used in protein-folding simulations: quantitative evidence from long molecular dynamics simulations.Curr. Opin. Struct. Biol. 2014; 24 (24463371): 98-10510.1016/j.sbi.2013.12.006Crossref PubMed Scopus (306) Google Scholar) and massively distributed computing schemes such as [email protected] (12Pande V.S. Understanding protein folding using Markov state models.Adv. Exp. Med. Biol. 2014; 797 (24297278): 101-10610.1007/978-94-007-7606-7_8Crossref PubMed Google Scholar) have made it possible to simulate the folding of small proteins in all-atom detail using realistic empirical force fields, without the aid of any biasing forces. Whereas these simulations have provided invaluable insights, even special purpose, high-performance computing platforms are limited to simulating the folding of smaller chains (currently ∼100 amino acids with folding times up to milliseconds). However, the median length of a protein is 532, 365, and 329 amino acids in eukaryotes, bacteria, and archaea, respectively (13Wang M. Kurland C.G. Caetano-Anollés G. Reductive evolution of proteomes and protein structures.Proc. Natl. Acad. Sci. U.S.A. 2011; 108 (21730144): 11954-1195810.1073/pnas.1017361108Crossref PubMed Scopus (49) Google Scholar), and folding times range from microseconds to tens of minutes. Even with anticipated continuing increases in computing power, simulating the folding of these larger slower folding proteins using standard molecular dynamics (MD) 5The abbreviations used are: MDmolecular dynamicsAATα1-antitrypsinSBMstructure-based modelBFbias functionalSCPSself-consistent path samplingRCLreactive center loopERendoplasmic reticulumAWSEMassociative memory, water-mediated, structure and energy modelDHFRdihydrofolate reductaseDLDdiscontinuous loop domainABDadenosine-binding domainWSMEWako-Saito-Muñoz-EatonAKEadenylate kinaseHDXhydrogen-deuterium exchangePDBProtein Data BankFEPfolding free energy profile(s). simulations will remain out of reach for the foreseeable future. In addition to the increased computational demands due to size, large slow folding proteins often fold through long-lived intermediates corresponding to deep local energy minima. For such proteins, even very long simulations are likely to simply observe the protein exploring limited conformational space within a single local minimum because transitions between minima are rare. molecular dynamics α1-antitrypsin structure-based model bias functional self-consistent path sampling reactive center loop endoplasmic reticulum associative memory, water-mediated, structure and energy model dihydrofolate reductase discontinuous loop domain adenosine-binding domain Wako-Saito-Muñoz-Eaton adenylate kinase hydrogen-deuterium exchange Protein Data Bank folding free energy profile(s). To simulate such rare transitions, numerous methods for accelerating or enhancing sampling during MD simulations have been developed (14Gō N. Statistical mechanics of protein folding, unfolding and fluctuation.Adv. 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Self-consistent calculation of protein folding pathways.J. Chem. Phys. 2017; 147 (064108) (28810783)10.1063/1.4997197Crossref Scopus (10) Google Scholar). One particularly effective approach to efficiently simulate the folding of large proteins is to take advantage of the evidence that protein folding is to a large extent encoded by the contacts in the native, folded protein. Computational schemes in which the force field explicitly includes a biasing term favoring native contacts allow the simulation of protein folding with many orders of magnitude less computational effort than is required with conventional MD methods. These studies, including folding simulations of large proteins such as adenylate kinase (24Li W. Terakawa T. Wang W. Takada S. Energy landscape and multiroute folding of topologically complex proteins adenylate kinase and 2ouf-knot.Proc. Natl. Acad. Sci. 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J. 2018; 114 (29742402): 2083-209410.1016/j.bpj.2018.03.027Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar), have been successful in generating folding pathways and intermediates that agree with experimental results and provide testable hypotheses on what intermediate states are likely to be populated during folding. This review focuses on native-centric simulation methods that are applicable to the folding of large and slow-folding proteins. We consider two categories of techniques, both of which rely on the native protein contact map but have different levels of spatial resolution and chemical detail: (i) Gō and related structure-based models (SBMs) that provide knowledge on the effects of structure on folding and (ii) all-atom-based methods that take into account the effects of side-chain chemistry on folding and can therefore predict how mutations may affect folding. As a prototypical case study, we discuss how both classes of methods have been used to simulate the folding of the canonical inhibitory serpin α1-antitrypsin (AAT) a 394-amino acid protein with folding times as long as tens of minutes (32Kim D. Yu M.H. Folding pathway of human α1-antitrypsin: characterization of an intermediate that is active but prone to aggregation.Biochem. Biophys. Res. Commun. 1996; 226 (8806643): 378-38410.1006/bbrc.1996.1364Crossref PubMed Scopus (48) Google Scholar, 33Tsutsui Y. Dela Cruz R. Wintrode P.L. Folding mechanism of the metastable serpin α1-antitrypsin.Proc. Natl. Acad. Sci. U.S.A. 2012; 109 (22392975): 4467-447210.1073/pnas.1109125109Crossref PubMed Scopus (52) Google Scholar, 34Stocks B.B. Sarkar A. Wintrode P.L. Konermann L. Early hydrophobic collapse of α1-antitrypsin facilitates formation of a metastable state: insights from oxidative labeling and mass spectrometry.J. Mol. 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Chem. 1997; 48 (9348663): 545-60010.1146/annurev.physchem.48.1.545Crossref PubMed Google Scholar). The effective potential energy (averaged over solvent degrees of freedom) as a function of chain conformation defines a protein's energy landscape (Fig. 1). This multidimensional energy landscape results from multiple driving forces and constraints, including the drive to bury hydrophobic residues, to satisfy hydrogen bond donors and acceptors, and to solvate or pair charged residues and the constraints of chain connectivity and steric clashes. According to arguments adapted from the physics of disordered systems, random amino acid sequences will be characterized by irreconcilable conflicts between these multiple driving forces and constraints, termed energetic frustration, resulting in many unrelated structures of similar energy (42Bryngelson J.D. Wolynes P.G. Spin glasses and the statistical mechanics of protein folding.Proc. Natl. Acad. Sci. 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The energy landscape of such a minimally frustrated protein resembles a high-dimensional funnel with the native state at the bottom. Although local minima and barriers still exist (the funnel is “rugged”), the global energetic bias toward the native structure promotes efficient folding (Fig. 1). A funneled energy landscape implies that the potential energy of the system should decrease with increasing numbers of native contacts, suggesting that the number of native contacts formed should serve as a good reaction coordinate for folding. Early support for a native-centric picture of protein folding was provided by simulations of simplified lattice proteins (14Gō N. Statistical mechanics of protein folding, unfolding and fluctuation.Adv. Biophys. 1976; 9 (1015397): 65-113Google Scholar, 44Taketomi H. Ueda Y. Gō N. Studies on protein folding, unfolding and fluctuations by computer simulation. I. 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The term structure-based models (SBMs) has been used to refer to Gō-type models, which, in addition to native structure-derived terms, may contain knowledge-based terms that are derived from sequence information (15Karanicolas J. Brooks 3rd, C.L. Improved Gō-like models demonstrate the robustness of protein folding mechanisms towards non-native interactions.J. Mol. Biol. 2003; 334 (14607121): 309-32510.1016/j.jmb.2003.09.047Crossref PubMed Scopus (0) Google Scholar, 54Azia A. Levy Y. Nonnative electrostatic interactions can modulate protein folding: molecular dynamics with a grain of salt.J. Mol. Biol. 2009; 393 (19683007): 527-54210.1016/j.jmb.2009.08.010Crossref PubMed Scopus (93) Google Scholar), nonnative interactions (55Yadahalli S. Hemanth Giri Rao V.V. Gosavi S. Modeling non-native interactions in designed proteins.Isr. J. Chem. 2014; 54: 1230-124010.1002/ijch.201400035Crossref Google Scholar), information from additional native structures (56Whitford P.C. Miyashita O. Levy Y. 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• W2986996910 created "2019-11-22" @default.
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• W2986996910 date "2020-01-01" @default.
• W2986996910 modified "2023-10-17" @default.
• W2986996910 title "Successes and challenges in simulating the folding of large proteins" @default.
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