DOCSLIB.ORG

Chemical Physics of Protein Folding

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Chemical Physics of Protein Folding Chemical physics of protein folding Peter G. Wolynesa,1, William A. Eatonb, and Alan R. Fershtc aDepartment of Chemistry and Center for Theoretical Biological Physics, Rice University, Houston, TX 77005; bNational Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892; and cMedical Research Council Laboratory of Molecular Biology, Cambridge CB2 0QH, United Kingdom n 2008, to the consternation of some, logically distinct traps. In other words, the Many of the most intriguing questions of one of the editors of this special issue energy landscape of evolved proteins ap- mechanism can only be addressed feebly at on the “Chemical Physics of Protein pears to be funneled (6, 7). the level of ensemble-averaged experi- I ” “ Folding, was quoted as saying, What The overall funneled nature of the folding ments; thus, folding science has called forth was called the protein folding problem 20 landscape provides a first guess of how some extremely challenging experiments in years ago is solved” (1). One purpose of this folding begins and continues: Proteins fold which molecules are studied individually special issue is to drive home this point. The by assembling primarily native substructures, oneatatime(17–19). On the computa- other, more important purpose is to illus- whereas they only transiently sample mis- tional side, although effective theories of trate how workers on the protein folding folds. This insight explains the success of folding can often use simplified models that problem, by moving beyond their early ob- protein engineering in providing detailed make interesting and surprising predictions session with seeming paradoxes (2), are structural information on the transition state (20, 21), many questions of detail can developing a quantitative understanding ensemble, the so-called “φ-value analysis” now be answered satisfactorily with fully of how the simpler biological structures (8, 9). In some cases, changing solution atomistic simulations that challenge com- assemble both in vitro and in vivo. The conditions changes the position of the tran- putational power to the limit (22–24). emerging quantitative understanding reveals sition state along the reaction coordinate, As the title of this special issue suggests, simultaneously the richness of folding phe- such that φ-value studies can even tell us in most of the contributions focus on explor- nomena and the elegant simplicity of the what order native parts assemble along the ing the general principles of folding. These underlying principles of spontaneous bio- dominant transition path. principles emerge as clearly in test tube molecular assembly. The appreciation of The ensemble nature of the transition studies as they do in the cell. However, the these contrasting aspects of the folding states was the first clue that mechanistic cell provides its own challenges to folding problem has come about through the co- complexity still remains on funneled science. The effects of heterogeneous in- operation of theorists and experimentalists, landscapes. Multiple choices of a precise tracellular environments on folding land- a theme common to all the contributions to folding mechanism are possible, and sev- scapes (25) are beginning to be explored. this special issue. Although the basic ideas eral experimental studies herein demon- Also, despite the inborn tendency of pro- about the folding energy landscape have strate the malleability of specific folding teins to do the right thing (arising from the turned out to be quite simple, entering mechanisms for proteins of either related minimal frustration principle), alternate even into some undergraduate textbooks (3), structure or related sequence; on a fun- folding, misfolding, and aggregation for exploring their consequences in real systems neled landscape, many roads lead to a few proteins do lead to pathology, a topic has required painstaking intellectual analy- Rome (10, 11). After a molecule embarks explored in this issue (26) with the super- sis, as well as detailed computer simulations on a route, even on a funneled energy oxide dismutase system that is involved in and experiments that still stretch the bounds landscape, assembly may not be com- amyotrophic lateral sclerosis. of what is feasible. The backgrounds of pletely straightforward. Occasionally, The topics discussed in this issue are only the contributors to this issue reflect the a greedy attempt to make native contacts a small part of the work in the folding field. breadth of the folding field and range from early on can lead to topological traps (12); Nevertheless, they make clear that protein computer science and theoretical physics to in that case, some early native interactions folding is a vibrant, living, interdisciplinary molecular biology and organic chemistry. must be undone to allow complete folding part of the natural sciences. We hope this A great deal of the progress in the field can and one must backtrack (13). Also, evo- snapshot of the field will encourage others to thus be traced to a fairly successful effort lution toward a funneled landscape cannot bring new approaches to contribute to our to develop a common language and con- repeal the universal character of the understanding of biomolecular assembly and ceptual framework for describing folding. physics of specific molecular interactions; encourage the use of the ideas and strategies The conceptual framework is provided thus, no real folding landscape is ever that have already proved successful in by energy landscape theory, which perfectly funneled. If the nonnative inter- the study of assembling the simplest bio- describes the diversity of structural possi- actions are fairly weak, they just provide molecules to look at the full complexity of bilities in statistical mechanical terms. The a source of “friction,” a topic quantified living systems at higher levels of complexity. main paradoxes of folding are resolved by here in several papers (14, 15). If the the consistency principle (4) or, more nonnative interactions become stronger, as generally, by the principle of minimal theory predicts, frustrated interactions can Author contributions: P.G.W., W.A.E., and A.R.F. wrote the frustration (5), which quantifies the allow specific intermediates to form with paper. dominance of interactions stabilizing the substantial nonnative contacts along with The authors declare no conflict of interest. fi 1 speci c native structure over other inter- some native structure. This phenomenon To whom correspondence should be addressed. E-mail: actions that would favor nonnative, topo- also receives attention here (16). [email protected]. 1. Service RF (2008) Problem solved* (*sort of). Science 5. Bryngelson JD, Wolynes PG (1987) Spin glasses and the ultrafast folding protein. Proc Natl Acad Sci USA 105 321(5890):784–786. statistical mechanics of protein folding. Proc Natl Acad (48):18655–18662. 2. Wolynes PG, Eaton WA (1999) The physics of protein Sci USA 84(21):7524–7528. 8. Fersht AR (1999) Structure and Mechanism in Protein folding. Physics World 12(9):39–44. 6. Leopold PE, Montal M, Onuchic JN (1992) Protein folding Science (Freeman, New York). 3. Voet D, Voet JG, Pratt CW (1999) Fundamentals of funnels: A kinetic approach to the sequence-structure 9. Matouschek A, Kellis JT, Jr., Serrano L, Fersht AR Biochemistry (Wiley, New York), p 154. relationship. Proc Natl Acad Sci USA 89(18):8721–8725. (1989) Mapping the transition state and pathway of 4. Go N (1983) Theoretical studies of protein folding. 7. Kubelka J, Henry ER, Cellmer T, Hofrichter J, Eaton WA protein folding by protein engineering. Nature 340 Annu Rev Biophys Bioeng 12:183–210. (2008) Chemical, physical, and theoretical kinetics of an (6229):122–126. 17770–17771 | PNAS | October 30, 2012 | vol. 109 | no. 44 www.pnas.org/cgi/doi/10.1073/pnas.1215733109 Downloaded by guest on September 26, 2021 SPECIAL FEATURE: INTRODUCTION 10. Giri R, et al. (2012) Folding pathways of proteins single-molecule spectroscopy. Proc Natl Acad Sci USA 21. Shental-Bechor D, et al. (2012) Nonnative interactions with increasing degree of sequence identities but 109:17800–17806. regulate folding and switching of myristoylated pro- different structure and function. Proc Natl Acad Sci 16. Beauchamp KA, McGibbon R, Lin Y-S, Pande VS tein. Proc Natl Acad Sci USA 109:17839–17844. USA 109:17772–17776. (2012) Simple few-state models reveal hidden com- 22. Piana S, Lindorff-Larsen K, Shaw DE (2012) Protein folding 11. Korzhnev DM, Religa TL, Kay LE (2012) Transiently plexity in protein folding. Proc Natl Acad Sci USA 109: kinetics and thermodynamics from atomistic simulation. populated intermediate functions as a branching point 17807–17813. Proc Natl Acad Sci USA 109:17845–17850. of the FF domain folding pathway. Proc Natl Acad Sci 17. Stigler J, Rief M (2012) Calcium-dependent folding of 23. McCully ME, Beck DAC, Daggett V (2012) Multi- – USA 109:17777 17782. single calmodulin molecules. Proc Natl Acad Sci USA molecule test-tube simulations of protein unfolding 12. Sułkowska JI, Noel JK, Onuchic JN (2012) Energy land- 109:17814–17819. and aggregation. Proc Natl Acad Sci USA 109: scape of knotted protein folding. Proc Natl Acad Sci 18. Jagannathan B, Elms PJ, Bustamante C, Marqusee S (2012) 17851–17856. USA 109:17783–17788. Direct observation of a force-induced switch in the an- 24. Potoyan DA, Papoian GA (2012) Regulation of the H4 13. Li W, Terakawa T, Wang W, Takada S (2012) Energy isotropic mechanical unfolding pathway of a protein. tail binding and folding landscapes via Lys-16 acetyla- landscape and multiroute folding of topologically com- – – plex proteins adenylate kinase and 2out-knot. Proc Natl Proc Natl Acad Sci USA 109:17820 17825. tion. Proc Natl Acad Sci USA 109:17857 17862. Acad Sci USA 109:17789–17794. 19. Ferreon ACM, Moosa MM, Gambin Y, Deniz AA (2012) 25. Guo M, Xu Y, Gruebele M (2012) Temperature de- 14. Wensley BG, et al. (2012) Separating the effects of in- Counteracting chemical chaperone effects on the sin- pendence of protein folding kinetics in living cells.
Load more

Recommended publications
  • DNA Energy Landscapes Via Calorimetric Detection of Microstate Ensembles of Metastable Macrostates and Triplet Repeat Diseases
    DNA energy landscapes via calorimetric detection of microstate ensembles of metastable macrostates and triplet repeat diseases Jens Vo¨ lkera, Horst H. Klumpb, and Kenneth J. Breslauera,c,1 aDepartment of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, 610 Taylor Rd, Piscataway, NJ 08854; bDepartment of Molecular and Cell Biology, University of Cape Town, Private Bag, Rondebosch 7800, South Africa; and cCancer Institute of New Jersey, New Brunswick, NJ 08901 Communicated by I. M. Gelfand, Rutgers, The State University of New Jersey, Piscataway, NJ, October 15, 2008 (received for review September 8, 2008) Biopolymers exhibit rough energy landscapes, thereby allowing biological role(s), if any, of kinetically stable (metastable) mi- biological processes to access a broad range of kinetic and ther- crostates that make up the time-averaged, native state ensembles modynamic states. In contrast to proteins, the energy landscapes of macroscopic nucleic acid states remains to be determined. of nucleic acids have been the subject of relatively few experimen- As part of an effort to address this deficiency, we report here tal investigations. In this study, we use calorimetric and spectro- experimental evidence for the presence of discrete microstates scopic observables to detect, resolve, and selectively enrich ener- in metastable triplet repeat bulge looped ⍀-DNAs of potential getically discrete ensembles of microstates within metastable DNA biological significance. The specific triplet repeat bulge looped structures. Our results are consistent with metastable, ‘‘native’’ ⍀-DNA species investigated mimic slipped DNA structures DNA states being composed of an ensemble of discrete and corresponding to intermediates in the processes that lead to kinetically stable microstates of differential stabilities, rather than DNA expansion in triplet repeat diseases (36).
    [Show full text]
  • Ruggedness in the Free Energy Landscape Dictates Misfolding of the Prion Protein
    Article Ruggedness in the Free Energy Landscape Dictates Misfolding of the Prion Protein Roumita Moulick 1, Rama Reddy Goluguri 1 and Jayant B. Udgaonkar 1,2 1 - National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bengaluru 560065, India 2 - Indian Institute of Science Education and Research, Pashan, Pune 411008, India Correspondence to Jayant B. Udgaonkar: Indian Institute of Science Education and Research, Pashan, Pune 411008, India. [email protected] https://doi.org/10.1016/j.jmb.2018.12.009 Edited by Sheena Radford Abstract Experimental determination of the key features of the free energy landscapes of proteins, which dictate their adeptness to fold correctly, or propensity to misfold and aggregate and which are modulated upon a change from physiological to aggregation-prone conditions, is a difficult challenge. In this study, sub-millisecond kinetic measurements of the folding and unfolding of the mouse prion protein reveal how the free energy landscape becomes more complex upon a shift from physiological (pH 7) to aggregation-prone (pH 4) conditions. Folding and unfolding utilize the same single pathway at pH 7, but at pH 4, folding occurs on a pathway distinct from the unfolding pathway. Moreover, the kinetics of both folding and unfolding at pH 4 depend not only on the final conditions but also on the conditions under which the processes are initiated. Unfolding can be made to switch to occur on the folding pathway by varying the initial conditions. Folding and unfolding pathways appear to occupy different regions of the free energy landscape, which are separated by large free energy barriers that change with a change in the initial conditions.
    [Show full text]
  • Introducing the Levinthal's Protein Folding Paradox and Its Solution
    Article pubs.acs.org/jchemeduc Introducing the Levinthal’s Protein Folding Paradox and Its Solution Leandro Martínez* Department of Physical Chemistry, Institute of Chemistry, University of Campinas, Campinas, Saõ Paulo 13083-970, Brazil *S Supporting Information ABSTRACT: The protein folding (Levinthal’s) paradox states that it would not be possible in a physically meaningful time to a protein to reach the native (functional) conformation by a random search of the enormously large number of possible structures. This paradox has been solved: it was shown that small biases toward the native conformation result in realistic folding times of realistic-length sequences. This solution of the paradox is, however, not amenable to most chemistry or biology students due to the demanding mathematics. Here, a simplification of the study of the paradox and its solution is provided so that it is accessible to chemists and biologists at an undergraduate or graduate level. Despite its simplicity, the model captures some fundamental aspects of the protein folding mechanism and allows students to grasp the actual significance of the popular folding funnel representation of the protein energy landscape. The analysis of the folding model provides a rich basis for a discussion of the relationships between kinetics and thermodynamics on a fundamental level and on student perception of the time-scales of molecular phenomena. KEYWORDS: Upper-Division Undergraduate, Graduate Education, Biochemistry, Biophysical Chemistry, Molecular Biology, Proteins/Peptides,
    [Show full text]
  • Complex Energy Landscapes” (IPAM Program, Fall 2017)
    Whitepaper: “Complex Energy Landscapes” (IPAM program, fall 2017) Authors list (alphabetically) Nestor F. Aguirre, Chris Anderson, Lorenzo Boninsegna, Gábor Csányi, Mauricio del Razo Sarmina, Marco Di Gennaro, Florent Hédin, Graeme Henkelman, Richard G. Hennig, Jan Janßen, Tony Lelièvre, Hao Li, Mitchell Luskin, Noa Marom, Jörg Neugebauer, Feliks Nüske, Joshua Paul, Danny Perez, Giovanni Pinamonti, Petr Plechac, Biswas Rijal, Gideon Simpson, Justin C. Smith, Thomas Swinburne, Anne Marie Z. Tan, Mira Todorova, Dallas R. Trinkle, Stephen Xie, Ping Yang Abstract Recent advances in computational resources and the development of high-throughput frameworks enable the efficient sampling of complicated multivariate functions. This includes energy and electronic property landscapes of inorganic, organic, biomolecular, and hybrid ​ ​ materials and functional nanostructures. Combined with new developments in data science, this ​ leads to a rapidly growing need for numerical methods and a fundamental mathematical ​ ​ understanding of efficient sampling approaches, optimization techniques, hierarchical surrogate models, coarse-graining techniques, and methods for uncertainty quantification. The complexity of these energy and property landscapes originates from their simultaneous dependence on discrete degrees of freedom – i.e., number of atoms and species types – and continuous ones – i.e., the position of atoms. Moreover, dynamical behavior governed by complex landscapes involves a rich hierarchy of timescales and is characterized by rare events
    [Show full text]
  • Protein Functional Landscapes, Dynamics, Allostery 297
    Quarterly Reviews of Biophysics 43, 3 (2010), pp. 295–332. f Cambridge University Press 2010 295 doi:10.1017/S0033583510000119 Printed in the United States of America Protein functional landscapes,dynamics, allostery: a tortuouspathtowards a universal theoretical framework Pavel I. Zhuravlev and Garegin A. Papoian* Department of Chemistry and Biochemistry, Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, USA Abstract. Energy landscape theories have provided a common ground for understanding the protein folding problem, which once seemed to be overwhelmingly complicated. At the same time, the native state was found to be an ensemble of interconverting states with frustration playing a more important role compared to the folding problem. The landscape of the folded protein – the native landscape – is glassier than the folding landscape; hence, a general description analogous to the folding theories is difficult to achieve. On the other hand, the native basin phase volume is much smaller, allowing a protein to fully sample its native energy landscape on the biological timescales. Current computational resources may also be used to perform this sampling for smaller proteins, to build a ‘topographical map’ of the native landscape that can be used for subsequent analysis. Several major approaches to representing this topographical map are highlighted in this review, including the construction of kinetic networks, hierarchical trees and free energy surfaces with subsequent structural and kinetic analyses. In this review, we extensively discuss the important question of choosing proper collective coordinates characterizing functional motions. In many cases, the substates on the native energy landscape, which represent different functional states, can be used to obtain variables that are well suited for building free energy surfaces and analyzing the protein’s functional dynamics.
    [Show full text]
  • Evolution, Energy Landscapes and the Paradoxes of Protein Folding
    Biochimie 119 (2015) 218e230 Contents lists available at ScienceDirect Biochimie journal homepage: www.elsevier.com/locate/biochi Review Evolution, energy landscapes and the paradoxes of protein folding Peter G. Wolynes Department of Chemistry and Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA article info abstract Article history: Protein folding has been viewed as a difficult problem of molecular self-organization. The search problem Received 15 September 2014 involved in folding however has been simplified through the evolution of folding energy landscapes that are Accepted 11 December 2014 funneled. The funnel hypothesis can be quantified using energy landscape theory based on the minimal Available online 18 December 2014 frustration principle. Strong quantitative predictions that follow from energy landscape theory have been widely confirmed both through laboratory folding experiments and from detailed simulations. Energy Keywords: landscape ideas also have allowed successful protein structure prediction algorithms to be developed. Folding landscape The selection constraint of having funneled folding landscapes has left its imprint on the sequences of Natural selection Structure prediction existing protein structural families. Quantitative analysis of co-evolution patterns allows us to infer the statistical characteristics of the folding landscape. These turn out to be consistent with what has been obtained from laboratory physicochemical folding experiments signaling a beautiful confluence of ge- nomics and chemical physics. © 2014 Elsevier B.V. and Societ e française de biochimie et biologie Moleculaire (SFBBM). All rights reserved. Contents 1. Introduction . ....................... 218 2. General evidence that folding landscapes are funneled . ....................... 221 3. How rugged is the folding landscape e physicochemical approach . ....................... 222 4.
    [Show full text]
  • Modeling the Free Energy Landscape of Biomolecules Via Dihedral Angle Principal Component Analysis of Molecular Dynamics Simulations
    Modeling the Free Energy Landscape of Biomolecules via Dihedral Angle Principal Component Analysis of Molecular Dynamics Simulations Dissertation zur Erlangung des Doktorgrades der Naturwissenschaften vorgelegt beim Fachbereich Biochemie, Chemie und Pharmazie der Goethe-Universit¨at in Frankfurt am Main von Alexandros Altis aus Frankfurt am Main Frankfurt am Main 2008 (D 30) vom Fachbereich Biochemie, Chemie und Pharmazie der Goethe-Universit¨at Frankfurt am Main als Dissertation angenommen. Dekan: Prof. Dr. Dieter Steinhilber 1. Gutachter: Prof. Dr. Gerhard Stock 2. Gutachter: JProf. Dr. Karin Hauser Datum der Disputation: ............................................. Contents 1 Introduction 1 2 Dihedral Angle Principal Component Analysis 7 2.1 Introduction to molecular dynamics simulation . ........ 9 2.2 Definition and derivation of principal components . ......... 11 2.3 Circularstatistics ................................ 12 2.4 Dihedral angle principal component analysis (dPCA) . ....... 18 2.5 Asimpleexample-trialanine . 19 2.6 Interpretation of eigenvectors . ..... 22 2.7 ComplexdPCA ................................. 25 2.8 Energylandscapeofdecaalanine. 27 2.9 CartesianPCA ................................. 31 2.10 DirectangularPCA............................... 35 2.11 Correlationanalysis. 39 2.12 Nonlinear principal component analysis . ...... 42 2.13Conclusions ................................... 43 3 Free Energy Landscape 47 3.1 Introduction................................... 47 3.2 Clustering ...................................
    [Show full text]
  • Energy Landscapes for Proteins: from Single Funnels to Multifunctional Systems
    Energy Landscapes for Proteins: From Single Funnels to Multifunctional Systems K. R¨oder,1,† J. A. Joseph,1,† Brooke E. Husic1,2 and D. J. Wales1,∗ 1: Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW, Cambridge, UK 2: Current affiliation: Chemistry Department, Stanford University, 333 Campus Drive, Stanford, CA 94305, USA ∗ Corresponding author: [email protected] † These authors contributed equally. Article type: Progress Report Keywords: Energy landscapes, protein folding, multifunnel paradigm In this report we advance the hypothesis that multifunctional systems may be associated with multifunnel potential and free energy landscapes, with particular focus on biomolecules. We compare systems that exhibit single, double, and multiple competing structures, and contrast multifunnel landscapes associated with misfolded amyloidogenic oligomers, which presumably do not arise as an evolutionary target. In this context, intrinsically disordered proteins could be considered intrinsically multifunctional molecules, associated with multifunnel landscapes. Potential energy landscape theory enables biomolecules to be treated in a common framework together with self-organising and multifunctional systems based on inorganic materials, atomic and molecular clusters, crystal polymorphs, and soft matter. 1 Introduction The field of protein science underwent dramatic transformations with the groundbreaking ex- periments of Sanger[1] and Kendrew,[2] characterising the sequence of insulin and the three- dimensional structure of myoglobin,
    [Show full text]
  • Making the Best of a Bad Situation: a Multiscale Approach to Free Energy Calculation Arxiv:1901.04455V3 [Physics.Comp-Ph] 22 F
    Making the best of a bad situation: a multiscale approach to free energy calculation Michele Invernizzi∗,y,{ and Michele Parrinelloz,{ yDepartment of Physics, ETH Zurich c/o USI Campus, Lugano, Switzerland zDepartment of Chemistry and Applied Biosciences, ETH Zurich c/o USI Campus, Lugano, Switzerland {Facolt`adi Informatica, Instituto di Scienze Computationali, and National Center for Computational Design and Discovery of Novel Materials MARVEL, Universit`adella Svizzera italiana (USI), Via Giuseppe Buffi 13, CH-6900 Lugano, Switzerland E-mail: [email protected] Abstract Many enhanced sampling techniques rely on the identification of a number of col- lective variables that describe all the slow modes of the system. By constructing a bias potential in this reduced space one is then able to sample efficiently and reconstruct the free energy landscape. In methods like metadynamics, the quality of these collective variables plays a key role in convergence efficiency. Unfortunately in many systems of interest it is not possible to identify an optimal collective variable, and one must deal arXiv:1901.04455v3 [physics.comp-ph] 22 Feb 2019 with the non-ideal situation of a system in which some slow modes are not accelerated. We propose a two-step approach in which, by taking into account the residual mul- tiscale nature of the problem, one is able to significantly speed up convergence. To do so, we combine an exploratory metadynamics run with an optimization of the free en- ergy difference between metastable states, based on the recently proposed variationally 1 enhanced sampling method. This new method is well parallelizable and is especially suited for complex systems, because of its simplicity and clear underlying physical picture.
    [Show full text]
  • Mapping Transiently Formed and Sparsely Populated Conformations on a Complex Energy Landscape Yong Wang, Elena Papaleo, Kresten Lindorff-Larsen*
    RESEARCH ARTICLE Mapping transiently formed and sparsely populated conformations on a complex energy landscape Yong Wang, Elena Papaleo, Kresten Lindorff-Larsen* Structural Biology and NMR Laboratory, Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark Abstract Determining the structures, kinetics, thermodynamics and mechanisms that underlie conformational exchange processes in proteins remains extremely difficult. Only in favourable cases is it possible to provide atomic-level descriptions of sparsely populated and transiently formed alternative conformations. Here we benchmark the ability of enhanced-sampling molecular dynamics simulations to determine the free energy landscape of the L99A cavity mutant of T4 lysozyme. We find that the simulations capture key properties previously measured by NMR relaxation dispersion methods including the structure of a minor conformation, the kinetics and thermodynamics of conformational exchange, and the effect of mutations. We discover a new tunnel that involves the transient exposure towards the solvent of an internal cavity, and show it to be relevant for ligand escape. Together, our results provide a comprehensive view of the structural landscape of a protein, and point forward to studies of conformational exchange in systems that are less characterized experimentally. DOI: 10.7554/eLife.17505.001 Introduction *For correspondence: lindorff@ Proteins are dynamical entities whose ability to change shape often plays essential roles in bio.ku.dk their function. From an experimental point of view, intra-basin dynamics is often described via con- Competing interests: The formational ensembles whereas larger scale (and often slower) motions are characterized as authors declare that no a conformational exchange between distinct conformational states.
    [Show full text]
  • Energy Landscape Underlying Spontaneous Insertion and Folding of an Alpha-Helical Transmembrane Protein Into a Bilayer
    ARTICLE DOI: 10.1038/s41467-018-07320-9 OPEN Energy landscape underlying spontaneous insertion and folding of an alpha-helical transmembrane protein into a bilayer Wei Lu1,2, Nicholas P. Schafer1,3 & Peter G. Wolynes1,2,3,4 Membrane protein folding mechanisms and rates are notoriously hard to determine. A recent force spectroscopy study of the folding of an α-helical membrane protein, GlpG, showed that 1234567890():,; the folded state has a very high kinetic stability and a relatively low thermodynamic stability. Here, we simulate the spontaneous insertion and folding of GlpG into a bilayer. An energy landscape analysis of the simulations suggests that GlpG folds via sequential insertion of helical hairpins. The rate-limiting step involves simultaneous insertion and folding of the final helical hairpin. The striking features of GlpG’s experimentally measured landscape can therefore be explained by a partially inserted metastable state, which leads us to a reinter- pretation of the rates measured by force spectroscopy. Our results are consistent with the helical hairpin hypothesis but call into question the two-stage model of membrane protein folding as a general description of folding mechanisms in the presence of bilayers. 1 Center for Theoretical Biological Physics, Rice University, Houston 77005 TX, USA. 2 Department of Physics, Rice University, Houston 77005 TX, USA. 3 Department of Chemistry, Rice University, Houston 77005 TX, USA. 4 Department of Biosciences, Rice University, Houston 77005 TX, USA. These authors contributed equally:
    [Show full text]
  • The Protein Folding Problem
    The Protein Folding Problem Ken A. Dill,1,2 S. Banu Ozkan,3 M. Scott Shell,4 and Thomas R. Weikl5 1Department of Pharmaceutical Chemistry, 2Graduate Group in Biophysics, University of California, San Francisco, California 94143; email: [email protected] 3Department of Physics, Arizona State University, Tempe, Arizona 85287; email: [email protected] 4Department of Chemical Engineering, University of California, Santa Barbara, California 93106; email: [email protected] 5Max Planck Institute of Colloids and Interfaces, Department of Theory and Bio-Systems, 14424 Potsdam, Germany; email: [email protected] Annu. Rev. Biophys. 2008. 37:289–316 Key Words The Annual Review of Biophysics is online at structure prediction, funnel energy landscapes, CASP, folding biophys.annualreviews.org code, folding kinetics This article’s doi: 10.1146/annurev.biophys.37.092707.153558 Abstract Copyright c 2008 by Annual Reviews. The “protein folding problem” consists of three closely related puz- All rights reserved zles: (a) What is the folding code? (b) What is the folding mechanism? 1936-122X/08/0609-0289$20.00 (c) Can we predict the native structure of a protein from its amino acid sequence? Once regarded as a grand challenge, protein fold- ing has seen great progress in recent years. Now, foldable proteins and nonbiological polymers are being designed routinely and mov- ing toward successful applications. The structures of small proteins are now often well predicted by computer methods. And, there is now a testable explanation for how a protein can fold so quickly: A protein solves its large global optimization problem as a series of smaller local optimization problems, growing and assembling the native structure from peptide fragments, local structures first.
    [Show full text]