Direct measurement of protein energy landscape roughness

Nevo, Reinat; Brumfeld, Vlad; Kapon, Ruti; Hinterdorfer, Peter; Reich, Ziv

doi:10.1038/sj.embor.7400403

Cited by 105 publications

(122 citation statements)

References 38 publications

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“…[66] It was shown that energy surfaces of single transmembrane a-helices, as well as pairs, contain considerable local energy corrugations with a roughness scale (e) of % 5 k B T (Figure 6 b). [66] Since a rough energy surface is a prerequisite for functionally related conformational changes and local flexibilities, [5,11,95] the finding that the energy surfaces of transmembrane a-helices are rugged is in line with the vital roles of ahelices as structural and functional building blocks. The determined energy surface ruggedness of transmembrane a-helices is of the same magnitude as that of short peptides ( % 2 k B T), small globular proteins (< 5 k B T), and the force-induced dissociation of two interacting proteins ( % 5 k B T) or DNA strands ( % 10 k B T).…”

Section: Transmembrane A-helices Have Rough Energy Surfacesmentioning

confidence: 86%

“…Based on Zwanzig's work Hyeon and Thirumalai recently established the basis for experimentally probing the roughness of two-state energy landscapes (Figure 6 a) using DFS at different temperatures. [94] A modified form of the model [95] was combined with temperature-dependent DFS experiments to determine the energy landscape roughness of the transmembrane a-helices of bacteriorhodopsin. [66] It was shown that energy surfaces of single transmembrane a-helices, as well as pairs, contain considerable local energy corrugations with a roughness scale (e) of % 5 k B T (Figure 6 b).…”

Section: Transmembrane A-helices Have Rough Energy Surfacesmentioning

confidence: 99%

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From Valleys to Ridges: Exploring the Dynamic Energy Landscape of Single Membrane Proteins

et al. 2008

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Membrane proteins are involved in essential biological processes such as energy conversion, signal transduction, solute transport and secretion. All biological processes, also those involving membrane proteins, are steered by molecular interactions. Molecular interactions guide the folding and stability of membrane proteins, determine their assembly, switch their functional states or mediate signal transduction. The sequential steps of molecular interactions driving these processes can be described by dynamic energy landscapes. The conceptual energy landscape allows to follow the complex reaction pathways of membrane proteins while its modifications describe why and how pathways are changed. Single-molecule force spectroscopy (SMFS) detects, quantifies and locates interactions within and between membrane proteins. SMFS helps to determine how these interactions change with temperature, point mutations, oligomerization and the functional states of membrane proteins. Applied in different modes, SMFS explores the co-existence and population of reaction pathways in the energy landscape of the protein and thus reveals detailed insights into local mechanisms, determining its structural and functional relationships. Here we review how SMFS extracts the defining parameters of an energy landscape such as the barrier position, reaction kinetics and roughness with high precision.

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Section: Transmembrane A-helices Have Rough Energy Surfacesmentioning

confidence: 86%

Section: Transmembrane A-helices Have Rough Energy Surfacesmentioning

confidence: 99%

From Valleys to Ridges: Exploring the Dynamic Energy Landscape of Single Membrane Proteins

et al. 2008

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“…The requirement of gp120 mutations for maC46 resistance, which was not observed for resistance to soluble CFI, could be explained by the different modes of binding to the target site for soluble and membrane-bound C peptides. Peptides are not rigid structures but are better represented as a manifold of low-energy conformations and conformational substrates (energy landscape) (4,20,26). The binding of a flexible soluble peptide to its target is governed purely by random molecular diffusion, and it can therefore be assumed that upon its first encounter with the target region, only a small segment of each peptide will be aligned correctly.…”

Section: Vol 83 2009mentioning

confidence: 99%

Mutations in gp120 Contribute to the Resistance of Human Immunodeficiency Virus Type 1 to Membrane-Anchored C-Peptide maC46

Hermann

Egerer

Brauer

et al. 2009

J Virol

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Binding of the human immunodeficiency virus (HIV) envelope glycoprotein (Env) to the cellular CD4 receptor and a chemokine coreceptor initiates a series of conformational changes in the Env subunits gp120 and gp41. Eventually, the trimeric gp41 folds into a six-helix bundle, thereby inducing fusion of the viral and cellular membranes. C peptides derived from the C-terminal heptad repeat (CHR) of gp41 are efficient entry inhibitors as they block the six-helix bundle formation. Previously, we developed a membrane-anchored C peptide (maC46) expressed from a retroviral vector that also shows high activity against virus strains resistant to enfuvirtide (T-20), an antiviral C peptide approved for clinical use. Here, we present a systematic analysis of mutations in Env that confer resistance of HIV type 1 (HIV-1) to maC46. We selected an HIV-1 BaL strain with 10-fold reduced sensitivity to maC46 (BaL_C46) by passaging virus for nearly 200 days in the presence of gradually increasing concentrations of maC46. In comparison to wild-type BaL, BaL_C46 had five mutations at highly conserved positions in Env, three in gp120, one in the N-terminal heptad-repeat (NHR), and one in the CHR of gp41. No mutations were found in the NHR domain around the GIV motif that are known to cause resistance to enfuvirtide. Instead, maC46 resistance was found to depend on complementary mutations in the NHR and CHR that considerably favor binding of the mutated NHR to the mutated CHR over binding to maC46. In addition, resistance was highly dependent on mutations in gp120 that accelerated entry. Taken together, resistance to maC46 did not develop readily and required multiple cooperating mutations at conserved positions of the viral envelope glycoproteins gp120 and gp41.

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“…Recently, single-molecule techniques have been used to probe features of the energy landscape of proteins and RNA that are not easily accessible in ensemble experiments (7-18). It is possible to construct the shape of the energy landscape, including the energy scales of ruggedness (19,20), by using dynamical trajectories that are generated by applying a constant force ( f ) to the ends of proteins and RNA (14,15,21,22). If the observation time is long enough for the molecule to sample the accessible conformational space, then the time average of an observable X recorded for the ␣th molecule [͗X͘ ϭ t3ϱ…”

mentioning

confidence: 99%

Force-dependent hopping rates of RNA hairpins can be estimated from accurate measurement of the folding landscapes

Hyeon

Morrison²,

Thirumalai

2008

Proc. Natl. Acad. Sci. U.S.A.

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The sequence-dependent folding landscapes of nucleic acid hairpins reflect much of the complexity of biomolecular folding. Folding trajectories, generated by using single-molecule force-clamp experiments by attaching semiflexible polymers to the ends of hairpins, have been used to infer their folding landscapes. Using simulations and theory, we study the effect of the dynamics of the attached handles on the handle-free RNA free-energy profile F eq o (zm), where zm is the molecular extension of the hairpin. A molecular understanding of how proteins and RNA fold is needed to describe the functions of enzymes (1) and ribozymes (2), interactions between biomolecules, and the origins of misfolding that is linked to a number of diseases (3). The energy-landscape perspective has provided a conceptual framework for describing the mechanisms by which unfolded molecules navigate the large conformational space in search of the native state (4-6). Recently, single-molecule techniques have been used to probe features of the energy landscape of proteins and RNA that are not easily accessible in ensemble experiments (7-18). It is possible to construct the shape of the energy landscape, including the energy scales of ruggedness (19,20), by using dynamical trajectories that are generated by applying a constant force ( f ) to the ends of proteins and RNA (14,15,21,22). If the observation time is long enough for the molecule to sample the accessible conformational space, then the time average of an observable X recorded for the ␣th molecule [͗X͘ ϭ t3ϱ, and the distribution P(X) should converge to the equilibrium distribution function P eq (X). By using this strategy, laser optical tweezer (LOT) experiments have been used to obtain the sequence-dependent folding landscape of a number of RNA and DNA hairpins (8,14,15,23), by using X ϭ R m , the end-to-end distance of the hairpin that is conjugate to f, as a natural reaction coordinate. In LOT experiments, the hairpin is held between two long handles [DNA (15) or DNA/RNA hybrids (8)], whose ends are attached to polystyrene beads (Fig. 1a). The equilibrium free-energy profile ␤F eq (R m ) ϭ ϪlogP eq (R m ) (␤ ϵ 1/k B T, k B is the Boltzmann constant, and T is the absolute temperature) may be useful in describing the dynamics of the molecule, provided R m is an appropriate reaction coordinate.The dynamics of the RNA extension in the presence of f (z m ϭ z 3Ј Ϫ z 5Ј Ϸ R m , provided transverse fluctuations are small) is indirectly obtained in an LOT experiment by monitoring the distance between the attached polystyrene beads (z sys ϭ z p Ϫ z o ), one of which is optically trapped at the center of the laser focus (Fig. 1a). The goal of these experiments is to extract the folding landscape [␤F eq o (z m )] and the dynamics of the hairpin in the absence of handles by using the f-dependent trajectories z sys (t). To achieve these goals, the fluctuations in the handles should minimally perturb the dynamics of the hairpin to probe the true dynamics of a molecule of interest. However, dependi...

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Direct measurement of protein energy landscape roughness

Cited by 105 publications

References 38 publications

From Valleys to Ridges: Exploring the Dynamic Energy Landscape of Single Membrane Proteins

From Valleys to Ridges: Exploring the Dynamic Energy Landscape of Single Membrane Proteins

Mutations in gp120 Contribute to the Resistance of Human Immunodeficiency Virus Type 1 to Membrane-Anchored C-Peptide maC46

Force-dependent hopping rates of RNA hairpins can be estimated from accurate measurement of the folding landscapes

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