Pair potentials for protein folding: Choice of reference states and sensitivity of predicted native states to variations in the interaction schemes

Betancourt, Marcos R.; Thirumalai, D.

doi:10.1110/ps.8.2.361

Cited by 329 publications

(343 citation statements)

References 28 publications

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“…The power-law dependence is conserved even for the complex topology of a yeast protein interaction network fragment (14) (Fig. 3D) and for networks optimized with alternative energy functions (29,30). A power-law decrease was also found in lattice-model calculations of the energy gaps separating the native and the most-competitive nonnative structures of proteins as a function of their length, reflecting a similar increase in the combinatorial possibilities for nonspecific interactions (22).…”

Section: Resultsmentioning

confidence: 69%

Nonspecific binding limits the number of proteins in a cell and shapes their interaction networks

Johnson

Hummer

2010

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

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Multicellular organisms, from Caenorhabditis elegans to humans, have roughly the same number of protein encoding genes. We show that the need to prevent disease-causing nonspecific interactions between proteins provides a simple physical reason why organism complexity is not reflected in the number of distinct proteins. By collective evolution of the amino acid sequences of protein binding interfaces we estimate the degree of misbinding as a function of the number of distinct proteins. Protein interaction energies are calculated with an empirical, residue-specific energy function tuned for protein binding. We show that the achievable energy gap favoring specific over nonspecific binding decreases with protein number in a power-law fashion. From the fraction of proteins involved in nonspecific complexes as a function of increasing protein number and decreasing energy gap, we predict the limits these binding requirements place on the number of different proteins that can function effectively in a given cellular compartment. Remarkably, the optimization of binding interfaces favors networks in which a few proteins have many partners, and most proteins have few partners, consistent with a scale-free network topology. We conclude that nonspecific binding adds to the evolutionary pressure to develop scale-free protein-protein interaction networks. T he number of proteins encoded by the genomes of humans and the nematode Caenorhabditis elegans is remarkably similar, ∼20;000 each (1), with comparable numbers for other eukaryotes (2). Large differences in organism complexity are thus reflected far less in proteome size than in gene regulatory networks (3), the degree of compartmentalization (4), the variety of distinct cell types (2), and alternative splicing (5). In this work, we provide a physical explanation for the absence of an increase in protein diversity from simple multicellular organisms to humans. Our approach seeks to capture the fundamental aspects of protein interactions conserved in any functioning cell, namely high binding specificity and minimal aggregation as the proteins participate in a network of binding interactions.The networks of protein-protein interactions, or interactomes (6-9), although distinctive to their individual organisms, manifest global and local characteristics that are shared across species (10, 11). Most notably, the organization of these networks exhibits a scale-free topology (10, 12) with a substantial number of highly connected hub proteins (13). Based on structural (14) and temporal (15) information, the hub proteins can be classified as date hubs or party hubs. In a date hub, multiple binding partners compete for binding to a single interface, where binding to one partner excludes simultaneous binding to any of the others. In a party hub, a protein has multiple binding interfaces that are accessible independent of each other, such that binding is not competitive. Collectively, these network features are relevant functionally by creating robust, modular interactomes (15, 16) an...

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Section: Resultsmentioning

confidence: 69%

Nonspecific binding limits the number of proteins in a cell and shapes their interaction networks

Johnson

Hummer

2010

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

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“…The α-helical secondary structure is stabilized by interactions which mimic (i, i + 4) hydrogen bonding (32). We use residuedependent energies for tertiary interactions (30). Simulations are carried out using an overdamped Brownian dynamics algorithm (33).…”

Section: Methodsmentioning

confidence: 99%

From mechanical folding trajectories to intrinsic energy landscapes of biopolymers

Hinczewski

Gebhardt

Rief

et al. 2013

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

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In single-molecule laser optical tweezer (LOT) pulling experiments, a protein or RNA is juxtaposed between DNA handles that are attached to beads in optical traps. The LOT generates folding trajectories under force in terms of time-dependent changes in the distance between the beads. How to construct the full intrinsic folding landscape (without the handles and beads) from the measured time series is a major unsolved problem. By using rigorous theoretical methods-which account for fluctuations of the DNA handles, rotation of the optical beads, variations in applied tension due to finite trap stiffness, as well as environmental noise and limited bandwidth of the apparatus-we provide a tractable method to derive intrinsic free-energy profiles. We validate the method by showing that the exactly calculable intrinsic freeenergy profile for a generalized Rouse model, which mimics the two-state behavior in nucleic acid hairpins, can be accurately extracted from simulated time series in a LOT setup regardless of the stiffness of the handles. We next apply the approach to trajectories from coarse-grained LOT molecular simulations of a coiled-coil protein based on the GCN4 leucine zipper and obtain a free-energy landscape that is in quantitative agreement with simulations performed without the beads and handles. Finally, we extract the intrinsic free-energy landscape from experimental LOT measurements for the leucine zipper. T he energy landscape perspective has provided a conceptual framework to describe how RNA (1) and proteins (2-4) fold. Some of the key theoretical predictions (5, 6), have been confirmed by experiments (7). More refined comparisons require mapping the full folding landscape of biomolecules. Advances in laser optical tweezer (LOT) experiments have been used to obtain free-energy profiles as a function of the extension of biomolecules under tension (7-12).The usefulness of the LOT technique, however, hinges on the assumption that information about the fluctuating biomolecule can be accurately recovered from the raw experimental data, namely the time-dependent changes in the positions of the beads in the optical traps, attached to the biomolecule by doublestranded DNA (dsDNA) handles (Fig. 1). Thus, we only have access to the intrinsic folding landscape of the biomolecule (in the absence of handles and beads) indirectly through the bead-bead separation along the force direction. Many extraneous factors, such as fluctuations of the handles (13, 14), rotation of the beads, and the varying applied tension due to finite trap stiffness, can distort the intrinsic folding landscape. Moreover, the detectors and electronic systems used in the data collection have finite response times, leading to filtering of high-frequency components in the signal (15). Ad hoc attempts have been made to account for handle effects based on experimental estimates of stretched DNA properties, using techniques similar to image deconvolution (8,11,16). Theory has been used to extract free-energy information from nonequilibrium pullin...

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“…On the residue level, the strength of pairwise interactions could be determined statistically based on their occurrences in native structures, and are stored in the form of matrix [72][73][74][80][81][82]. The features of proteins have already been implicitly included.…”

Section: Simplification Based On Pairwise Interaction Between Amino Amentioning

confidence: 99%

Simplification of complexity in protein molecular systems by grouping amino acids: a view from physics

Wang

2016

Advances in Physics: X

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Pair potentials for protein folding: Choice of reference states and sensitivity of predicted native states to variations in the interaction schemes

Cited by 329 publications

References 28 publications

Nonspecific binding limits the number of proteins in a cell and shapes their interaction networks

Nonspecific binding limits the number of proteins in a cell and shapes their interaction networks

From mechanical folding trajectories to intrinsic energy landscapes of biopolymers

Simplification of complexity in protein molecular systems by grouping amino acids: a view from physics

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