Residue packing in proteins: Uniform distribution on a coarse-grained scale

Bagci, Elife Zerrin; Jernigan, Robert L.; Bahar, İvet

doi:10.1063/1.1432502

Cited by 38 publications

(35 citation statements)

References 44 publications

(35 reference statements)

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“…It was also noticed that an average value was calculated for radii of chains with the same number of amino acids. This intrinsic characteristic of the protein structures must be responsible for explaining several aspects of these molecules such as the high compactness, which has been discussed in several other different contexts [1,2,14,15,16,17,18,19]. The ν exponent associated with the average radius obtained is 0.404 ± 0.008, which is in agreement with recent similar study involving 200 proteins [20].…”

supporting

confidence: 90%

Dihedral-angle Gaussian distribution driving protein folding

Figueirêdo

Moret

Nogueira

et al. 2008

Physica A: Statistical Mechanics and its Applications

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The proposal of this paper is to provide a simple angular random walk model to build up polypeptide structures, which encompass properties of dihedral angles of folded proteins. From this model, structures will be built with lengths ranging from 125 up to 400 amino acids for the different fractions of secondary structure motifs, which dihedral angles were randomly chosen according to narrow Gaussian probability distributions. In order to measure the fractal dimension of proteins three different cases were analyzed. The first contained α-helix structures only, the second β-strands structures and the third a mix of α-helices and β-sheets. The behavior of proteins with α-helix motifs are more compacted than in other situations. The findings herein indicate that this model describes some structural properties of a protein and suggest that randomness is an essential ingredient but proteins are driven by narrow angular Gaussian probability distributions and not by randomwalk processes. The manner in which a protein folds from a random coil into a unique native state in a relatively short time is one of the fundamental puzzles of molecular biophysics. It is well accepted that a unique native three-dimensional structure, characteristic of each protein and determined by the sequence of its amino-acids sequence, dictates protein functions. The folding process should involve a very complex molecular recognition phenomenon depending on the interplay of many relatively weak non-bonded interactions. This would leads to a huge number of possible final conformations under conventional molecular optimization methods based on the search for the minima of the energy hypersurface. This number, which should increases with the number of the chain's degrees of freedom, however, is severely restricted during the real folding process, excluding relevant portions of the energy landscapes as far as an extended or random conformation is chosen as the initial state [1,2,3,4,5,6,7,8,9,10]. On the other hand, if the extreme limit, were considered, where a polypeptide chain departs from its denatured state and in very relatively short period of time finds its unique native state after searching amongst the astronomical number of possible configurations, the simulating process for proteins with fifty to five hundred amino acids using approaches such as Monte Carlo and molecular dynamics, becomes impracticable, due to the very high computation cost. Such contradictory dynamical picture is known as Levinthal paradoxTo investigate the role of stochasticity on the final native state, an inverse strategy is proposed, based on a simple angular 3D random-walk model to build up protein backbones with different lengths and distinct percentages of secondary structures. In the proposed model, each step has a fixed radial size l 0 but dihedral Φ and Ψ angles of the protein backbone are chosen according to independent Gaussian probability distributions, following the suggestion given in reference [12]. The mean value and standard deviation of each d...

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supporting

confidence: 90%

Dihedral-angle Gaussian distribution driving protein folding

Figueirêdo

Moret

Nogueira

et al. 2008

Physica A: Statistical Mechanics and its Applications

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“…It is known that the relative orientation of side chains is an important determinant of the local (secondary structure) geometry as well as three-dimensional (3-D) (tertiary structure) topology [5,15,16]. By analyzing various families of structures, we observed that certain orientational order parameters are prominent [17].…”

Section: Introductionmentioning

confidence: 90%

Continuous anisotropic representation of coarse-grained potentials for proteins by spherical harmonics synthesis

Buchete

Straub

Thirumalai

2004

Journal of Molecular Graphics and Modelling

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“…It lacks the parity problem and models real protein conformations with good quality (see [38], where it was shown, that the FCC lattice can model protein conformations with coordinate root mean square deviation of 1.78 Å , whereas the cubic lattice achieves a deviation of 2.84 Å there). Recently, [13,14] have shown that neighborhood of amino acids in proteins closely resembles a distorted FCC-lattice, and that the FCC is best suited for modeling proteins. This is an immediate effect of hydrophobic packing.…”

Section: Contribution Of the Paper: A Constraint-based Approachmentioning

confidence: 99%

A Constraint-Based Approach to Fast and Exact Structure Prediction in Three-Dimensional Protein Models

Backofen

Will

2006

Constraints

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Simplified protein models are used for investigating general properties of proteins and principles of protein folding. Furthermore, they are suited for hierarchical approaches to protein structure prediction. A well known protein model is the HP-model of Lau and Dill [Lau, K. F., & Dill, K. A. (1989)]. A lattice statistical mechanics model of the conformational and sequence spaces of proteins. Macromolecules, 22, 3986 -3997) which models the important aspect of hydrophobicity. One can define the HP-model for various lattices, among them two-dimensional and three-dimensional ones. Here, we investigate the three-dimensional case. The main motivation for studying simplified protein models is to be able to predict model structures much more quickly and more accurately than is possible for real proteins. However, up to now there was a dilemma: the algorithmically tractable, simple protein models can not model real protein structures with good quality and introduce strong artifacts.We present a constraint-based method that largely improves this situation. It outperforms all existing approaches for lattice protein folding in HP-models. This approach is the first one that can be applied to two three-dimensional lattices, namely the cubic lattice and the face-centered-cubic (FCC) lattice. Moreover, it is the only exact method for the FCC lattice. The ability to use the FCC lattice is a significant improvement over the cubic lattice. The key to our approach is the ability to compute maximally compact sets of points (used as hydrophobic cores), which we accomplish for the first time for the FCC lattice.

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Residue packing in proteins: Uniform distribution on a coarse-grained scale

Cited by 38 publications

References 44 publications

Dihedral-angle Gaussian distribution driving protein folding

Dihedral-angle Gaussian distribution driving protein folding

Continuous anisotropic representation of coarse-grained potentials for proteins by spherical harmonics synthesis

A Constraint-Based Approach to Fast and Exact Structure Prediction in Three-Dimensional Protein Models

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