Structural information content and Lyapunov exponent calculation in protein unfolding

Gerstman, Bernard S.; Garbourg, Yoni

doi:10.1002/(sici)1099-0488(19981115)36:15<2761::aid-polb10>3.0.co;2-5

Cited by 18 publications

(4 citation statements)

References 19 publications

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“…However, this does not imply that the two observed trajectories are an unbiased sample of trajectories near the terminus since kinetics may play a role [16] and thus, state space properties near the attractor cannot be inferred directly. The number of time points monitored in the experiments also does not allow a detailed state space analysis as in other high-dimensional dynamic systems [17].…”

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confidence: 99%

Cell Fates as High-Dimensional Attractor States of a Complex Gene Regulatory Network

et al. 2005

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Cells in multicellular organisms switch between distinct cell fates, such as proliferation or differentiation into specialized cell types. Genome-wide gene regulatory networks govern this behavior. Theoretical studies of complex networks suggest that they can exhibit ordered (stable) dynamics, raising the possibility that cell fates may represent high-dimensional attractor states. We used gene expression profiling to show that trajectories of neutrophil differentiation converge to a common state from different directions of a 2773-dimensional gene expression state space, providing the first experimental evidence for a high-dimensional stable attractor that represents a distinct cellular phenotype.

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confidence: 99%

Cell Fates as High-Dimensional Attractor States of a Complex Gene Regulatory Network

et al. 2005

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“…The last few decades have witnessed an enormous surge in interest [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] in modeling proteins. Much attention is focused on identifying the universal characteristics, e.g., folding pathways via analysis of the energy landscapes of protein chains as well as their specific characteristics that entail local structures to understand binding to pertinent targets.…”

Section: Globular Structure Of a Human Immunodeficiency Virus-1 Protementioning

confidence: 99%

Globular structure of a human immunodeficiency virus-1 protease (1DIFA dimer) in an effective solvent medium by a Monte Carlo simulation

Pandey

Farmer

2010

The Journal of Chemical Physics

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A coarse-grained model is used to study the structure and dynamics of a human immunodeficiency virus-1 protease (1DIFA dimer) consisting of 198 residues in an effective solvent medium on a cubic lattice by Monte Carlo simulations for a range of interaction strengths. Energy and mobility profiles of residues are found to depend on the interaction strength and exhibit remarkable segmental symmetries in two monomers. Lowest energy residues such as Arg41 and Arg140 (most electrostatic and polar) are not the least mobile; despite the higher energy, the hydrophobic residues (Ile, Leu, and Val) are least mobile and form the core by pinning down the local segments for the globular structure. Variations in the gyration radius (Rg) and energy (Ec) of the protein show nonmonotonic dependence on the interaction strength with the smallest Rg around the largest value of Ec. Pinning of the conformations by the hydrophobic residues at high interaction strength seems to provide seed for the protein chain to collapse.

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“…[25][26][27] The model we use [28][29][30][31] includes separate degrees of freedom for amino acid backbones and side chains, allows amino acids to be distinguished by endowing them with various properties such as hydrophobicity or hydrophilicity, and uses a lattice that allows the representation of secondary and tertiary structures. 34 The underlying lattice is a simple cubic lattice. 34 The underlying lattice is a simple cubic lattice.…”

Section: B Computer Lattice Modelmentioning

confidence: 99%

Sampling of states for estimating the folding funnel entropy and energy landscape of a model alpha-helical hairpin peptide

Chapagain

Parra

Gerstman

et al. 2007

The Journal of Chemical Physics

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Protein folding times are many orders of magnitude shorter than would occur if the peptide chain randomly sampled possible configurations, which implies that protein folding is a directed process. The detailed shape of protein's energy landscape determines the rate and reliability of folding to the native state, but the large number of structural degrees of freedom generates an energy landscape that is hard to visualize because of its high dimensionality. A commonly used picture is that of an energy funnel leading from high energy random coil state down to the low energy native state. As lattice computer models of protein dynamics become more realistic, the number of possible configurations becomes too large to count directly. Statistical mechanic and thermodynamic approaches allow us to count states in an approximate manner to quantify the entropy and energy of the energy landscape within a folding funnel for an alpha-helical protein. We also discuss the problems that arise in attempting to count the huge number of individual states of the random coil at the top of the funnel.

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Structural information content and Lyapunov exponent calculation in protein unfolding

Cited by 18 publications

References 19 publications

Cell Fates as High-Dimensional Attractor States of a Complex Gene Regulatory Network

Cell Fates as High-Dimensional Attractor States of a Complex Gene Regulatory Network

Globular structure of a human immunodeficiency virus-1 protease (1DIFA dimer) in an effective solvent medium by a Monte Carlo simulation

Sampling of states for estimating the folding funnel entropy and energy landscape of a model alpha-helical hairpin peptide

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