The relationship between peptide plane rotation (PPR) and similar conformations

Parker, J. M. R.

doi:10.1002/(sici)1096-987x(19990715)20:9<947::aid-jcc6>3.0.co;2-s

Cited by 3 publications

(4 citation statements)

References 43 publications

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“…In addition, the 400 c i -f i11 distributions accounting for the statistics in the two dihedral angles between specified adjacent amino acids have also been constructed and are presented. The latter distributions have been seen to contain nontrivial structure and the present results-over the existing larger database-serve to validate prior conclusions (19)(20)(21)(22). The information-theory entropy, S, is defined in terms of the probabilities (or likelihood) of particular pairs of dihedral angles along the protein given its primary structure.…”

Section: Introductionsupporting

confidence: 72%

“…For example, flanking residues are known to affect the probability distribution in the dihedral angles of a given residue (24)(25)(26)(27)(41)(42)(43)(44)(45)(46)(47). As previously suggested, one defines the P Ri;Ri11 ðc i ; f i11 Þ distributions-in which the angles are associated with the sequential residues-to complement the information in the Ramachandran plot (19)(20)(21)(22). Since the c i -f i11 plot accounts for the correlation between two adjacent residues, its use in structure assessment provides a nontrivial sequence-dependent measure of the likelihood that a given pair of residues will be connected by the specified dihedral angles.…”

Section: Methods F I -C I and C I -F I11 Distributionsmentioning

confidence: 99%

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Dihedral-Angle Information Entropy as a Gauge of Secondary Structure Propensity

Zhong

Moix

Quirk³

et al. 2006

Biophysical Journal

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Protein structural information can be uncovered using an information-theory-based entropy and auxiliary functions by taking advantage of high-quality correlation plots between the dihedral angles around a residue and those between sequential residues. A standard information entropy for a primary sequence has been defined using the values of the probabilities of the most likely dihedral angles along the sequence. The distribution of entropy differences relative to the standard for each protein in a reference set--a sublibrary of the Protein Data Bank at the 90% sequence redundancy level--appears to be nearly Gaussian. It gives rise to an auxiliary checking function whose value signals the extent to which the dihedral angle propensities differ from typical structures. Such deviations can arise either because of incorrect dihedral angle assignments or secondary structural propensities that are atypical of the structures in the reference set. This auxiliary checking function can be readily calculated at the public website, (http://www.d2check.gatech.edu). Its utility is demonstrated here in an analysis displaying differences between experimentally and theoretically derived structures, and in the analysis of structures derived by homology modeling. A comparison of the new measure, D(2)Check, to other checking functions based on backbone conformation-namely, PROCHECK and WHAT_CHECK--is also provided.

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Section: Introductionsupporting

confidence: 72%

Section: Methods F I -C I and C I -F I11 Distributionsmentioning

confidence: 99%

Dihedral-Angle Information Entropy as a Gauge of Secondary Structure Propensity

Zhong

Moix

Quirk³

et al. 2006

Biophysical Journal

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“…Our choice for a single-angle representation of the mechanical degrees of freedom of a monomer was motivated by our desire to limit the mathematical complexity, although our methods would apply also if we were to work with the conventional two conformation angles (φ, ψ). In fact, there is evidence [40] to suggest that the conventional two-angle representation is redundant, and that only one newly defined torsion angle is needed per amino-acid to specify a protein's conformation. If we insist on identifying the single-site degrees of freedom in our model with one of the standard conformation angles (φ, ψ), we have to choose the one that matches our statistical assumptions best.…”

Section: Relation Between Model Assumptions and Biological Realitymentioning

confidence: 99%

“…Equations (26,27,34,35) (where we need the eigenfunctions with the largest eigenvalue) together with the supporting expressions (38,39,40,41,42,43,44) constitute a closed set of equations for the RS order parameters {m, k, Φ(x), Ψ(x)} of our model. We now simplify this set further.…”

Section: Simplified Form Of the Theorymentioning

confidence: 99%

A solvable model of the genesis of amino-acid sequences via coupled dynamics of folding and slow-genetic variation

Rabello¹,

Coolen²,

Perez-Vicente³

et al. 2008

J. Phys. A: Math. Theor.

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We study the coupled dynamics of primary and secondary structure formation (i.e. slow genetic sequence selection and fast folding) in the context of a solvable microscopic model that includes both short-range steric forces and and long-range polarity-driven forces. Our solution is based on the diagonalization of replicated transfer matrices, and leads in the thermodynamic limit to explicit predictions regarding phase transitions and phase diagrams at genetic equilibrium. The predicted phenomenology allows for natural physical interpretations, and finds satisfactory support in numerical simulations.

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Increased arylhydrocarbon receptor expression offers a potential therapeutic target for pancreatic cancer

et al. 2002

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The arylhydrocarbon receptor (AhR) was initially identified as a member of the adaptive metabolic and toxic response pathway to polycyclic aromatic hydrocarbons and to halogenated dibenzo-p-dioxins and dibenzofurans. In the present study, we sought to determine the functional significance of the AhR pathway in pancreatic carcinogenesis. AhR expression was analysed by Northern blotting. The exact site of AhR expression was analysed by in situ hybridization and immunohistochemistry. The effects of TCDD and four selective AhR agonists on pancreatic cancer cell lines were investigated by growth assays, apoptosis assays, and induction of the cyclin-dependent kinase inhibitor p21. There was strong AhR mRNA expression in 14 out of 15 pancreatic cancer samples, weak expression in chronic pancreatitis tissues, and faint expression in all normal pancreata. In pancreatic cancer tissues, AhR mRNA and protein expression were localized in the cytoplasm of pancreatic cancer cells. TCDD and the four AhR agonists inhibited pancreatic cancer cell growth in a dose-dependent manner, and decreased anchorage-independent cell growth. DAPI staining did not reveal nuclear fragmentation and CYP1A1 and was not induced by TCDD and AhR agonists. In contrast, TCDD and AhR agonists induced the expression of the cyclindependent kinase inhibitor p21. In conclusion, the relatively non-toxic AhR agonists caused growth inhibition in pancreatic cancer cells with high AhR expression levels via cell cycle arrest. In addition, almost all human pancreatic cancer tissues expressed this receptor at high levels, suggesting that these or related compounds may play a role in the therapy of pancreatic cancer in the future.

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The relationship between peptide plane rotation (PPR) and similar conformations

Cited by 3 publications

References 43 publications

Dihedral-Angle Information Entropy as a Gauge of Secondary Structure Propensity

Dihedral-Angle Information Entropy as a Gauge of Secondary Structure Propensity

A solvable model of the genesis of amino-acid sequences via coupled dynamics of folding and slow-genetic variation

Increased arylhydrocarbon receptor expression offers a potential therapeutic target for pancreatic cancer

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