A Physical Origin for Functional Domain Structure in Nucleic Acids as Evidenced by Cross-linking Entropy: I
Identifieur interne : 000132 ( Istex/Corpus ); précédent : 000131; suivant : 000133A Physical Origin for Functional Domain Structure in Nucleic Acids as Evidenced by Cross-linking Entropy: I
Auteurs : Wayne Dawson ; Kazuo Suzuki ; Kenji YamamotoSource :
- Journal of Theoretical Biology [ 0022-5193 ] ; 2001.
English descriptors
- Teeft :
- Academic press, Achter felsenfeld, Algorithm, Ambient temp, Average separation, Average structure, Base pair, Base pairing, Biol, Biopolymers, Central limit theorem, Chem, Chemical bond, Chemical bonds, Coil state, Conceptual model, Cornell university press, Crosslinking, Crosslinking entropy, Dawson, Dawson yamamoto, De5nition, Debe goddard, Delisi, Delisi crothers, Domain, Domain boundaries, Domain boundary, Domain size, Domain structure, Double strand, Dsdna, Dynamic programming algorithm, Eisenberg felsenfeld, Entire sequence, Entropic, Entropic contribution, Entropic contributions, Entropic penalties, Entropic penalty, Entropy, Entropy contribution, Entropy model, Eqns, Equilibrium separation, Equilibrium separation distance, Equilibrium thermodynamics, Escherichia coli, Exibility, Exible, Experimental conditions, Experimental evidence, Feller, Felsenfeld, Flory, Free energy, Functional domain, Functional domain structure, Functional domains, Gaussian, Gaussian polymer chain, Gaussian polymer chain model, Genome informatics series, Gibbs dimarzio, Gray bars, Grosberg, Grosberg khokhlov, Hairpin, Hairpin loop, Helix, Hemispheric balls, Hierarchal complexity, Hydrogen bonds, Hypothetical experiment, Ideal polymer chain, Imbl, Individual monomers, Internal loop, Internal loops, Intron, James guth, John wiley sons, Kcal, Khokhlov, Larger circle, Linear increase, Links, Logarithmic, Logarithmic contribution, Logarithmic term, Long list, Loop penalty, Loop regions, Maximum number, Mers, Minimum size, Molecular machinery, Molecular machines, Monomer, Monomer separation distance, Mrna, Multibranch, Multibranch loops, Natl acad, Nearest neighbor, Nnss, Nnss algorithms, Nnss strategies, Nucl, Nucleic, Nucleic acid, Nucleic acid sequences, Nucleic acids, Nucleotide, Other hand, Other words, Outer circle, Pairing, Partition function, Peptide, Persistence, Persistence length, Persistence lengths, Persistence ratio, Phantom network, Phys, Plischke bergersen, Pmbl, Poland scheraga, Polymer, Polymer chain, Polymer chains, Polymer physics, Polymer science, Private comm, Probability density function, Protein structure calculations, Reaction intermediates, Real polymer, Real polymers, Respective centers, Ribosomal subunit, Same chain, Same persistence length, Same polymer chain, Schematic example, Scheraga, Sears salinger, Secondary structure, Secondary structure calculations, Secondary structure plot, Secondary structure prediction, Secondary structures, Separation distance, Sequence length, Single strand, Small amount, Square displacement, Ssdna, Ssrna, Statistical mechanics, Structure prediction, Subdomain, Suboptimal structures, Temperature dependence, Temperature increases, Thermodynamic, Thermodynamic equilibrium, Thermodynamic probability, Thermodynamics, Thin lines, Tinoco, Total entropy, Total length, Total number, Traditional nnss approaches, Turner energy rules, Universal academy press, Volume dependence, Weight function, Weighted contribution, Williams tinoco, Yamamoto, Zuker, Zuker stiegler.
Abstract
Abstract: A global strategy for estimating the entropy of long sequences of RNA is proposed to help improve the predictive capacity of RNA secondary structure dynamic programming algorithm (DPA) free energy (FE) minimization methods. These DPA strategies only consider the effects that occur in the immediate (nearest neighbor) vicinity of a given base pair (bp) in a secondary structure plot. They are therefore defined as nearest-neighbor secondary structure (NNSS) strategies. The new approach utilizes the statistical properties of the Gaussian polymer chain model to introduce both local and global contributions to the entropy of a given secondary structure. These entropic contributions are primarily a function of the persistence length of the RNA. Limits on the domain size are strongly suggested by this model and these limits are a function of both the length and the percentage of bp enclosed within a given domain. The model generalizes the penalties found in the NNSS algorithms. The approach considers the importance of flexibility in the folding and stability of RNA by considering the role of the persistence length in a biopolymer structure. The theory also suggests that molecular machinery may also take advantage of this global entropic effect to bring about catalytic effects. The applications can also be extended to protein structure calculations with some additional considerations.
Url:
DOI: 10.1006/jtbi.2001.2436
Links to Exploration step
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<front><div type="abstract" xml:lang="en">Abstract: A global strategy for estimating the entropy of long sequences of RNA is proposed to help improve the predictive capacity of RNA secondary structure dynamic programming algorithm (DPA) free energy (FE) minimization methods. These DPA strategies only consider the effects that occur in the immediate (nearest neighbor) vicinity of a given base pair (bp) in a secondary structure plot. They are therefore defined as nearest-neighbor secondary structure (NNSS) strategies. The new approach utilizes the statistical properties of the Gaussian polymer chain model to introduce both local and global contributions to the entropy of a given secondary structure. These entropic contributions are primarily a function of the persistence length of the RNA. Limits on the domain size are strongly suggested by this model and these limits are a function of both the length and the percentage of bp enclosed within a given domain. The model generalizes the penalties found in the NNSS algorithms. The approach considers the importance of flexibility in the folding and stability of RNA by considering the role of the persistence length in a biopolymer structure. The theory also suggests that molecular machinery may also take advantage of this global entropic effect to bring about catalytic effects. The applications can also be extended to protein structure calculations with some additional considerations.</div>
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<json:item><name>KAZUO SUZUKI</name>
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<abstract>Abstract: A global strategy for estimating the entropy of long sequences of RNA is proposed to help improve the predictive capacity of RNA secondary structure dynamic programming algorithm (DPA) free energy (FE) minimization methods. These DPA strategies only consider the effects that occur in the immediate (nearest neighbor) vicinity of a given base pair (bp) in a secondary structure plot. They are therefore defined as nearest-neighbor secondary structure (NNSS) strategies. The new approach utilizes the statistical properties of the Gaussian polymer chain model to introduce both local and global contributions to the entropy of a given secondary structure. These entropic contributions are primarily a function of the persistence length of the RNA. Limits on the domain size are strongly suggested by this model and these limits are a function of both the length and the percentage of bp enclosed within a given domain. The model generalizes the penalties found in the NNSS algorithms. The approach considers the importance of flexibility in the folding and stability of RNA by considering the role of the persistence length in a biopolymer structure. The theory also suggests that molecular machinery may also take advantage of this global entropic effect to bring about catalytic effects. The applications can also be extended to protein structure calculations with some additional considerations.</abstract>
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