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Expected degree for RNA secondary structure networks
 J Comp Chem
, 2015
"... Consider the network of all secondary structures of a given RNA sequence, where nodes are connected when the corresponding structures have base pair distance one. The expected degree of the network is the average number of neighbors, where average may be computed with respect to the either the unifo ..."
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Consider the network of all secondary structures of a given RNA sequence, where nodes are connected when the corresponding structures have base pair distance one. The expected degree of the network is the average number of neighbors, where average may be computed with respect to the either the uniform or Boltzmann probability. Here we describe the first algorithm, RNAexpNumNbors, that can compute the expected number of neighbors, or expected network degree, of an input sequence. For RNA sequences from the Rfam database, the expected degree is significantly less than the CMFE structure, defined to have minimum free energy over all structures consistent with the Rfam consensus structure. The expected degree of structural RNAs, such as purine riboswitches, paradoxically appears to be smaller than that of random RNA, yet the difference between the degree of the MFE structure and the expected degree is larger than that of random RNA. Expected degree does not seem to correlate with standard structural diversity measures of RNA, such as positional entropy, ensemble defect, etc. The program RNAexpNumNbors is written in C, runs in cubic time and quadratic space, and is publicly available at
4 A Remark on Deriving Precise Upper Bounds of the Number of the RNA Secondary Structures
, 2014
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RESEARCH ARTICLE RNA Thermodynamic Structural Entropy
"... Conformational entropy for atomiclevel, three dimensional biomolecules is known experimentally to play an important role in proteinligand discrimination, yet reliable computation of entropy remains a difficult problem. Here we describe the first two accurate and efficient algorithms to compute th ..."
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Conformational entropy for atomiclevel, three dimensional biomolecules is known experimentally to play an important role in proteinligand discrimination, yet reliable computation of entropy remains a difficult problem. Here we describe the first two accurate and efficient algorithms to compute the conformational entropy for RNA secondary structures, with respect to the Turner energy model, where free energy parameters are determined from UV absorption experiments. An algorithm to compute the derivational entropy for RNA secondary structures had previously been introduced, using stochastic context free grammars (SCFGs). However, the numerical value of derivational entropy depends heavily on the chosen context free grammar and on the training set used to estimate rule probabilities. Using data from the Rfam database, we determine that both of our thermodynamic methods, which agree in numerical value, are substantially faster than the SCFGmethod. Thermodynamic structural entropy is much smaller than derivational entropy, and the correlation between lengthnormalized thermodynamic entropy and derivational entropy is moderately weak to poor. In applications, we plot the structural entropy as a function of temperature for known thermoswitches, such as the repression of heat shock gene expression (ROSE) element, we determine that the correlation between hammerhead ribozyme cleavage activity and total free energy is improved by including an additional free energy term arising from conformational entropy, and we plot the structural entropy of windows of the HIV1 genome. Our software RNAentropy can compute structural entropy for any userspecified temperature, and supports both the Turner’99 and Turner’04 energy parameters. It follows that RNAentropy is stateoftheart software to compute RNA secondary structure conformational entropy. Source code is available at