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Tools for Simulating and Analyzing RNA Folding Kinetics

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Part of the Lecture Notes in Computer Science book series (LNBI,volume 4453)

Abstract

It has recently been found that some RNA functions are determined by the actual folding kinetics and not just the RNA’s nucleotide sequence or its native structure. We present new computational tools for simulating and analyzing RNA folding kinetic metrics such as population kinetics, folding rates, and the folding of particular subsequences. Our method first builds an approximate representation (called a map) of the RNA’s folding energy landscape, and then uses specialized analysis techniques to extract folding kinetics from the map. We provide a new sampling strategy called Probabilistic Boltzmann Sampling (PBS) that enables us to approximate the folding landscape with much smaller maps, typically by several orders of magnitude. We also describe a new analysis technique, Map-based Monte Carlo (MMC) simulation, to stochastically extract folding pathways from the map. We demonstrate that our technique can be applied to large RNA (e.g., 200+ nucleotides), where representing the full landscape is infeasible, and that our tools provide results comparable to other simulation methods that work on complete energy landscapes. We present results showing that our approach computes the same relative functional rates as seen in experiments for the relative plasmid replication rates of ColE1 RNAII and its mutants, and for the relative gene expression rates of MS2 phage RNA and its mutants.

Keywords

  • Master Equation
  • Energy Landscape
  • Folding Process
  • Folding Pathway
  • Folding Rate

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supported in part by NSF Grants EIA-0103742, ACR-0081510, ACR-0113971, CCR-0113974, ACI-0326350, by the DOE, and by HP. Thomas supported in part by a Dept. of Education GAANN Fellowship, a NSF Graduate Research Fellowship, and a P.E.O. Scholarship. Tapia supported in part by a NIH Molecular Biophysics Training Grant (T32GM065088) and a Dept. of Education GAANN Fellowship.

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Tang, X., Thomas, S., Tapia, L., Amato, N.M. (2007). Tools for Simulating and Analyzing RNA Folding Kinetics. In: Speed, T., Huang, H. (eds) Research in Computational Molecular Biology. RECOMB 2007. Lecture Notes in Computer Science(), vol 4453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71681-5_19

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  • DOI: https://doi.org/10.1007/978-3-540-71681-5_19

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