# Sfold 2.2

**:: DESCRIPTION**

Sfold predicts probable RNA secondary structures, assesses target accessibility, and provides tools for the rational design of RNA-targeting nucleic acids.Sfold is based on patent-pending algorithms developed by Ding and Lawrence (2001, 2002, 2003) for RNA folding, prediction of target accessibility, and rational design of RNA-targeting nucleic acids. The RNA folding algorithm generates a statistical sample of secondary structures from the Boltzmann ensemble of RNA secondary structures. From a statistical mechanics perspective, an RNA molecule can have a population of structures distributed according to a Boltzmann distribution, which gives the probability of a secondary structure I at equilibrium as (1/U)exp[-E(I)/RT], where E(I) is the free energy of the structure, R is the gas constant, T is the absolute temperature, and U is the partition function for all admissible secondary structures of the RNA sequence. The algorithm samples secondary structures exactly and rigorously according to the Boltzmann distribution, using recent Turner free energy rules.

**::DEVELOPER**

Wadsworth Bioinformatics Center

**:: SCREENSHOTS**

N/A

**:: REQUIREMENTS**

- Linux
- Perl

**:: DOWNLOAD**

**:: MORE INFORMATION**

Citation

Ding, Y., Chan, C.Y. and Lawrence, C.E. (2005)

RNA secondary structure prediction by centroids in a Boltzmann weighted ensemble.

RNA 11, 1157-1166.

Ding, Y. and Lawrence, C.E. (2003)

A statistical sampling algorithm for RNA secondary structure prediction.

Nucleic Acids Res. 31, 7280-7301.