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considered. Not every folding is thermodynamically optimal (it should always have a low

folding energy, because the lower the free energy, the more stable the structure), especially

since there are several secondary structure forms (e.g. stem-, hairpin- and interior-loop).

Secondary structures can be predicted bioinformatically, but this is not easy. There are

various algorithms for this purpose, which are all based on dynamic programming meth­

ods, but nevertheless work differently. For example, the Nussinov algorithm first calcu­

lates the maximum number of base pairs and then uses this information to calculate the

secondary structure with the maximum base pairing. However, since RNA structures do

not always have the maximum possible base pairing, this method does not always give

useful results. A more optimal and faster solution for structure determination is provided

by algorithms based on energy minimization. The Zuker algorithm calculates the optimal

secondary structure with the minimum free energy, based on a thermodynamic model, e.g.

mFold server. On the other hand, the Sankoff algorithm simultaneously folds and aligns

two sequences using an energy model to minimize the free energy, e.g. LocARNA pro­

gram. A useful online web server for secondary structure prediction is ViennaRNA

Webservices (https://rna.tbi.univie.ac.at/). There are many more tools for RNA analysis

here. For additional information, see the book section or Kunz et al. (2015).

In the exercise example, RNAfold (also in ViennaRNA Webservices, also based on

energy minimization) should find a possible secondary structure fold with a minimum free

energy of − 360.20 kcal/mol.

20.2

2. Here it is important to see that the change in energy released is not automatically equal

to the sequence length, e.g. it is not double. For example, the sequence

ATGCTACGCGATGCATCGAGCGCAT has an energy of −3.5 kcal/mol and twice the

20.2  Magic RNA