RNAdesign
The RNAdesign program solves the multitarget RNA sequence design problem. You
can give one or more structural targets for which a single compatible sequence
is designed.
PAPER
Christian Hoener zu Siederdissen, Stefan Hammer, Ingrid Abfalter, Ivo L. Hofacker, Christoph Flamm, Peter F. Stadler.
Computational Design of RNAs with Complex Energy Landscapes.
2013. Biopolymers. 99, no. 12. 99. 1124–36. http://dx.doi.org/10.1002/bip.22337.
Contact
choener@tbi.univie.ac.at
HOW TO USE RNAdesign
RNAdesign designs RNA sequences given one or more structural targets. The
program offers a variety of optimization functions that each can be used to
optimize candidate sequence towards a certain goal, say, minimal ensemble
defect or small energetic distance to another target structure.
RNAdesign input
Structural targets are given via stdin, preferably via an input file. Below is
a the small tristable from our paper, which you should then pipe to RNAdesign:
“echo tristable.dat  RNAdesign”
“cat tristable.dat:”
a tristable example target. (optional comment)
((((….))))….((((….))))……..
……..((((….((((….))))….))))
((((((((….))))((((….))))….))))
below follows a simple (and optional) sequence constraint.
CKNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNB
The input may contain many comments lines, starting with a hash “#” and at most
one sequence constraint line. All of these lines are optional, except of course
for the structural constraints.
Optimization functions
Depending on the actual design you are looking for, you’ll want to modify the
optimization function. Below, the different options available are detailed. By
giving a complex “–optfun”, many different design goals can be tried.
A good optimization goal is (as an example for three targets):
–optfun “eos(1)+eos(2)+eos(3)  3 * gibbs + 1 * ((eos(1)eos(2))^2 + (eos(1)eos(3))^2 + (eos(2)eos(3))^2)”
This way, the sequence produces closetomfe foldings with the targets (left)
and the targets are close together in terms of energy. (1 * ) scales the two
terms according to user choice.
binary, combining:


 / :: the four basic operations
^ :: (^) generalized power function
binary, apply function to many targets:
sum max min :: run function over set of targets: sum(eos,1,2) or sum(eos,all)
unary, apply to single target:
eos :: energy of a structure: eos(1)
ed :: ensemble defect of a structure: ed(3)
partc :: constrained partition function: partc(1).
You probably want to use partc in conjunction with eos, where eos is modified
by a small constant: “0.1 * eos(1) + partc(1)”. eos guides the optimizer to the
first viable sequence, after which the constrained partition function becomes
active.
nullary, constant for the current sequence:
Ged :: global, weighted ensemble defect: Ged
gibbs :: gibbs free energy of sequence
mfe :: minimum free energy of sequence
special:
logMN :: requires four parameters logMN(0.2,0.3,0.3,0.2) penalizes according to given mononucleotide distribution in order of ACGU