|Title:||Efficient parameter estimation for RNA secondary structure prediction M. Andronescu, A. Condon, H. Hoos, D. Mathews and K. Murphy, ISMB 2007.|
Motivation: Accurate prediction of RNA secondary structure from the base sequence
is an unsolved computational challenge. The accuracy of
predictions made by free energy minimization
is limited by the
quality of the energy parameters in the underlying free energy model.
The most widely used model, the Turner99 model, has hundreds of
parameters, and so a robust parameter estimation scheme should
efficiently handle large data sets with thousands of
Moreover, the estimation scheme should
also be trained using available experimental free energy data in addition
to structural data.
Results: In this work, we present constraint generation (CG), the first computational approach to RNA free energy parameter estimation that can be efficiently trained on large sets of structural as well as thermodynamic data. Our constraint generation approach employs a novel iterative scheme, whereby the energy values are first computed as the solution to a constrained optimization problem. Then the newly-computed energy parameters are used to update the constraints on the optimization function, so as to better optimize the energy parameters in the next iteration. Using our method on biologically sound data, we obtain revised parameters for the Turner99 energy model. We show that by using our new parameters, we obtain significant improvements in prediction accuracy over current state-of-the-art methods.