  Fast and accurate protein structure prediction is one of the major challenges
in structural biology, biotechnology and molecular biomedicine. These fields
require 3D protein structures for rational design of proteins with improved or
novel properties. X-ray crystallography is the most common approach even with
its low success rate, but lately NMR based approaches have gained popularity.
The general approach involves a set of distance restraints used to guide a
structure prediction, but simple NMR triple-resonance experiments often provide
enough structural information to predict the structure of small proteins.
Previous protein folding simulations that have utilised experimental data have
weighted the experimental data and physical force field terms more or less
arbitrarily, and the method is thus not generally applicable to new proteins.
Furthermore a complete and near error-free assignment of chemical shifts
obtained by the NMR experiments is needed, due to the static, or deterministic,
assignment. In this thesis I present Chemshift, a module for handling chemical
shift assignments, implemented in the protein structure determination program
Phaistos. This module treats both the assignment of experimental data, as well
as the weighing compared to physical terms, in a probabilistic framework where
no data is discarded. Provided a partial assignment of NMR peaks, the module is
able to improve the assignment with the intension to utilise this in the
protein folding with little bias.
