  One algorithm to predict protein structure is the residual dipolar coupling
based residue assembly and filter tool (REDCRAFT). This algorithm exploits an
exponential reduction of the search space of all possible structures to find a
structure that best fits a set of experimental residual dipolar couplings.
However, the minimum amount of data required to successfully determine a
protein's structure using REDCRAFT has not been previously investigated. Here
we explore the effect of reducing the amount of data used to fold proteins. Our
goal is to reduce experimental data collection times while retaining the
accuracy levels previously achieved with larger amounts of data. We also
investigate incorporating a priori secondary structure information into
REDCRAFT to improve its structure prediction ability.
