  Predicting protein 3D structure from amino acid sequence remains as a
challenge in the field of computational biology. If protein structure
homologues are not found, one has to construct 3D structural conformations from
the very beginning by the so-called ab initio approach, using some empirical
energy functions. A successful algorithm in this category, Rosetta, creates an
ensemble of decoy conformations by assembling selected best short fragments of
known protein structures and then recognizes the native state as the highly
populated one with a very low energy. Typically, an energy function is a
combination of a variety of terms characterizing different structural features,
say hydrophobic interactions, van der Waals force, hydrogen bonding, etc. It is
critical for an energy function to be capable to distinguish native-like
conformations from non-native ones and to drive most initial conformations
assembled from fragments to a native-like one in a conformation search process.
In this paper we propose a linear programming algorithm to optimize weighting
of a total of 14 energy terms used in Rosetta. We reverse the Monte Carlo
process of Rosetta to approach native-like conformations to a process
generating from the native state an ensemble of initial conformations most
relevant to the native state. Intuitively, an ideal weighting scheme would
result in a large "basin of attraction" of the native structure, which leads to
an objective function for the linear programming. We have examined the proposal
on several benchmark proteins, and the experimental results suggest that the
optimized weights enlarge the attraction basin of the native state and improve
the quality of the predicted native states as well. In addition, a comparison
of optimal weighting schema for proteins of different classes indicates that in
different protein classes energy terms may have different effects.
