Protein distance constraints predicted by neural networks and probability
O. Lund, K. Frimand, J. Gorodkin, H. Bohr, J. Bohr, J. Hansen
and S. Brunak.
Protein Engineering: 10:1241-1248, 1997.
Center for Biological Sequence Analysis, BioCenterum-DTU,
The Technical University of Denmark, DK-2800 Lyngby, Denmark
We predict interatomic Calpha distances by two independent data driven methods.
The first method uses statistically derived probability distributions of the
pairwise distance between two amino acids, whilst the latter method consists of
a neural network prediction approach equipped with windows taking the context
of the two residues into account. These two methods are used to predict whether
distances in independent test sets were above or below given thresholds. We
investigate which distance thresholds produce the most information-rich
constraints and, in turn, the optimal performance of the two methods. The
predictions are based on a data set derived using a new threshold which defines
when sequence similarity implies structural similarity. We show that distances
in proteins are predicted more accurately by neural networks than by
probability density functions. We show that the accuracy of the predictions can
be further increased by using sequence profiles. A threading method based on
the predicted distances is presented. A homepage with software, predictions and
data related to this paper is available at