Abstract for the paper:
Using Sequence Motifs for Enhanced Neural Network Prediction
of Protein Distance Constraints.
J. Gorodkin, O. Lund, C. A. Andersen, and S. Brunak.
ISMB99. In press.
Correlations between sequence separation (in residues) and distance (in
Angstrom) of any pair of amino acids in polypeptide chains are investigated.
For each sequence separation we define a distance threshold. For pairs of amino
acids where the distance between C-alpha atoms is smaller than the threshold, a
characteristic sequence (logo) motif, is found. The motifs change as the
sequence separation increases: for small separations they consist of one peak
located in between the two residues, then additional peaks at these residues
appear, and finally the center peak smears out for very large separations. We
also find correlations between the residues in the center of the motif. This
and other statistical analyses are used to design neural networks with enhanced
performance compared to earlier work. Importantly, the statistical analysis
explains why neural networks perform better than simple statistical data-driven
approaches such as pair probability density functions. The statistical results
also explain characteristics of the network performance for increasing sequence
separation. The improvement of the new network design is significant in the
sequence separation range 10--30 residues. Finally, we find that the
performance curve for increasing sequence separation is directly correlated to
the corresponding information content. A WWW server, distanceP, is available at
Abstract for the paper:
Protein distance constraints predicted by neural networks
and probability density functions.
O. Lund, K. Frimand, J. Gorodkin, H. Bohr, J. Bohr, J. Hansen, and S. Brunak.
Protein Engineering, Volume 10, Issue 11: November 1997. 1241-1248.
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