Protein distance constraints predicted by neural networks and probability density functions

Ole Lund, Kenneth Frimand, Jan Gorodkin, Henrik Bohr, Jakob Bohr, Jan Hansen, Søren Brunak

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

We predict interatomic C-α 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 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 http://www.cbs.dtu.dk/services/CPHmodels/
Original languageEnglish
JournalProtein Engineering
Volume10
Issue number11
Pages (from-to)1242-1248
ISSN0269-2139
Publication statusPublished - 1997

Fingerprint Dive into the research topics of 'Protein distance constraints predicted by neural networks and probability density functions'. Together they form a unique fingerprint.

Cite this