Solvent accessibility is predicted by a neural network method rating at a
correlation coefficient (correlation between experimentally observed and
predicted relative solvent accessibility) of 0.54 cross-validated on a set of
238 globular proteins (Rost & Sander, Proteins, 1994, 20, 216-226;
evaluation of accuracy). The output of the neural network codes for 10 states
of relative accessibility. Expressed in units of the difference between
prediction by homology modelling (best method) and prediction at random
(worst method), PROFacc is some 26 percentage points superior to a comparable
neural network using three output states (buried, intermediate, exposed) and
using no information from multiple alignments.
in integral membrane proteins are predicted by a system of neural networks.
The shortcoming of the network system is that often too long helices are
predicted. These are cut by an empirical filter. The final prediction
(Rost et al., Protein Science, 1995, 4, 521-533; evaluation of accuracy)
has an expected per-residue accuracy of about 95%. The number of false
positives, i.e., transmembrane helices predicted in globular proteins, is
The neural network prediction of transmembrane helices
(PHDhtm) is refined by a dynamic programming-like algorithm. This method
resulted in correct predictions of all transmembrane helices for 89% of the
131 proteins used in a cross-validation test; more than 98% of the
transmembrane helices were correctly predicted. The output of this method
is used to predict topology, i.e., the orientation of the N-term with respect
to the membrane. The expected accuracy of the topology prediction is > 86%.
Prediction accuracy is higher than average for eukaryotic proteins and lower
than average for prokaryotes. PHDtopology is more accurate than all other
methods tested on identical data sets.
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