  A cascading system of hierarchical, artificial neural networks (named
PRED-CLASS) is presented for the generalized classification of proteins into
four distinct classes-transmembrane, fibrous, globular, and mixed-from
information solely encoded in their amino acid sequences. The architecture of
the individual component networks is kept very simple, reducing the number of
free parameters (network synaptic weights) for faster training, improved
generalization, and the avoidance of data overfitting. Capturing information
from as few as 50 protein sequences spread among the four target classes (6
transmembrane, 10 fibrous, 13 globular, and 17 mixed), PRED-CLASS was able to
obtain 371 correct predictions out of a set of 387 proteins (success rate
approximately 96%) unambiguously assigned into one of the target classes. The
application of PRED-CLASS to several test sets and complete proteomes of
several organisms demonstrates that such a method could serve as a valuable
tool in the annotation of genomic open reading frames with no functional
assignment or as a preliminary step in fold recognition and ab initio structure
prediction methods. Detailed results obtained for various data sets and
completed genomes, along with a web sever running the PRED-CLASS algorithm, can
be accessed over the World Wide Web at http://o2.biol.uoa.gr/PRED-CLASS
