Any predictive method that needs large numbers of parameters must be
cross-validated to ensure that the method does not do artificially
well on the examples used to derive the parameters. For cross
validation of the secondary structure and accessibility predictions,
we used the jack-knifed neural-network architectures described by
Rost &Sander (1993a) (Kindly provided by Dr. B. Rost.) Secondary
structure and accessibility for each query protein was predicted by an
architecture that did not include the query protein or any
homologue.
The filters and matching algorithm described here use only a few
geometric parameters all of which are independent of the protein
sequence. Accordingly, removal of query proteins and homologues from
the set used to derive the equations above makes a negligible
difference to the parameters.