During prediction of each residue, the generated secondary structure is returned into the network by a feedback procedure. Evolutionary information for the sequence input of the network is included by applying a PAM 250 matrix scoring function between the residues in the slidingwindow shifted along the sequence and a secondary structure segment database. The segment database is subdivided in coil, helix, sheet and hairpin fragment groups. The prediction system performs an interactive data exchange between two neural network systems.
Predictions start from the N-terminal side.
After completion of the sequence,secondary structure data is transferred to the network system which itself is trained on sequences in reversed order. The reverse predictor continues with the reversed sequence and reversed feedback structure array at the C-terminal side. The completed structure array is added to a structure profile and transferred to the forward predictor and the prediction process continues and repeats until self-consistency is achieved. The secondary structure of the central residue in the sliding window provided by the highest scoring secondary structure segment in the fragment group database was added to the secondary structure feedback profile. The PDB coordinates of the fragments are translated into dssp secondary structure fragments.