Secondary Structure Predictor, is a classic three-states predictor
(alpha helix, beta strand, random coil) calibrated on soluble globular
This class contains only one prediction method, that is based on Neural
Network (NN) computation and on "profile" generation.
Secondary Structure Predictor (NN-based and Profile-based)
- Target: The real prediction target is represented by the secondary
structures (like alpha-helices and beta-strand) of globular proteins,
more extensively of soluble proteins. These structures and their
features are widespread and often relatable to all proteins that
fold and reside in a water environment. This tool should not
be used for the membrane proteins. Its results for this kind of
proteins are unreliable and should not be taken in account.
- Output: The NN output is represented by three predicted states
for each aminoacidic position, each one representing the probability
to be in an helix or in a strand or in a coiled state.
The output is graphically presented in three colors, according
to the legend at the bottom of the image, one for each predicted
The difference between the two biggest predicted probabilities is
also represented as a continuous line interpolating each discrete
In the table, the results for each each segment is labelled
accordingly to the predicted feature:
- Alpha - for alpha-helices
- Beta - for beta-strands
- Other - for random coils