[2][3][4] It is a server-side program, featuring a website serving as a front-end interface, which can predict a protein's secondary structure (beta sheets, alpha helixes and coils) from the primary sequence.
Secondary structure is the general three-dimensional form of local segments of biopolymers such as proteins and nucleic acids (DNA, RNA).
For proteins, a prediction consists of assigning regions of the amino acid sequence as highly probable alpha helixes, beta strands (often noted as extended conformations), or turns.
The idea of this method is to use the information of the evolutionarily related proteins to predict the secondary structure of a new amino acid sequence.
This matrix is processed by an artificial neural network,[3][6] which was constructed and trained to predict the secondary structure of the input sequence;[7] in short, it is a machine learning method.
The network has one hidden layer of 75 units and 3 output nodes (one for each secondary structure element: helix, sheet, coil).
The network has one hidden layer of 60 units and results in three output nodes (one for each secondary structure element: helix, sheet, coil).