Figure 3

Schematic overview of the NN-align algorithm.The artificial neural network (initially with assigned random weights) is used to predict the binding affinity for a given peptide (right panel). The peptide (shown in light blue) is partitioned into overlapping 9mers, and the binding affinity is predicted encoding the 9mer binding core combined with information about the peptide flanking residues (PFR), the length of the PFR and the peptide length as described in the text. The binding affinity of the peptide is assigned from the highest scoring sub-peptide (shown in red). Next, the ANN weight configuration is updated using back-propagation to minimize the squared error between the predicted and measured binding affinities. This is repeated in a cycle for all peptides in the training data set for a given number of iterations.