A list of up to 100 peptide sequences, each up to 150 amino acids in length, may be submitted to the PeptideRanker webserver. PeptideRanker will predict the probability that each of these peptides will be bioactive. The list will then be returned to the user ranked by the predicted probability of bioactivity for each peptide. When the user is interpreting the results is important to note that the server predicts how likely the peptide is to be bioactive, and not how bioactive the peptide is likely to be. We have chosen as an example the Bovine milk protein beta-casein (UniProt accession number P02666). This protein, nor any of its peptides, was not included in our training or test datasets. To run this example click on "example" and then click the "submit" button. We have split the beta-casein protein sequence into nine random sections, including the beta-casomorphin 7 peptide (YPFPGPI). The results show the beta-casomorphin 7 peptide strongly predicted to be bioacitve (0.917487), while none of the surrounding peptide sections are predicted to be bioactive (all < 0.5). PeptideRanker was trained at a threshold of 0.5 i.e. any peptide predicted over a 0.5 threshold is labelled as bioactive. However, the user may decide to chose a higher threshold to reduce the number of false positives. From our testing (see paper) we would expect that choosing a threshold of 0.8 will reduce the false positive rate from 11% and 16% at 0.5 threshold to 2% and 6% at a 0.8 threshold for long and short peptides respectively. However, increasing the threshold to 0.8 from 0.5 also reduces the true positive rate so the user need to chose a threshold carefully based on their needs i.e. is it more important the reduce the number of false positives or capture all the true positives? Another factor that will reduce the number of true positives predicted by PeptideRanker is if the peptide has Cysteine content lower than that of most secreted proteins/peptides i.e. less than 4%. When we tested PeptideRanker we observed that the false negatives in our independent test set (bioactive peptides incorrectly predicted as non-bioactive) had an average Cysteine content of just 1.7%. PeptideRanker was trained and tested on amino acid sequences without any modifications. Peptide modifications such as the acetylation of the N-terminus, the amidation of the C-terminus, the cyclisation of the peptide or the inclusion of one or more D-amino acids may increase the bioactivity and the specificity of the peptide. The inclusion of D-amino acids may also increase protease resistance. We would suggest that after using PeptideRanker to discover bioactive peptides in silico that various peptide modifications would be experimented with in vitro to increase the bioactivity.