When prior knowledge is available on the HLA alleles that are present in the sample, tools like NetMHCpan can be used to predict the binding affinity of a certain peptide to a specific MHC molecule. Features based on these predictions incorporate biological knowledge, in contrast to the previously mentioned features that are purely chemistry based. It is important to note the biases that can be introduced by imposing expected binding motifs based on prior knowledge. Moreover, because some HLA alleles are better characterized than others, the strength of the predictors varies depending on the HLA allele, potentially introducing a bias towards well-known HLA alleles. This conflicts with part of the goal of using untargeted mass spectrometry-based immunopeptidomics for the unbiased discovery of immunopeptides. Predicted binding affinity based features are supported by only two PSM rescoring tools, MHCquant and inSPIRE . inSPIRE uses MHC binding affinity predictions in combination with RT and fragment ion intensity predictions and showed that not only peptides with higher binding affinities were identified, but also those with overall better spectral features. This suggests that the features work in concert rather than one being prioritized over the others. In contrast, MHCquant only uses binding affinity as a feature and thus has an increased risk of introducing a bias, resulting in the identification of mainly already known binders. Other features, such as spectral similarity features and retention time features, provide a quality check. Without these features there is no filter for low-quality spectra, which can lead to an increased risk of introducing false positive peptide identifications primarily based on a couple of amino acids on specific anchor positions.