Position Specific Scoring Matrices (PSSMs) Over the past two decades, a variety of tools have been developed to predict human MHC class I binders . The most well known methods such as NetMHCpan-4.1 and MHCflurry 2.0 employ Artificial Neural Networks (ANN) to predict peptide binders across multiple species and alleles. Alternatively, there exists simpler methods based on Position-Specific Scoring Matrices (PSSMs), where columns represent each position in the peptide sequence and rows indicate the influence of each of the 20 natural amino acids on the overall interaction with the MHC receptor at that position. The final pIC50 is obtained by the summing the contribution of each amino acid position.