Table 3 MHC class I binding algorithm comparison
From: Best practices for bioinformatic characterization of neoantigens for clinical utility
Features/ software | Algorithm type used | Type of data used for training | Number of HLA alleles used for training | HLA alleles and peptide length that can be predicted | Output information |
|---|---|---|---|---|---|
Pickpocket (2009) | Position-specific weight matrices | In vitro quantitative binding data (> 150,000 data points) | More than 150 different MHC molecules | HLA-A, −B, −C, −E and -G alleles, also for non-human primates, mice, cattle and pigs. Peptides of 8–12 in length | Prediction values are given in nM IC50 values |
NetMHCcons (2012) | Integration of NetMHC 3.4, NetMHCpan 2.8 and PickPocket 1.1 | In vitro binding affinity data | NetMHC 3.4 (94 MHC class I alleles), NetMHCpan 2.8 (> 120 different MHC molecules), PickPocket 1.1 (94 different MHC alleles) | Can predict peptides to any MHC molecule of known sequence. Peptides of 8–15 amino acids in length | Prediction values are given in nM IC50 values and as %rank to a set of 200,000 random natural peptides |
NetMHC 4.0 (2016) | Artificial neural networks | In vitro binding affinity data | 81 different human MHC alleles (HLA-A, −B, −C, and -E) and 41 animal alleles | 81 different human MHC alleles (HLA-A, −B, −C, and -E) and 41 animal alleles. Any length but recommends 9 and discourages above 11 amino acids | Core position for binding within the peptide, interaction core sequence, affinity in nM, rank of prediction compared with 400,000 random natural peptides (strong binders %rank < 0.5), and so on |
NetMHCpan 4.0 (2017) | Artificial neural networks | Binding affinity (> 180,000 data points) and eluted ligand (MS) data | 172 human and other animal MHC molecules | Can predict peptides to any MHC molecule of known sequence | Core position for binding within peptide, interaction core sequence, affinity in nM, rank of the predicted affinity compared to a set of random natural peptides (strong binders %rank < 0.5), and so on |
MHCnuggets (2017) | Gated recurrent neural networks | IC50 values from immuno-fluorescent binding experiments for pMHC Class I pairs (137,654 data points) | 106 unique MHC alleles | Any MHC alleles, more reliable for alleles that are present in IEDB. Any peptide length is valid | IC50 binding affinity prediction |
MHCflurry (2018) | Allele-specific feed forward neural networks | Binding affinity and eluted ligand (MS) data (> 230,735 data points) | Across 130 alleles from IEDB combined with benchmark dataset from Kim et al. [209] | 112 alleles showed performance sufficient for their inclusion in predictor. Peptide lengths of 8–15 are supported | Affinity given in nM, percentile predictions across the models, and quantile of affinity prediction among large number of random peptides tested |
EDGE (2019) | Deep neural network | Peptide sequences from HLA immunoprecipitation followed by MS characterization | Not explicitly specified | 53 HLA alleles, 8–15-mer (inclusive) | Not explicitly specified |