| arb 1.0 |
Average Relative Binding (ARB) matrix methods that directly predict IC(50) values allowing combination of searches involving different peptide sizes and alleles into a single global prediction |
Bui, et al., 2005 |
MHC-I binding |
| bimas 1.0 |
log-transformed binding affinities relative to a reference peptide |
Parker, et al., 1994 |
MHC-I binding |
| comblibsidney 1.0 |
IC50 nM values for each mixture were standardized as a ratio to the geometric mean IC50 nM value of the entire set of 180 mixtures, and then normalized at each position as previously described [17,18] so that the value associated with the optimal value at each position corresponds to 1. For each position, an average (geometric) relative binding affinity (ARB) was calculated, and then the ratio of the ARB for the entire library to the ARB for each position was derived. We have denominated this ratio, which describes the factor by which the normalized geometric average binding affinity associated with all 20 residues at a specified position differs from that of the average affinity of the entire library, as the specificity factor (SF) |
Sidney, et al., 2008 |
MHC-I binding |
| epidemix 1.0 |
position-specific scoring matrices. The matrices are statistically computed based on the positive training set of SVMHC. Sequence weighting and pseudo-count correction are applied to obtain the frequencies used to generate the matrices. |
Feldhahn, et al., 2009 |
MHC-I binding |
| hammer 1.0 |
based on position-specific scoring matrices and predicts binding peptides for MHC class II |
Sturniolo, et al., 1999 |
MHC-II binding |
| netctlpan 1.1 |
The NetCTLpan prediction value is defined as a weighted sum of the three individual prediction values for MHC class I affinity, TAP transport efficiency, and C-terminal proteasomal cleavage |
Stranzl, et al., 2010 |
T-cell epitope |
| netmhc 3.0a |
artificial neural network predictions are given as actual IC50 values whereas PSSM predictions are given as a log-odds likelihood scores. (I believe that newer netMHC versions don't calculate PSSM predictions anymore) |
Lundegaard, et al., 2008 |
MHC-I binding |
| netmhcii 2.2 |
Here, we present a novel stabilization matrix alignment method, SMM-align, that allows for direct prediction of peptide:MHC binding affinities |
Nielsen, et al., 2007 |
MHC-II binding |
| netmhciipan 3.0,3.1 |
Here, we present the first pan-specific method capable of predicting peptide binding to any HLA class II molecule with a defined protein sequence |
Karosiene, et al., 2013 |
MHC-II binding |
| netmhcpan 2.4,2.8 |
Here, we present NetMHCpan-2.0, a method that generates quantitative predictions of the affinity of any peptide-MHC class I interaction |
Hoof, et al., 2009 |
MHC-I binding |
| pickpocket 1.1 |
For MHC molecules with known specificities, we established a library of pocket-residues and corresponding binding specificities. The binding specificity for a novel MHC molecule is calculated as the average of the specificities of MHC molecules in this library weighted by the similarity of their pocket-residues to the query. This PickPocket method is demonstrated to accurately predict MHC-peptide binding for a broad range of MHC alleles, including human and non-human species. |
Zhang, et al., 2009 |
MHC-I binding |
| smm 1.0 |
binding affinity |
Peters and Sette, 2005 |
MHC-I binding |
| smmpmbec 1.0 |
A novel amino acid similarity matrix has been derived for peptide:MHC binding interactions |
Kim, et al., 2009 |
MHC-I binding |
| svmhc 1.0 |
support vector machine classification to predict MHC-binding peptides. The method is trained on known MHC-binding peptides from the SYFPEITHI database |
Dönnes and Elofsson, 2002 |
MHC-I binding |
| syfpeithi 1.0 |
position-specific scoring matrices; the matrices are manually generated based on expert knowledge and the occurrence of amino acids in naturally processed MHC ligands from the SYFPEITHI database |
Rammensee, et al., 1999 |
T-cell epitope |
| tepitopepan 1.0 |
First, each HLA-DR binding pocket is represented by amino acid residues that have close contact with the corresponding peptide binding core residues. Then the pocket similarity between two HLA-DR molecules is calculated as the sequence similarity of the residues. Finally, for an uncharacterized HLA-DR molecule, the binding specificity of each pocket is computed as a weighted average in pocket binding specificities over HLA-DR molecules characterized by TEPITOPE. |
(Zhang, et al., 2012) |
MHC-II binding |
| unitope 1.0 |
support vector classification method, combines structural and sequence information in a machine-learning framework, The allele encoding uses pocket profiles derived from crystal structures of peptide:MHC complexes. The peptides are also encoded using physico-chemical properties |
Toussaint, et al., 2010, Toussaint et al. 2011 |
T-cell epitope |