Major histocompatibility complex class I binding detection software tools | Immune system data analysis
A key role in cell-mediated immunity is dedicated to the major histocompatibility complex (MHC) molecules that bind peptides for presentation on the cell surface. Several in silico methods capable of predicting peptide binding to MHC class I have been developed. The accuracy of these methods depends on the data available characterizing the binding specificity of the MHC molecules.
Allows prediction of peptide binding affinity. NetMHCpan is a simple pan-specific machine learning method. It improves predictive performance by characterizing the binding specificity of a given major histocompatibility complex (MHC) molecule and predicting peptide length profile.
Predicts peptide-major histocompatibility complex (MHC) class I binding affinity. NetMHC is based on artificial neural networks that allows insertions and deletions in the alignment. It is able to learn the length profile of different MHC molecules. The tool can quantify the reduction of the experimental effort required to identify potential epitopes. It can also be used to identify binding motifs in other peptide datasets characterized by a linear component.
Predicts CTL epitopes in protein sequences. The method integrates prediction of peptide MHC class I binding, proteasomal C terminal cleavage and TAP transport efficiency. The server allows for predictions of CTL epitopes restricted to 12 MHC class I supertype. MHC class I binding and proteasomal cleavage is performed using artificial neural networks. TAP transport efficiency is predictied using weight matrix.
Ranks all possible peptides from an input protein using the PSSM coefficients. The predictive power of the method was evaluated by running RANKPEP on proteins known to bear MHCI K(b)- and D(b)-restricted T-cell epitopes. Analysis of the results indicates that > 80% of these epitopes are among the top 2% of scoring peptides. In addition, the RANKPEP server also allows the user to enter additional profiles, making the server a powerful and versatile computational biology benchmark for the prediction of peptide-MHC binding.
Predicts binding of peptides to any known major histocompatibility complex (MHC) class I molecule. NetMHCcons is a server that allows the user in an automatic manner to obtain the most accurate predictions for any given MHC molecule. This method provides affinity predictions for any peptide of length 8–11 amino acids to any given MHC class I molecule of known protein sequence. It also provides a possibility for the user to choose MHC molecule in question from a list of alleles or alternatively upload the MHC protein sequence of interest.
Determines human leukocyte antigen (HLA) matching at epitope level in alloantibody responses. HLAMatchmaker utilizes an algorithm where each HLA antigen is considered as a string of amino acid configurations in antibody-accessible positions. This program can be used as a quantitative tool to define the degree of a mismatch such as mismatched eplets of triplets.
Predicts HLA (Human Leukocyte Antigen) class I restricted cytotoxic T lymphocytes (CTL) epitopes. Epitope Prediction which is based on logistic regression, is simple to implement. It is solved by finding a single global maximum. In contrast to almost all other work in this area, a single model was trained on epitopes from all HLA alleles and supertypes. Epitope Prediction is therefore able to leverage data across all HLA alleles and/or their supertypes, automatically learning what information should be shared and also how to combine allelespecific, supertype-specific, and global information in a principled way.