We are analyzing https://link.springer.com/article/10.1007/s00251-004-0709-7.

Title:
Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles | Immunogenetics
Description:
We introduced previously an on-line resource, RANKPEP that uses position specific scoring matrices (PSSMs) or profiles for the prediction of peptide-MHC class I (MHCI) binding as a basis for CD8 T-cell epitope identification. Here, using PSSMs that are structurally consistent with the binding mode of MHC class II (MHCII) ligands, we have extended RANKPEP to prediction of peptide-MHCII binding and anticipation of CD4 T-cell epitopes. Currently, 88 and 50 different MHCI and MHCII molecules, respectively, can be targeted for peptide binding predictions in RANKPEP. Because appropriate processing of antigenic peptides must occur prior to major histocompatibility complex (MHC) binding, cleavage site prediction methods are important adjuncts for T-cell epitope discovery. Given that the C-terminus of most MHCI-restricted epitopes results from proteasomal cleavage, we have modeled the cleavage site from known MHCI-restricted epitopes using statistical language models. The RANKPEP server now determines whether the C-terminus of any predicted MHCI ligand may result from such proteasomal cleavage. Also implemented is a variability masking function. This feature focuses prediction on conserved rather than highly variable protein segments encoded by infectious genomes, thereby offering identification of invariant T-cell epitopes to thwart mutation as an immune evasion mechanism.
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Keywords {🔍}

google, scholar, pubmed, cas, article, mhc, class, binding, prediction, peptide, peptides, immunol, molecules, tcell, protein, biol, major, histocompatibility, motifs, epitopes, complex, acids, res, sequence, mol, reche, cleavage, nucleic, analysis, reinherz, bioinformatics, nature, processing, search, immunogenetics, rankpep, zhang, peptidemhc, epitope, language, structure, database, usa, rammensee, antigen, henikoff, privacy, cookies, content, research,

Topics {✒️}

vitro t-cell-mediated cytotoxicity month download article/chapter peptide binding predictions virtual matrix-based algorithms major histocompatibility complex position-specific gap penalties dana-farber cancer institute invariant t-cell epitopes candidate t-cell epitopes t-cell epitope discovery cd4 t-cell epitopes position-based sequence weights cd4 t-cell responses mhc class ii neural network-based prediction t-cell epitopes prediction allele-specific motifs revealed predict mhc-binding sequences structure-based algorithm t-cell receptor recognition structure-based prediction counter-propagation neural networks mhci-restricted epitopes results mhc superfamily structure variability masking function peptide-mhc class full article pdf flanking amino acids major proteolytic fragments covalently stabilized complex spoken language research peptide-mhc binding predicting peptide binding t-cell receptor privacy choices/manage cookies article immunogenetics aims mhc-binding peptides peptide-mhc complexes mhc-peptide complexes bell syst tech viral peptides presented mhc-binding motifs t-cell repertoire peptide-binding motifs peptide binding specificity mhci-restricted epitopes antigen peptide binding statistical language modeling hiv protein sequences t-cell recognition

Questions {❓}

Rosenfeld R (2000) Two decades of statistical language modeling: where do we go from here?

Schema {🗺️}

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         description:We introduced previously an on-line resource, RANKPEP that uses position specific scoring matrices (PSSMs) or profiles for the prediction of peptide-MHC class I (MHCI) binding as a basis for CD8 T-cell epitope identification. Here, using PSSMs that are structurally consistent with the binding mode of MHC class II (MHCII) ligands, we have extended RANKPEP to prediction of peptide-MHCII binding and anticipation of CD4 T-cell epitopes. Currently, 88 and 50 different MHCI and MHCII molecules, respectively, can be targeted for peptide binding predictions in RANKPEP. Because appropriate processing of antigenic peptides must occur prior to major histocompatibility complex (MHC) binding, cleavage site prediction methods are important adjuncts for T-cell epitope discovery. Given that the C-terminus of most MHCI-restricted epitopes results from proteasomal cleavage, we have modeled the cleavage site from known MHCI-restricted epitopes using statistical language models. The RANKPEP server now determines whether the C-terminus of any predicted MHCI ligand may result from such proteasomal cleavage. Also implemented is a variability masking function. This feature focuses prediction on conserved rather than highly variable protein segments encoded by infectious genomes, thereby offering identification of invariant T-cell epitopes to thwart mutation as an immune evasion mechanism.
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         Allergology
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