Peptide binding specificity of major histocompatibility complex (MHC) class I resolved into an array of apparently independent sub-specificities. Quantitation by peptide libraries and improved prediction of binding

Stryhn, Anette; Pedersen, Lars Østergaard; Romme, Tina; Holm, Charlotte Bisgaard; Holm, Arne; Buus, Søren

doi:10.1016/0198-8859(96)84772-0

Cited by 8 publications

(18 citation statements)

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“…We have previously demonstrated that crude matrix‐driven quantitative predictions can be made with reasonable accuracy – at least for peptides with optimal primary anchors (27). We now extend this investigation to any peptide regardless of whether it has optimal primary anchors, or not.…”

Section: Resultsmentioning

confidence: 68%

“…A positional scanning combinatorial peptide library (PSCPL) approach (24, 25) has previously been used to generate an unbiased description of the specificity of MHC class I (26, 27). According to this strategy, peptides of a particular size are systematically distributed into a set of sub‐libraries, where one amino acid at a specified position is kept constant, while the remaining positions contain randomly selected amino acids (see Table 1).…”

Section: Resultsmentioning

confidence: 99%

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Quantitative predictions of peptide binding to MHC class I molecules using specificity matrices and anchor‐stratified calibrations

Lauemøller

Holm²,

Hildén

et al. 2001

Tissue Antigens

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Peptides are key immune targets. They are generated by fragmentation of antigenic proteins, selected by major histocompatibility complex (MHC) molecules and subsequently presented to T cells. One of the most selective requirements is that of peptide binding to MHC. Accurate descriptions and predictions of peptide-MHC interactions are therefore important. Quantitative matrices representing MHC class I specificity can be used to search any query protein for the presence of MHC binding peptides. Assuming that each peptide residue contributes to binding in an additive and sequence independent manner, such "crude" matrix-driven predictions can be expressed as a quantitative estimates of binding strength. Crude matrix-driven predictions are reasonably uniform (i.e. precise), however, there is a general tendency towards overestimating binding (i.e. being inaccurate). To evaluate and possibly improve predictions, we have measured the MHC class I binding of a large number of peptides. In an attempt to further improve predictions and to include sequence dependency, we subdivided the panel of peptides according to whether the peptides had zero, one or two primary anchor residues. This allowed us to define unique anchor-stratified calibrations, which led to predictions of improved precision and accuracy.

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Section: Resultsmentioning

confidence: 68%

Section: Resultsmentioning

confidence: 99%

Quantitative predictions of peptide binding to MHC class I molecules using specificity matrices and anchor‐stratified calibrations

Lauemøller

Holm²,

Hildén

et al. 2001

Tissue Antigens

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show abstract

“…We have recently used a complete positional scanning combinatorial library approach to describe MHC specificity in an unbiased and quantitative way [18]. For each amino acid in each position a relative binding value can be calculated indicating how well a given amino acid side chain is tolerated in that position.…”

Section: Discussionmentioning

confidence: 99%

Longer peptide can be accommodated in the MHC class I binding site by a protrusion mechanism

et al. 2000

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According to current consensus, CD8+ T cell responses are focused upon short peptide sequences (8–11 amino acids) presented by MHC class I molecules. This size restriction is thought to operate mostly at the level of peptide‐MHC class I interaction. Crystal structures have shown that the free N and C termini of a bound peptide interact through hydrogen bonding networks to conserved residues at either end of the class I binding site. Accordingly, it is thought that the termini are fixed and that only minor variations in peptide size are possible through a central bulging mechanism. We find that this consensus view is not always correct as some peptide‐MHC class I interaction will accept significant extensions. Furthermore, our results indicate that in some cases protrusion, rather than bulging, may be the mechanism of extension. Depending upon the particular peptide‐MHC combination in question, such extensions can occur at either the N or C terminus (but never both at the same time). Finally, we show that MHC and T cell in some cases can detect the identity of the extension, i.e. that extensions may be part of the specificity of the T cell immune response. We suggest that such extensions may play a physiological role.

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“…QMs for predicting peptide binding to MHC molecules have also been developed using binding affinity data obtained from positional scanning combinatorial peptide libraries (PSCPLs) [68,69]. In this approach, all possible peptides of a given length are represented by sets of sublibraries, and in each sublibrary, one amino acid is fixed and the remaining positions contain mixtures of all amino acids.…”

Section: Quantitative Additive Modelsmentioning

confidence: 99%

Prediction of MHC-Peptide Binding: A Systematic and Comprehensive Overview

Lafuente¹,

Reche

2009

CPD

133

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T cell immune responses are driven by the recognition of peptide antigens (T cell epitopes) that are bound to major histocompatibility complex (MHC) molecules. T cell epitope immunogenicity is thus contingent on several events, including appropriate and effective processing of the peptide from its protein source, stable peptide binding to the MHC molecule, and recognition of the MHC-bound peptide by the T cell receptor. Of these three hallmarks, MHC-peptide binding is the most selective event that determines T cell epitopes. Therefore, prediction of MHC-peptide binding constitutes the principal basis for anticipating potential T cell epitopes. The tremendous relevance of epitope identification in vaccine design and in the monitoring of T cell responses has spurred the development of many computational methods for predicting MHC-peptide binding that improve the efficiency and economics of T cell epitope identification. In this report, we will systematically examine the available methods for predicting MHC-peptide binding and discuss their most relevant advantages and drawbacks.

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Peptide binding specificity of major histocompatibility complex (MHC) class I resolved into an array of apparently independent sub-specificities. Quantitation by peptide libraries and improved prediction of binding

Cited by 8 publications

References 0 publications

Quantitative predictions of peptide binding to MHC class I molecules using specificity matrices and anchor‐stratified calibrations

Quantitative predictions of peptide binding to MHC class I molecules using specificity matrices and anchor‐stratified calibrations

Longer peptide can be accommodated in the MHC class I binding site by a protrusion mechanism

Prediction of MHC-Peptide Binding: A Systematic and Comprehensive Overview

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