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Helper T cell epitope-mapping reveals MHC-peptide binding affinities that correlate with T helper cell responses to pneumococcal surface protein A.

ABSTRACT: Understanding the requirements for protection against pneumococcal carriage and pneumonia will greatly benefit efforts in controlling these diseases. Several proteins and polysaccharide capsule have recently been implicated in the virulence of and protective immunity against Streptococcus pneumonia. Pneumococcal surface protein A (PspA) is highly conserved among S. pneumonia strains, inhibits complement activation, binds lactoferrin, elicits protective systemic immunity against pneumococcal infection, and is necessary for full pneumococcal virulence. Identification of PspA peptides that optimally bind human leukocyte antigen (HLA) would greatly contribute to global vaccine efforts, but this is hindered by the multitude of HLA polymorphisms. Here, we have used an experimental data set of 54 PspA peptides and in silico methods to predict peptide binding to HLA and murine major histocompatibility complex (MHC) class II. We also characterized spleen- and cervical lymph node (CLN)-derived helper T lymphocyte (HTL) cytokine responses to these peptides after S. pneumonia strain EF3030-challenge in mice. Individual, yet overlapping peptides, 15 amino acids in length revealed residues 199 to 246 of PspA (PspA(199-246)) consistently caused the greatest IFN-gamma, IL-2, IL-5 and proliferation as well as moderate IL-10 and IL-4 responses by ex vivo stimulated splenic and CLN CD4(+) T cells isolated from S. pneumonia strain EF3030-challeged F(1) (B6xBALB/c) mice. IEDB, RANKPEP, SVMHC, MHCPred, and SYFPEITHI in silico analysis tools revealed peptides in PspA(199-246) also interact with a broad range of HLA-DR, -DQ, and -DP allelles. These data suggest that predicted MHC class II-peptide binding affinities do not always correlate with T helper (Th) cytokine or proliferative responses to PspA peptides, but when used together with in vivo validation can be a useful tool to choose candidate pneumococcal HTL epitopes.

SUBMITTER: Singh R

PROVIDER: S-EPMC2828482 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

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Helper T cell epitope-mapping reveals MHC-peptide binding affinities that correlate with T helper cell responses to pneumococcal surface protein A.

Singh Rajesh R Singh Shailesh S Sharma Praveen K PK Singh Udai P UP Briles David E DE Hollingshead Susan K SK Lillard James W JW

PloS one 20100225 2

Understanding the requirements for protection against pneumococcal carriage and pneumonia will greatly benefit efforts in controlling these diseases. Several proteins and polysaccharide capsule have recently been implicated in the virulence of and protective immunity against Streptococcus pneumonia. Pneumococcal surface protein A (PspA) is highly conserved among S. pneumonia strains, inhibits complement activation, binds lactoferrin, elicits protective systemic immunity against pneumococcal infe ...[more]

PMID: 20195541

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Project description:The activation of T cells is triggered by the interactions of T cell receptors (TCRs) with their epitopes, which are peptides presented by major histocompatibility complex (MHC) on the surfaces of antigen presenting cells (APC). While each TCR can only recognize a specific subset from a large repertoire of peptide-MHC (pMHC) complexes, it is very often that peptides in this subset share little sequence similarity. This is known as the specificity and cross-reactivity of T cells, respectively. The binding affinities between different types of TCRs and pMHC are the major driving force to shape this specificity and cross-reactivity in T cell recognition. The binding affinities, furthermore, are determined by the sequence and structural properties at the interfaces between TCRs and pMHC. Fortunately, a wealth of data on binding and structures of TCR-pMHC interactions becomes publicly accessible in online resources, which offers us the opportunity to develop a random forest classifier for predicting the binding affinities between TCR and pMHC based on the structure of their complexes. Specifically, the structure and sequence of a given complex were projected onto a high-dimensional feature space as the input of the classifier, which was then trained by a large-scale benchmark dataset. Based on the cross-validation results, we found that our machine learning model can predict if the binding affinity of a given TCR-pMHC complex is stronger or weaker than a predefined threshold with an overall accuracy approximately around 75 %. The significance of our prediction was estimated by statistical analysis. Moreover, more than 60 % of binding affinities in the ATLAS database can be successfully classified into groups within the range of 2 kcal/mol. Additionally, we show that TCR-pMHC complexes with strong binding affinity prefer hydrophobic interactions between amino acids with large aromatic rings instead of electrostatic interactions. Our results therefore provide insights to design engineered TCRs which enhance the specificity for their targeted epitopes. Taken together, this method can serve as a useful addition to a suite of existing approaches which study binding between TCR and pMHC.

Dataset Information

Helper T cell epitope-mapping reveals MHC-peptide binding affinities that correlate with T helper cell responses to pneumococcal surface protein A.

Publications

Helper T cell epitope-mapping reveals MHC-peptide binding affinities that correlate with T helper cell responses to pneumococcal surface protein A.

Similar Datasets

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