Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles

Reche, Pedro A.; Glutting, John-Paul; Zhang, Hong; Reinherz, Ellis L.

doi:10.1007/s00251-004-0709-7

Cited by 351 publications

(282 citation statements)

References 81 publications

Supporting

Mentioning

276

Contrasting

Unclassified

Order By: Relevance

“…Possibly, BCG or M.tb could induce T cells specific for P8 in TB10.3/TB12.9, and not TB10.4. However, only TB10.4 was predicted by RANKPEP (http://bio.dfci.harvard.edu/RANK-PEP) [35] to have an MHC-II (I-Ad)-restricted epitope within P8 for the b and d haplotype-restricted CB6F1 mice, suggesting that the P8-specific CD4 1 T cells observed in our study recognized P8 from TB10.4.Both BCG and TB10.4/CAF01 vaccines were taken up by DC and macrophages, but TB10.4/CAF01 was targeted by DC to a higher degree than BCG in line with results from Korsholm et al[7] (Fig. 4).…”

mentioning

confidence: 99%

Difference in TB10.4 T‐cell epitope recognition following immunization with recombinant TB10.4, BCG or infection with Mycobacterium tuberculosis

et al. 2010

View full text Add to dashboard Cite

Most novel vaccines against infectious diseases are based on recombinant Ag; however, only few studies have compared Ag-specific immune responses induced by natural infection with that induced by the same Ag in a recombinant form. Here, we studied the epitope recognition pattern of the tuberculosis vaccine Ag, TB10.4, in a recombinant form, or when expressed by the pathogen Mycobacterium tuberculosis (M.tb), or by the current anti-tuberculosis vaccine, Mycobacterium bovis BCG. We showed that BCG and M.tb induced a similar CD4 1 T-cell specific TB10.4 epitope-pattern, which differed completely from that induced by recombinant TB10.4. This difference was not due to post-translational modifications of TB10.4 or because TB10.4 is secreted from BCG and M.tb as a complex with Rv0287. In addition, BCG and TB10.4/CAF01 were both taken up by DC and macrophages in vivo, and in vitro uptake experiments revealed that both TB10.4 and BCG were transported to Lamp 1 -compartments. BCG and TB10.4 however, were directed to different types of Lamp 1 -compartments in the same APC, which may lead to different epitope recognition patterns. In conclusion, we show that different vectors can induce completely different recognition of the same protein.Key words: Epitopes . Intracellular localization . Subdominant . T cells . Vaccine uptake IntroductionThe size, shape and nature of a synthetic recombinant vaccine and its target pathogen differ significantly. For instance, bacteria are typically in the range of 0.5-10 mm in diameter, which exceed the size of most viruses by 10 to 100-fold, and protein based adjuvanted vaccines are even smaller. In addition, compared with vaccines based on recombinant proteins and an adjuvant, pathogens are often taken up by different mechanisms by the cells of the immune system [1]. The different uptake mechanisms could lead to different intracellular processing of Ag, giving rise to different epitopes [1]. Furthermore, live pathogens express a wide range of specific lipids 1342and proteins that bind a variety of pattern-recognition receptors on phagocytes and induce signaling through these receptors, whereas recent evidence suggests subunit vaccines more specifically tend to target DC through activation of toll-like receptors [2]. These differences are likely to lead to different responses with regard to the priming of the early immune response [3]. For instance, the main host cell of the intracellular pathogen Mycobacterium tuberculosis (M.tb), the causative agent of tuberculosis in humans, is thought to be macrophages [4]; however, although mycobacteria are mainly taken up by macrophages, mycobacteria can infect a wide range of cells including neutrophils, epithelial cells and other cell types [5,6]. On the other hand, viral vaccine vectors have been shown to be ingested largely by immature DC [1], and soluble Ag formulated in cationic adjuvants such as CAF01 or IC31 are also believed to target DC [7,8]. Different types of APC have different mechanisms of Ag uptake, different pH levels in lysosomal co...

show abstract

mentioning

confidence: 99%

Difference in TB10.4 T‐cell epitope recognition following immunization with recombinant TB10.4, BCG or infection with Mycobacterium tuberculosis

et al. 2010

View full text Add to dashboard Cite

show abstract

“…Previously proposed learning approaches for protein-peptide binding prediction, address the binding prediction problem using traditional margin based binary classifiers: for each protein a classifier is trained to distinguish binding peptides from non-binding peptides [6,4,16] (for a review see [9]). Recently, we proposed PepDist: a novel approach for predicting binding affinity based on learning peptide-peptide distance functions 1 [28].…”

Section: Learning Peptide-peptide Distance Functionsmentioning

confidence: 99%

Identifying HLA supertypes by learning distance functions

Hertz

Yanover²

2007

Bioinformatics

View full text Add to dashboard Cite

The development of epitope-based vaccines crucially relies on the ability to classify Human Leukocyte Antigen (HLA) molecules into sets that have similar peptide binding specificities, termed supertypes. In their seminal work, Sette and Sidney [21] defined 9 HLA class I supertypes, and claimed that these provide an almost perfect coverage of the entire repertoire of HLA class I molecules.HLA alleles are highly polymorphic and polygenic and therefore experimentally classifying each of these molecules to supertypes is at present an impossible task. Recently, a number of computational methods have been proposed for this task. These methods are based on defining protein similarity measures, derived from analysis of binding peptides or from analysis of the proteins themselves [13,7]. In this paper we define both peptide derived and protein derived similarity measures, which are based on learning distance functions. The peptide driven measure is defined using a peptide-peptide distance function, which is learnt using information about known binding and non-binding peptides [28]. The protein similarity measure is defined using a protein-protein distance function, which is learnt using information about alleles previously classified into supertypes by [21]. We compare the classification obtained by these two complimentary methods to previously suggested classification methods. In general, our results are in excellent agreement with the classifications suggested by Sette and Sidney [21] and with those reported in [13].There are two important advantages of our proposed distancebased approach. First, it makes use of two different and important immunological sources of information -HLA alleles and peptides that are known to bind or not bind to these alleles. Second, since each of our distance measures is trained using a different source of information, their combination can provide a more confident classification of alleles into supertypes.

show abstract

“…9 These binding data are needed in order to identify the specific binding motifs and preferred peptide anchor residues for given alleles. They allow potential binding peptide sequences to be predicted by T-cell epitope prediction algorithms such as SYFPETHI, 10 NetMHC, 11 RANKPEP, 12 PeptideCheck 13 and BIMAS. 14 One failing of these programs is that they cannot address the presentation of peptides longer than the normal 8 to 10 mers.…”

Section: Introductionmentioning

confidence: 99%

The impact of human leukocyte antigen (HLA) micropolymorphism on ligand specificity within the HLA-B*41 allotypic family

Bade‐Doeding¹,

Theodossis²,

Gras³

et al. 2010

Haematologica

View full text Add to dashboard Cite

The online version of this article has a Supplementary Appendix. BackgroundPolymorphic differences between human leukocyte antigen (HLA) molecules affect the specificity and conformation of their bound peptides and lead to differential selection of the T-cell repertoire. Mismatching during allogeneic transplantation can, therefore, lead to immunological reactions. Design and MethodsWe investigated the structure-function relationships of six members of the HLA-B*41 allelic group that differ by six polymorphic amino acids, including positions 80, 95, 97 and 114 within the antigen-binding cleft. Peptide-binding motifs for B*41:01, *41:02, *41:03, *41:04, *41:05 and *41:06 were determined by sequencing self-peptides from recombinant B*41 molecules by electrospray ionization tandem mass spectrometry. The crystal structures of HLA-B*41:03 bound to a natural 16-mer self-ligand (AEMYGSVTEHPSPSPL) and HLA-B*41:04 bound to a natural 11-mer self-ligand (HEEAVSVDRVL) were solved. ResultsPeptide analysis revealed that all B*41 alleles have an identical anchor motif at peptide position 2 (glutamic acid), but differ in their choice of C-terminal pΩ anchor (proline, valine, leucine). Additionally, B*41:04 displayed a greater preference for long peptides (>10 residues) when compared to the other B*41 allomorphs, while the longest peptide to be eluted from the allelic group (a 16mer) was obtained from B*41:03. The crystal structures of HLA-B*41:03 and HLA-B*41:04 revealed that both alleles interact in a highly conserved manner with the terminal regions of their respective ligands, while micropolymorphism-induced changes in the steric and electrostatic properties of the antigen-binding cleft account for differences in peptide repertoire and auxiliary anchoring. ConclusionsDifferences in peptide repertoire, and peptide length specificity reflect the significant functional evolution of these closely related allotypes and signal their importance in allogeneic transplantation, especially B*41:03 and B*41:04, which accommodate longer peptides, creating structurally distinct peptide-HLA complexes.

show abstract

Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles

Cited by 351 publications

References 81 publications

Difference in TB10.4 T‐cell epitope recognition following immunization with recombinant TB10.4, BCG or infection with Mycobacterium tuberculosis

Difference in TB10.4 T‐cell epitope recognition following immunization with recombinant TB10.4, BCG or infection with Mycobacterium tuberculosis

Identifying HLA supertypes by learning distance functions

The impact of human leukocyte antigen (HLA) micropolymorphism on ligand specificity within the HLA-B*41 allotypic family

Contact Info

Product

Resources

About