New methods to predict MHC-binding sequences within protein antigens

New methods to predict MHC-binding sequences within protein antigens luergen Hammer Roche M i l a n o Ricerche, Milan, Italy The identification and analysis of MHC-binding sequences within protein antigens, and ultimately the ability to predict them, is central to immunology. Recent advances have revealed increasingly complex MHC-binding motifs and allow prediction of sequences that bind to both classes of MHC molecules. The systematic characterization of binding motifs for all human MHC alleles is now possible and will facilitate the design of peptides for therapeutic intervention. Current Opinion in Immunology 1995, 7:263-269

Introduction The recognition of protein antigens is a major function of the immune system, in which MHC-encoded molecules play a central role. M H C molecules bind peptide fragments derived from protein antigens and display them on the surface of antigen-presenting cells, evoking effector responses upon recognition by the antigenspecific receptors of T lymphocytes. Consequently, a prerequisite for any protein antigen to be recognized by the immune system is its fragmentation by the intraceUular processing machinery into peptides capable of binding to a host M H C molecule. Despite the numerous peptides that can theoretically be generated, in practice only a small number are bound and presented by M H C molecules. Predicting these peptides would have many immunological applications. This review addresses the rapid progress in gaining knowledge of peptide binding to M H C molecules that has provided the basis for a more rational prediction of T-ceU epitopes.

MHC-peptide interaction as a means to predict T-cell epitopes The goal of T cell epitope prediction is to accurately identify peptide sequences within any protein that, in the context of a defined M H C molecule, will elicit desired T-cell responses. For peptide vaccination, these epitopes should ideally be dominant and promiscuous, so that they are recognized by most individuals within an outbred population. Ultimately, however, T-cell epitope prediction can only be as advanced as the molecular understanding of antigen uptake, processing and presentation. In the 1980s, for example, knowledge of antigen

processing and presentation was still rudimentary, and epitope prediction was based solely on the analysis of known T-ceU antigenic sites, mainly on the identification of amphipathic helices [1-4]. Such approaches were superseded, however, when evidence emerged for epitopes bound in extended conformation by both classes of M H C molecules [5--8,9°°]. Other approaches which analyzed the primary structure of T-cell epitopes, rather than the secondary structure, identified common patterns of amino acids in immunogenic peptides and utilized them for prediction of the T-cell epitopes [10,11]. All of these approaches, however, ignored the key requirements for M H C specificity. In the early 1990s, advances in the field of antigen presentation led to a general consensus that epitope prediction ought to be based on a molecular understanding of the events that determine antigen presentation, in other words, antigen processing and M H C binding. In fact, ifa set of rules underlies these events this could be sufficient for the precise prediction of T-cell epitopes. Inherent structural properties of protein antigens, however, presumably exclude simple rules for antigen processing, in that, for example, the structure of a given antigen will influence its susceptibility to proteolysis. Additional parameters that add to the complexity of antigen processing are the specificity of the proteolytic enzymes involved, the stability of the generated peptides and the selective transport ofpeptides into the endoplasmatic reticulum as part of the class I-dependent pathway [12]. In short, antigen processing is a multistep process that, at least for now, can neither be described by simple rules nor utilized for T-cell epitope prediction. MHC-peptide binding constitutes the second 'bottleneck' in the natural selection of T-cell epitopes, but appears to be less complex than antigen processing.

Abbreviations ANN--artificial neural network; CLIP---class II associatedinvariantchain peptide;CTL--cytotoxic T lymphocyte. © Current Biology Ltd ISSN 0952-7915

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Immunological techniques Consequently, the prospect of utilizing M H C binding as a means to predict T-cell epitopes spurred the rapid advance in the understanding of peptide binding to M H C molecules. The result was the identification of detailed binding motifs for both classes of M H C molecules and a basis for rational epitope prediction.

Increasingly detailed motifs improve prediction of M H C binding sequences Both M H C classes demonstrate an extensive peptide binding capacity. X-ray crystal structures of M H C and their bound peptides indicate that this capacity is due to hydrogen bonding between conserved M H C residues and the peptide main chain that forces different peptides into similar conformations [7,9°°,13]. Peptide mainchain interactions, however, are not the only mode of M H C binding. Some of the peptide side chains contact pockets in the M H C cleft and increase the overall binding affinity and specificity of the associated peptides. These pockets are usually shaped by clusters ofpolymorphic M H C residues, resulting in strong, allele-specific preferences for interaction with particular amino acid side chains. The sum of these preferences is defined as the binding motif of an M H C molecule. The validity of a binding motif can be evaluated by its ability to predict MHC-binding sequences.

Identification of MHC allele-specific binding motifs using large peptide repertoires A breakthrough for the analysis of the MHC's binding motifs was the characterization of large, MHC-selected peptide pools. This allowed the definition of general rules for peptide binding to M H C molecules. For class I molecules, Falk et al. [14] developed the pooled peptide sequencing technique which allowed the elution of endogenous class I peptide pools and their subsequent analysis by Edman sequencing. As the class I binding cleft is closed at both ends and the free amino and carboxyl groups of the associated peptides are involved in binding [7,13], class I-peptide interaction results in a strong bias for stable binding of short peptides in the range of eight to ten residues [14]. This length restriction leads to a natural alignment ofpeptides bound to class I, thus allowing the identification of position-dependent preferences for particular amino acid side chains (class I anchors by sequencing total mixtures of the eluted peptides. The peptide-binding groove of class II molecules is open at both ends [15], allowing class II bound peptides to extend out of the cleft. As a result, these peptides are longer than those bound by class I molecules and exhibit considerable length variations [16-19]. To define general rules for class II-peptide binding, my colleagues and I [20] circumvented the problem of variable peptide length. We characterized peptides bound to class II

that were isolated from large pools selected from M13 bacteriophage peptide display libraries comprising several million random nonamer peptides. Sequence analysis of the DNA encoding the displayed peptides led to the identification of positions where amino acids with similar side chains occurred with increased frequencies (class II anchors). Both pool sequencing and the screening of phage libraries have been used for the identification of several allele-specific class I and class II binding motifs, respectively [14,20-24]. These motifs generally consist of two to four anchor positions that are at fixed distances from one another. Most importantly, however, the M H C motifs identified as a result of study of M H C binding to large peptide pools indicated a general mode of peptide binding, thus providing the basis for prediction. The usefulness of the motifs for the prediction of M H C binding sequences has indeed been demonstrated in several experimental systems. The class I motifs facilitated the identification of naturally occurring epitopes derived from tumors or pathogens [25-30]: Pamer et al. [27] used the H-2Kd binding motif for the identification of a cytotoxic T lymphocyte (CTL) epitope from Listeria monocytogenes; Hill et al. [26] detected a CTL epitope from Plasmodiumfalciparum using the HLA-B53 binding motif and Sijts et al. [25] identified a murine leukemia virus (MuLV) encoded CTL epitope with the H-2Kb motif. For class II, the ability to predict M H C binding sequences has been demonstrated in vitro, in that the binding of a set of sequences selected from different proteins with three to four DR, l-anchor residues in frame was compared with the binding of another set of pepides with no anchors in frame [23]. Most peptides with anchors bound to DR1, whereas all peptides without anchors in frame failed to bind.

Additional approaches to defining MHC binding motifs Class I motifs were also identified by the alignment of individual peptide sequences obtained by Edman or tandem mass spectrometric sequencing of peptides eluted from class I molecules [31-35]. Although the restricted length of natural class I ligands facilitated the identification of motifs by sequence alignment, the variable length of class II ligands hindered the clear definition of class II motifs [17,18,36,37]. Similarly, pool sequencing approaches with class II eluted peptides also failed to reveal patterns as unequivocal as for class I ligands [38,39]. Yet another approach for studying both murine and human class II molecule-peptide interaction has been to characterize the effects of single residue substitutions in T-cell epitopes, revealing residues critical for the interaction with M H C [40-43]. Although several motifs were proposed on the basis of these studies, the results could not be easily generalized on the basis of only a few modified peptides. In retrospect, however, these early substitu-

Predicting MHC-binding sequences within protein antigens Hammer tion experiments were an important lead into the recent identification of more detailed class II motifs.

Expanded MHC binding motifs The discovery of motifs through pool sequencing and phage library screening would imply simple rules for peptide-MHC interaction. Several lines of evidence indicate, however, that these motifs are only the tip of the iceberg, and that the rules for peptide-MHC binding are actually complex. For example, the presence or absence of peptide side chains which can interfere with peptide binding may be just as important for peptide binding by class II M H C as the presence of anchor residues [23,44,45]. Binding studies of designer and natural peptides with several M H C class II molecules have revealed both position- and allele-specific properties of such inhibitory residues [23,46°]. They are found more frequently at anchor positions, as these are the major contact sites for the peptide side chains with the class II molecules. Apart from the class I anchor positions identified by pool sequencing, additional constraints were also found for class I-peptide interactions. Ruppert et al. [47 °] expanded the HLA-A2 motif with 'secondary' anchors, which were identified by correlating aligned HLA-A2 binding sequences with their binding affinity. Kubo et al. [48] refined the primary anchor positions of several class I motifs by amino acid substitution experiments. In both cases, the expanded motifs resulted in improved prediction of M H C binding sequences. Kast et al. [49] found that the pool sequencing derived motifs were present in only 27% of the high-affinity binders of the human papillomavirus type 16 E6 and E7 proteins, whereas the expanded motifs were present in 73% of the high affinity binders.

Quantitative MHC binding motifs The growing evidence that peptide side-chain effects (anchor, inhibitory or neutral effects) depend on the position within a particular peptide-frame rather than on neighbouring amino acids has led to the approximation that each amino acid in a peptide sequence contributes independently of the others to the affinity of the peptide [23,46°,50,51",52°]. The increasingly available multiplepeptide synthesis technology, together with new highflux in vitro binding assays, allowed this approximation to be tested [46°,47°,50,51°,52°]. The important consequence is the possibility to quantitatively predict the affinity of the MHC's binding sequences, once the effects of each amino acid at all positions have been determined. Parker et al. [51"] used this approach to determine a quantitative binding motif for the class I A2 molecule, and R,eay et al. [52 °] defined such a motif for the murine class II I-E k molecule. Hammer et al. [46 °] used the DRBI*0401 molecule to design a strategy for identifying quantitative motit~, that is now widely applicable to

many other human class II D R molecules. These studies confirm the working hypothesis of independent binding of side chains as a useful first approximation, and also that quantitative motifs can have a remarkable predictive power. Algorithms based on these quantitative motifs ranked peptides and T-cell epitopes binding with high affinity in the top 2-4% of all possible peptide-frames of given antigens. The immunodominant T-cell epitopes of the human myelin basic protein or the influenza hemagglutinin, for example, were placed at the top of all possible frames [46°]. Although independent side-chain effects seem to explain the binding of most peptides, a few exceptions were noted [46°,51°,53]. To characterize them, a more complicated, sequence specific, explanation may be needed. Additional complications could also be expected for the prediction of slightly longer class I binding peptides, because they loop out in the middle to maintain favorable contacts at both termini [54]. An example of the evolution of MHC-binding motifs from simple to expanded and quantitative motifs is illustrated in Fig. 1. Peptides known to bind to a given M H C molecule without any of the expected anchors have often generated controversy about the existence of M H C motifs. The class II associated invariant chain peptide (CLIP) 105-117 binds with high affinity to DRBI*0401 but has no major anchor positions in frame. Therefore, neither the simple nor the expanded moti~ would have predicted the CLIP peptide. An algorithm based on the quantitative DRBI*0401 motif, however, ranked the CLIP peptide (frame 107-115) at the top of all possible nonamers of the invariant chain.

The future of epitope prediction Processing motifs, TCR repertoires and immunodominance The appropriate MHC-binding motif within a protein is a necessary, but not sufficient, requirement for an immunodominant epitope, because antigen processing involves several selection steps. Even the actual presentation of antigenic fragments on the cell surface may not necessarily lead to T-cell stimulation because of possible 'holes' in the T-cell repertoires. Is epitope prediction, then, solely on the basis of M H C binding, really a valid approach? The answer is affirmative, provided that the majority of immunodominant peptides are high-aflqnity binders. Epitope prediction using quantitative M H C binding motifs and systematic binding studies [46°,49] has demonstrated an average of one to three high-affinity MHC-binding peptides every one hundred residues. Consequently, a 50 kDa protein may contain up to 14 high affinity binding peptides. Even if only one of them was processed, bound and recognized by T cells, fourteen peptides would still be a reasonable number for experimental evaluation. Several studies have indicated a major role of the peptide binding affinity in the selection of T-cell epitopes [46°,47°,51°,52°,53]. For example, R.uppert et al. [47"] and Parker et al. [51 °] demon-

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Simple motif: by screening of large peptide repertoires [22] Expanded motif: by high stringency screening of large peptide repertoires [23]

Quantitative motif: (simplified) by side chain scanning using a set of optimised, pl-anchored and Ala-based designer peptides [46]

pl p4 p6 p7 [Y~q- [X]- [X]- [MAVL]- [X]- [lS]- [LQMN]- [X]-[X]

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Amino acid residues ADEFGHIKLMNPQRSTVWY

pl -frame i

i

•~ SKMRMATPLLMQA No dominant anchor residues in frame pl -frame i

i

D, SKMRMATPLLMQA No dominant anchor residues in frame Peptide 4- ~'Clip 107-115 score-4--2"02~

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.

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© 1995 Current Opinion in Immunology

-5 -3 -1 0 2 4 Peptide score [ ] Score distribution in natural sequence • Peptide affinity range

Fig. 1. The evolution of the DRBI "0401 binding motif from simple to expanded and quantitative is illustrated using the CLIP peptide as an example. The simple and expanded DRBI "0401 motif would not predict CLIP, because CLIP has neither two dominant anchors (bold letters in motifs) nor one dominant and one secondary anchor in frame (plain letters in motifs). A prediction, however, which takes into account any number and combination of secondary anchors (e.g. underlined in the CLIP peptide) leads to a huge number of false positives and is therefore not useful. An algorithm based on the quantitative DRB1*0401 motif, here illustrated as a simplified raster diagram (see [46 °] for the exact side-chain values), assigns CLIP a score of = 4, ranking it at the top of all possible nonamers of the invariant chain (upper figure in right column). The 'score' is derived from the sum of the corresponding side chain values of the quantitative motif. As the 'score-distributionaffinity' diagram demonstrates (lower figure in right column), only a small percentage of the natural peptide-frames has scores in this range. The diagram indicates further that peptides with a score of = 4 are expected to bind with relatively high affinity to DRBI "0401. Indeed, CLIP binds with a affinity similar to that of influenza hemagglutinin T-cell epitope HA 301-31 9 [44].

strated with in vitro binding assays that most known CTL epitopes are amongst the high-affnity binders. Similarly, Chen et al. [53] concluded from studies on ovalbumin that dominance in their system can be explained almost completely by high affinity. Thus, prediction based on M H C seems reasonable for the majority of dominant epitopes that bind to M H C with high affinity. Nevertheless, future epitope prediction might be further finetuned by incorporating rules of antigen processing. Putative 'processing motifs', such as a proline residue close to the amino terminus of M H C class II bound peptides [38] or selection characteristics of peptide transporters [12], have already been identified.

Algorithms and artificial neural networks The quality of MHC-based epitope prediction may also depend on the data processing used to identify potential antigenic sites. Simple, mathematical algorithms are sufficient for epitope prediction using quantitative binding

motifs, as these are based solely on independent sidechain values [55]. The quality of prediction might, however, be improved by the incorporation of less obvious, sequence-specific parameters, but a systematic approach to identifying them would be impossible considering the large number of different peptides that would have to be tested. The solution could be the application of artificial neural networks (ANNs). ANNs can capture subtle relationships and generalize them from presented data (i.e. selectively retain features associated with defined criteria such as high binding affinity). The ability of ANNs to classify non-linearly separable data makes them a powerful tool for the analysis of biological data. Bisset and Fierz [56] used an ANN for the prediction of Dl:kl binding peptides, but with a small data set of 40 known binders, and derived a positive predictive value of only 55%. Brusic et al. [57 °] used a larger set of benchmark HLA-A2 binding peptides and a different ANN application to reach a positive predictive value of 78%. As for conventional algorithms, prediction with ANNs requires

Predicting MHC-bindingsequenceswithin protein antigensHammer 267 a sufficient amount of binding (and non-binding) data, but the ability of ANNs to 'learn' empirically make them particulary attractive. It remains to be seen if simple algorithms, neural networks or even combinations of both prove to be the best tools for epitope prediction.

is expected that within a short time, most human class I and class II motit~ will be defined quantitatively; software packages that combine these data and possibly enable prediction of promiscuous epitopes, will be developed to evaluate the impact of MHC-based epitope prediction on many facets of immunology.

Prediction of promiscuous epitopes The extensive polymorphism of the M H C is the main reason why different individuals in an outbred population recognize different epitopes in protein antigens. Consequently, a major objective of any predictive strategy is the selection of promiscuous T-cell epitopes (i.e. epitopes capable of binding to many M H C molecules) [58-60]. In theory, promiscuous peptides should either contain overlapping M H C binding motifs or, when only one binding frame is used, they should use anchors that are conserved among M H C molecules and should lack allele-specific contact sites that prevent binding to other M H C molecules. The latter is likely to occur for peptides binding to H L A - D R alleles, which account for approximately 90% of all human class II molecules expressed on the surface of antigen-presenting cells. Unlike the polymorphic G-chain, the or-chain is invariant in all D R alleles and forms half of the binding cleft [15], thus providing the structural basis for promiscuous peptide binding. More specifically, the D R 0t-chain together with a fairly conserved part of the G-chain, creates a deep pocket in the binding groove that interacts with a conserved anchor at position 1 (101) in class II binding peptides [9,22]. The pl anchor is essential for high affinity interaction with most D R alleles, and can even be sufficient in peptides that otherwise contain no inhibitory residues [40]. Substitution experiments and high stringency screenings of phage libraries defined a second conserved anchor at p2, and a third, but less conserved one, at p4 [23]. Altogether, they increase the chance of peptides binding in a promiscuous mode. The prediction of promiscuous T helper cell epitopes may, therefore, be rather simple, once quantititative motifs for most HLAD R molecules have been determined. The quantitative values of each allele could then be combined into a 'supermotif' that is able to predict epitopes effective in most individuals of an outbred population.

Acknowledgements I thank LC Harrison, MC Honeyman, L Adorini and F Sinigaglia for critical reading of the manuscript. I apologize to the many authors whose publications could not be cited due to format limits.

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Hammer J, Bono E, Gallazzi F, Belunis C, Nagy Z, Sinigaglia F: Precise prediction of MHC class II-peptide interaction based on peptide side chain scanning. J Exp Med 1994, 180:2353-2358 A systematic approach for the prediction of HLA-DR binding sequences is demonstrated based on the approximation of independent binding of peptide side chains. To prevent possible shifts within the peptide frame, side-chain scanning was performed on a set of short, position 1 (pl) an•

Predicting M H C - b i n d i n g sequences within protein antigens H a m m e r chored and Ala-based designer peptides where all amino acids had been individually substituted at each position from two to nine. The data were incorporated into an algorithm that calculates a score for each pl-frame of a given antigen. A large panel of binding data demonstrates a correlation between peptide score and peptide affinity, thus providing the basis for a quantitative prediction of class II binding sequences. Ruppert J, Grey HM, Sette A, Kubo RT, Sidney J, Cells E: Prominenl role of secondary anchor residues in peplide binding to HLA-A2.1 molecules. Cell 1993, 74:929-937. The importance of secondary anchors is demonstrated using a very large panel of synthetic peptides. An extended motif taking into account these secondaty anchors increased the predictability of A2.1-binding epitopes to a level of 70%, underscoring the practical usefulness of expanded MHC binding motifs. 47. •

48.

Kubo RT, Sette A, Grey HM, Appella E, Sakaguchi K, Zhu N-Z, Arnott D, Sherman N, Shabanowitz J, Michel H e t al.: Definition of specific motifs for four major HLA-A alleles. J Immunol 1994, 152:3913-3925.

49.

Kast WM, Brandt RMP, Sidney J, Drijfhout HM, Melief CJM, Sette A: Role of HLA-A lion of potential CTL epilopes in human 16 E6 and E7 proteins. ] Immunol 1994,

50.

Hammer J, Gatlazzi F, Bono E, Karr RW, Guenot J, Valsasnini P, Nagy ZA, Sinigaglia F: Peptide binding specificity of HLA-DR4 molecules: correlation with rheumatoid arthritis association. ] Exp Med 1995, in press.

J-W, Kubo RT, Grey motifs in identificapapillomavlrus type 152:3904-3912.

Parker KC, Bednarek MA, Coligan JE: Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains. J Immunol 1994, 152:163-175. The concept of independent binding of peptide side chains is introduced as a first approximation for the prediction of class I binding peptides. An algorithms based on independent side chain values ranked previously described HLA-A2 binding peptides in the top 2% of all possible nonamers for each source protein.

53.

Chen W, Khilko S, Fecondo J, Margulies DH, McCluskey J: Determinant selection of major histocompatibility complex class I-restricted antigenic peplides is explained by class I-peptide affinity and is strongly influenced by non-dominant anchor residues. J Exp Med 1994, 180:1471-1483

54.

Guo HC, Jardetzky TS, Garret TP, Lane WS, Strominger JL, Wiley DC: Different length peptides bind 1o HLA-Aw68 similarly at their ends but bulge out in the middle. Nature 1992, 360:364-366.

55.

Hammer J, Sinigaglia F: Techniques to identify the rules governing class II MHC-peplide interaction. In MHC: a practical approach. Edited by Butcher G, Fernandez N. Oxford: Oxford University Press, in press.

56.

Bisset LR, Fierz W: Using a neural network to identify potential HLA-DR1 binding sites within proteins. J Mol Recognit 1993, 6:41-48.

Brusic V, Rudy G, Harrison LC: Prediction of MHC binding peptides using artificial neural networks. In Complex systems: mechanism of adaptation. Edited by Stonier RJ, Yu XS. Amsterdam: lOS Press; 1994:253-260. The prediction of MHC-peptide binding using ANNs is demonstrated. The ANN was trained to classify peptides into those predicted to bind or not bind a given MHC molecule, either HLA-A2 or H-2Kb. High predictive values were reached for both molecules. 57. *

58.

Sinigaglia F, Guttinger M, Kilgus J, Doran DM, Matile H, Etlinger H, Trzeciak A, Gillesse D, Pink JRL: A malaria T-cell epitope recognized in association with most mouse and human MHC class II molecules. Nature 1988, 336:778-780.

59.

Panina-Bordignon P, Tan A, Termijtelen A, Demotz S, Corradin G, Lanzavecchia A: Universally immunogenic T cell epilopes: promiscuous binding 1o human MHC class II and promiscuous recognition by T cells. Eur J Immunol 1989, 19:2237-2242.

60.

Sinigaglia S, Hammer J: Motifs and supermotifs for MHC class II binding peptides. J Exp Nled 1995, 181:449-451.

51. •

Reay PA, Kantor RM, Davis MM: Use of global amino acid replacements to define the requirements for MHC binding and T cell recognition of moth cytochrome c (93-103). J Immunol 1994, 152:3946-3957. The concept of independent binding of peptides side chains is introduced for the identification of quantitative class II motifs. Although the basic peptide was not optimized, remarkable predictability was achieved. 52. •

Juergen Hammer, Roche Milano Ricerche, Via Olgettina 58, 120132 Milano, Italy.

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