Identifying cytotoxic T cell epitopes from genomic and proteomic information: A. Fomsgaard. The human MHC project

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1 1 IMMU P :34:30 Lg/disk / a200348/rig0o4-8 S.L. Lauemøller C. Kesmir S.L. Corbet Identifying cytotoxic T cell epitopes from genomic and proteomic information: A. Fomsgaard The human MHC project A. Holm M.H. Claesson S. Brunak S. Buus Key words: genome; proteome; antigen; MHC; vaccine Copyright c Munksgaard 2000 Reviews in Immunogenetics. ISSN Rev Immunogenetics 2001: 3: Printed in Denmark. All rights reserved Abstract: Complete genomes of many species including pathogenic microorganisms are rapidly becoming available and with them the encoded proteins, or proteomes. Proteomes are extremely diverse and constitute unique imprints of the originating organisms allowing positive identification and accurate discrimination, even at the peptide level. It is not surprising that peptides are key targets of the immune system. It follows that proteomes can be translated into immunogens once it is known how the immune system generates and handles peptides. Recent advances have identified many of the basic principles involved. The single most selective event is that of peptide binding to MHC, making it particularly important to establish accurate descriptions and predictions of peptide binding for the most common MHC variants. These predictions should be integrated with those of other steps involved in antigen processing, as these become available. The ability to translate the accumulating primary sequence databases in terms of immune recognition should enable scientists and clinicians to analyze any protein of interest for the presence of potentially immunogenic epitopes. The computational tools to scan entire proteomes should also be developed, as this would enable a rational approach to vaccine development and immunotherapy. Thus, candidate vaccine epitopes might be predicted from the various microbial genome projects, tumor vaccine candidates from mrna expression profiling of tumors ( transcriptomes ) and auto-antigens from the human genome. Genomics and the immune system The current revolution in genomics and proteomics is driven by technological advances. These have enabled a rapid and systematic acquisition of information on the gene and protein composition of living organisms. As the information accumulates, it eventually becomes complete. Thus, the first draft of the humane genome has been announced and a finalized version is expected shortly (in about 2003). At that time, a complete map of all human genes will be available. Similar genomic information has been obtained for many other organisms (virus, bacteria, yeast, animals, etc.) including most pathogenic microorganisms. These will provide catalogues of non- Authors affiliations: Sanne Lise Lauemøller 1, Can Kesmir 2, Sylvie L. Corbet 3, Anders Fomsgaard 3, Arne Holm 4, Mogens H. Claesson 5, Søren Brunak 2, Søren Buus 1 Institute of Medical Microbiology and Immunology, University of Copenhagen, Copenhagen, Denmark, 2 Center for Biological Sequence Analysis, The Technical University of Denmark, Lyngby, Denmark, 3 Institute of Virology, Statens Serum Institut, Copenhagen, Denmark, 4 Institute of Chemistry, Royal Veterinary University, Copenhagen, Denmark, 5 Institute of Medical Anatomy, University of Copenhagen, Copenhagen, Denmark Correspondence to: Søren Buus Institute of Medical Microbiology and Immunology University of Copenhagen Blegdamsvej 3 DK-2200 Copenhagen N Denmark Tel: π Fax: π S.Buus/immi.ku.dk

2 2 Reviews in Immunogenetics 2001: 3: human genes. To enable a detailed mapping of human diversity, a large number of single nucleotide polymorphisms (SNPs) are currently being identified (completion expected any day). Recently, mrna microarray technology has been developed to the point where genome-wide evaluations of the levels of gene expression (the transcriptome) can be obtained from any given cell. In addition, the technology to identify the encoded proteins (the proteome) and to evaluate their expression and post-translational modification(s) is being developed. Accompanying the genomics revolution, a computational approach to biology, bioinformatics, is developed to assimilate the enormous amount of data in a way that allows efficient dissemination and use. These developments will lead to fundamental changes in the way we think about biology and medicine. With many genome projects (almost) completed, focus is shifting from DNA sequencing to the considerably more complicated task of functional gene interpretation. Current efforts are directed at identifying genes, for example, that are involved in differentiation and activation, that are responsible for various traits and diseases; or that serve as markers for prognosis and treatment. Other efforts are being directed at developing methods to decipher genome information in terms of structure and function of the encoded proteins. These are very complicated tasks that have not yet been solved and probably will not be solved for years to come. In contrast, gene sequences can readily be translated into primary structure (i.e. sequence) information, making this the immediately available output of the many genome projects. It is somewhat puzzling that the immunological implications of mapping protein sequences have gone largely unnoticed. Recent immunological advances have demonstrated that the major specificity of the immune system is one of recognizing peptides in a way that resembles reading off the sequence of target proteins. Thus, primary protein structures can be viewed as constituting important immunological input, and mapping protein composition is like charting the immunological landscape. If one could predict how the immune system handles proteins then one should be able to translate proteomes to immunogens (i.e. forecast immune recognition) (1) (left hand and middle part of Fig. 1). Peptides as immune targets To fully appreciate the potential, one has to consider the following features of the immune system. The hallmark of the immune system Fig. 1. Genomes translated into proteomes, which computationally are digested and evaluated for MHC binding. The predicted peptides can be synthesized and tested for MHC binding and T cell stimulation. Also shown is how T cells recognize peptide-mhc complexes on antigen presenting cells.

3 Reviews in Immunogenetics 2001: 3: is its ability to recognize and distinguish between self and non-self. From a human perspective, the humane genome/proteome represents what immunologists would call self and the various nonhuman genomes/proteomes represent non-self or foreign. Any kind of defense depends crucially upon selecting targets that are appropriate: they should be accessible and easy to identity, difficult to conceal, change or remove and they should allow accurate discrimination between enemy and friend. Proteins fulfill these criteria and it is perhaps not surprising that they are the prime targets of the immune system. As carriers of structural and functional information, they are indispensable to all known forms of life. At the same time, their diversity is enormous making them excellent targets for recognition and discrimination. In fact, one does not have to resort to intact proteins let alone proteomes to be confronted with an impressive diversity. Using the 20 naturally occurring amino acids, one can generate some different 8-mer peptides and some different 16-mer peptides (roughly corresponding to MHC class I and II targets, respectively). Thus, even a relatively short peptide sequence may carry sufficient information about the organism, which encoded it, to allow accurate immune discrimination. The immune system has made an excellent choice when it selected peptides as prime targets. T cell recognition of peptides Progress made over the last two decades has conclusively demonstrated that one of the most important immune responses that of T cells indeed uses peptides as one part of a complicated target structure (reviewed in 2, 3). These cells are specific for peptides presented in the context of major histocompatibility gene molecules (MHC); a phenomenon known as MHC restriction (right hand part of Fig. 1). Through limited proteolytic fragmentation of all the proteins within the body, a pool of peptides is generated prior to antigen presentation (reviewed in 2, 4). This is known as antigen processing ; it ensures that all potential targets, including those hidden within microbial organisms and even those hidden within the tertiary structure of a protein, are exposed and subject to immune surveillance. The exact composition of this pool of peptides is determined by the specificities of the processing events involved. After antigen processing, the MHC samples some of the resulting peptides for antigen presentation. The net effect of this complicated system is that each individual generates a unique and highly diverse peptide imprint of the ongoing protein metabolism. To recognize such a diverse array of target structures, the T cells employ a receptor repertoire, which constitutes a more or less complete library of reactivities. This immense repertoire is educated in the context of self-mhc molecules to ignore peptides derived from self-proteins, and to recognize peptides derived from non-self proteins. T cells are frequently considered to command the pivotal specificity of the immune system as they discriminate between self and non-self. However, the specificity of MHC molecules is equally important as it controls antigen presentation. From the point of view of a microorganism, MHC may actually be the more important specificity to consider since the risk of being met by an appropriate T cell reactivity is high once the MHC has found an antigen to present (a 50% risk per epitope as estimated in 3). MHC polymorphism MHC has a direct impact on the specificity of the T cell immune system (5). During ontogeny, the MHC molecules available to the host are involved in shaping the T cell repertoire through selection processes, which ensure that the resulting T cells are restricted by the host MHC molecules (reviewed in 6). Later in life, only peptides perceived to be of foreign origin (e.g. pathogen-derived) and presented in the context of one of the host MHC molecules can be recognized by the host T cell immune system. Thus, the specificity of the MHC molecules is of considerable scientific and practical interest. The T cell immune system would be seriously crippled if MHC molecules were highly specific and only presented a few peptides. Rather, for the immune system to gain access to a reasonable representation of the proteins available to APCs, MHC molecules should be able to bind many different peptides. It has been estimated that each MHC allotype binds about 0.5% of the universe of peptides (3). Although a clear minority, in absolute numbers it is still a sizable amount of potential targets ( 10 8 epitopes the size of 8-mer peptides), and, teleologically, it appears to be a sufficient sampling capacity. Conversely, 99% of all peptides are ignored by any given MHC. If there were only one MHC, these non-binding peptides would constitute immunological blind spots amounting to a constant evolutionary pressure upon microorganisms to remove MHC presentable epitopes. The immune system has solved this potential problem through MHC polymorphism (in fact, the MHC is the most polymorphic gene system known, (for compilations, see 7, 8)). On a population basis, many alleles have been found per MHC encoding loci. On an individual basis, only one (homozygous) or two (heterozygous) of these alleles are expressed per locus. Importantly, the MHC is extremely polymorphic and the peptide binding specificity varies for the different polymorphic MHC molecules (9, 10). Thus, one allelic MHC product will recognize one part of the universe of peptides, whereas another allelic MHC product will recognize a different part of this universe (11) (Fig. 2). This leads to an

4 4 Reviews in Immunogenetics 2001: 3: Fig. 2. MHC polymorphism individualizes the immune system. Shown is how individuals with MHC x, y or z due are exposed to different parts of the universe of peptides. Also shown is how a microorganism represented by several isolates will be presented by individual z. Conserved epitopes (i.e. those shared among several isolates (indicated in pink)) are ideal vaccine candidates. individualized immune reactivity. No two individuals will have the same set of immunological blind spots and no microorganism could therefore evolve to circumvent the immune systems of the entire species. From this point of view, polymorphism is what allows the MHC to exercise some degree of specificity. From a more practical point of view, MHC polymorphism is a huge challenge to any MHC project as the one described here. co-stimulation. Irrespective of how one chooses to achieve co-stimulation, the ability to identify T cell epitopes for proper targeting of the immune system is of considerable value. This review focuses on ways to identify T cell epitopes (in particular those involved in MHC class I-restricted, cytotoxic T cell responses); what constitutes proper co-stimulation is beyond the scope of this review. T cell responses are the result of the series of events involved in peptide generation (antigen processing), peptide selection (antigen presentation) and peptide recognition (reviewed in 2 4). Prior to T cell recognition, protein antigens are processed and presented in a complicated series of intracellular events featuring individual specificities. A detailed picture of these processes is emerging. In general terms, antigen presentation involves peptide generation by antigen processing followed by peptide selection and presentation by MHC. For those peptides, which are to be recognized by MHC class I- restricted CD8π T cells, the efficiencies of peptide generation, selection and recognition have been estimated (3) (Fig. 3). Accordingly, only about 1/5th of all the peptide specificities that could possibly be generated from any given protein are actually generated by the antigen processing machinery. Only about 1/200th of the peptide specificities that are offered to any MHC haplotype are selected for subsequent presentation. Once these hurdles have been successfully passed, it has been estimated that as much as half of all the resulting peptide-mhc specificities are matched by the presence of an appropriate T cell receptor specificity within the T cell repertoire. Combining these figures would indicate that only one out of 2000 peptides represents an immunodominant T cell epitope. Obviously, the goal is to identify these relatively rare peptides with high accuracy, that is, with both high sensitivity (i.e. avoiding false negatives) and high specificity (i.e. avoiding false positives). A priori, identifying T cell epitopes is not a simple task. The immune system selects T cell epitopes in the course of the interaction between two large and complex repertoires: on one hand, all peptide-mhc complexes, which can be generated from a given pro- Identifying T cell epitopes Immunogenicity denotes the ability of an antigen to induce a specific immune response. The physiological requirements for the induction of a specific immune response are complex, involving both the adaptive and the innate arms of the immune system. These supply not only the specificity environment, including the unique antigen(s) and the cells carrying the appropriate specific receptors, but also the co-stimulatory environment, including more general factors such as cytokines, adhesion and signaling molecules. Practical applications of immunogenicity, such as vaccines, should be formulated so that they satisfy both the requirements for specificity and Fig. 3. About 20% of the possible peptides, which can be generated from a given protein, survives antigen processing; and about 0.5% of those will bind to a given MHC molecule; and about 50% of those will be recognized by T cells. Thus, only one out of 2000 peptides will end up being immunodominant (3).

5 Reviews in Immunogenetics 2001: 3: tein (or a proteome); on the other, the entire T cell repertoire. It is difficult to envision any single rule that would predict the outcome of the interaction between these two extremely diverse repertoires. A more meaningful strategy might therefore be to obtain detailed descriptions and predictions for each of the events involved and then link these predictions in an attempt to reproduce the biology of the immune system. There are several advantages to such a strategy. It would reduce the overall problem to several smaller and more manageable problems. Each could be addressed in isolation, allowing quantitative analytical methods to be developed and optimized for each of these events. In terms of defining immune specificity, the relative contribution of peptide generation, selection and recognition differs widely as described above. This suggests some important strategic choices in the work ahead. The first observation is that about 50% of all peptide-mhc complexes are matched by a corresponding T cell receptor specificity. This supports the contention that the T cell repertoire is more or less complete (i.e. that it contains T cell specificities corresponding to most foreign peptides presented in the context of self-mhc). It is fortunate that this, the most complicated of all the events involved, also is the least selective and therefore the one best ignored, at least for the time being. The problem of predicting T cell epitopes can then be reduced to one of predicting how protein sequence information is being handled by the antigen-presenting part of the immune system. The second observation is that less than 1% of all peptides binds to any given MHC class I molecule with sufficient affinity to be presented. This makes peptide binding to MHC the single most selective event involved in antigen handling and the most important to predict. Defining and predicting peptide binding to MHC is therefore a natural starting point (and the reason for the subtitle: The human MHC project ). It is a very large task, which is multiplied many times over by the polymorphism of the MHC. Eventually, the definition and prediction of all selective events involved in antigen presentation such as those of peptide generation by proteasome digestion, and peptide translocation by the TAP transporter, should be included. Describing and predicting the specificity of MHC class I Specificity through motif recognition The specificity of many MHC class I molecules have been described biochemically (12 14) and some have even been explained structurally (reviewed in 15) (Fig. 4). As expected from a specificity that should allow efficient sampling of the intracellular protein met- Fig. 4. MHC class I with peptide. The MHC is given as a ribbon representation with strands in yellow and alpha helices in red. The peptide is given as a balls and sticks representation in gray. abolism, the peptide binding specificity of MHC is quite broad. This broad specificity is obtained through the recognition of peptide motifs ; a recognition mode that requires the presence and proper spacing of particular amino acids in certain anchor positions (12, 16, 17). Those residues found most frequently are known as primary anchor residues. For MHC class I, there are usually 2 3 primary anchor residues located at primary anchor positions: the C-terminal, near the N-terminal (usually at P2), and sometimes in the middle of the peptide. The primary anchor positions are characterized by the dominant occurrence of the primary anchor residue; very few other residues are found, and these mostly represent conservative substitutions of the primary anchor residue. Scattered throughout the peptide sequence are the less dominating residues, the secondary anchor residues and the disfavored residues, which modulate the binding made possible by the presence of one or more of the primary anchors (13, 14). To some extent, the presence of secondary anchors may compensate for the lack of one of the primary anchors. Specificity-wise, the disfavored residues are particularly important since as much as 40% of the peptide residues are disfavored, making this the single most important mode of modulating MHC specificity (14) (e.g. Fig. 5 below). X-ray crystallography has identified a unique

6 6 Reviews in Immunogenetics 2001: 3: peptide-binding site at the outer polymorphic domains of the MHC (9, 10, 18 20). The majority of the peptide-mhc bonds involve peptide main chain (or backbone) atoms, including the termini for MHC class I (20 22). Since backbone atoms are common to all peptides, this explains how one MHC haplotype can perform high affinity (K D 10 ª8 9 M) binding of a large and diverse repertoire of peptides. Only the minority of the binding energy involves peptide side chain atoms. These interactions, however, are believed to explain the primary and secondary anchor specificity of the MHC (22). Structural explanations for disfavored amino acids have also been found. In one example, two closely appositioned amino acids residues are competing for space within the peptide-binding groove and therefore mutually exclusive. Obviously, identifying, describing and predicting such features of the peptide-binding specificity is not an easy task. Additional complications arise from the diverse structural solutions of how peptides can bind to MHC (20, 23 25). Methods to describe motifs and peptide binding Two complementary experimental approaches to determine MHC specificity are currently in use; each has distinct advances and disadvantages (Box 1). One approach investigates what the MHC has already bound in vivo (12). Natural MHC molecules are purified from cell lines or tissues and exposed to acid conditions to elute off the bound peptides. The eluted peptides represent the sum of all the events occurring during antigen processing and presentation. The common features of these peptides can subsequently be identified by sequencing ( pool sequencing ). This yields crude, semiquantitative descriptions of the specificity in the form of a simple motif. This is clearly dominated by the specificity of the purified MHC (albeit this is not the only specificity involved in pool sequencing, vide infra). It primarily describes the identity and position of the primary anchor residues and to some extent on the secondary anchor residues. Note, however, that pool sequencing does not give information on the identity and position of disfavored residues. Simple motifs are usually reported as rough, non-quantitative, outlines, which state the identity and position of primary and secondary anchor residues. The group of Hans-Georg Rammensee lists such motif information, providing the information on the internet ( An attempt has also been made to normalize the information obtained from pool sequencing, and reporting the information in the form of complete quantitative matrixes representing the effect of every amino acid in every position (26). One very important aspect of the in vivo approach is that it has made it possible to identify natural MHC ligands. In this variation of the in vivo approach, the eluted peptides are further purified, and any peptide isolated in sufficient amount is sequenced (27). Natural MHC ligands are examples of peptides that have successfully passed all the hurdles of antigen processing and presentation. If the originating protein can be identified and this has been possible in many cases then these peptides are examples of successful antigen processing and presentation. As such, they are unique examples of the workings of the immune system under physiological conditions, which otherwise would be very difficult to describe. Both the development and validation of these predictions should benefit greatly from the availability of natural MHC ligands. Any future prediction methods of proteasome degradation, TAP translocation, MHC binding, etc., should be able to identify these peptides. Any proper linking of these predictions into a final overall prediction of antigen processing and presentation should also meet this critical test. The other approach investigates what the MHC will bind in vitro (28 30). In general, MHC molecules are purified from appropriate cell lines (or generated recombinantly) and incubated with synthetic peptide. The resulting peptide-mhc complex formation is measured in a biochemical assay (e.g. by a radioimmuno assay) (Box 2). A major advantage of such an in vitro approach is that binding can be Box 1. Box 2.

7 Reviews in Immunogenetics 2001: 3: Fig. 5. Construction of a positional scanning combinatorial peptide library (PSCPL). Fig. 6. A complete matrix representing the specificity of ninemer peptide binding of HLA-A*0204. Pink, green and blue indicates residues which contributes positively, neutral or negatively, respectively, to binding. is very resource intensive, and it is perhaps not surprising that there is a tendency for some amino acids in some positions to be poorly represented. In an attempt to solve this problem, we and others have introduced a positional scanning combinatorial peptide library (PSCPL) approach in the analysis of MHC class I specificity (14, 32). In this approach, all possible peptides of a given size are represented by a systematic set of sub-libraries. In each sub-library, one amino acid in one position is kept constant (single letter amino acid code) whereas the remaining positions contain mixtures of amino acids (denoted x) (Fig. 5). This approach exploits the fact that MHC class I bound peptides are in register, i.e. different peptides are read in the same frame. Consequently, the constant amino acid residue of the PSCPL approach is read in one and the same frame. The PSCPL approach yields a complete representation of the MHC class I specificity, including primary anchors, secondary anchors and disfavored residues. Reassuringly, when PSCPL driven analysis has been applied to MHC class I molecules that have already been characterized by pool sequencing, the identity of primary and secondary anchors has been confirmed. The PSCPL approach has several important theoretical and practical advances. All amino acids are well represented in all positions. All peptides are potentially tested, i.e. there is no bias towards any particular sequence. The approach is universal (i.e. it applies to all MHC class I molecules) and re-useable (the same PSCPL can be used repeatedly on different MHC class I molecules). It significantly reduces the cost, experimentation and data handling that are associated with previous technologies. A detailed mapping of all human MHC specificities can therefore easily be envisioned. The resulting description of MHC specificity can be represented as a quantitative matrix, which for every peptide position gives the likelihood of finding each of the 20 naturally occurring amino acids (Fig. 6). Predicting peptide binding to MHC accurately quantitated and that MHC specificity can be addressed in isolation, very pointedly and under highly controlled conditions. Any substance, including any peptide of choice, can be tested for its effects upon MHC binding. Therefore, very detailed quantitative studies involving any peptide specificity can be done. This kind of analysis has allowed the identification of primary, secondary as well as disfavored residues; information, which is needed for the definition of the extended motifs (13, 14, 31). A large compilation of extended motifs as determined by in vitro MHC binding is now available on the internet ( bind/). A major disadvantage of this approach is that large panels of peptides must be examined to describe the extended motifs. This Several different approaches to predicting peptide binding to MHC class I molecules has been proposed (Box 3). The most straightfor- Box 3.

8 8 Reviews in Immunogenetics 2001: 3: ward way to identify potential T cell epitopes is to search the protein in question for the presence of MHC binding motifs. Simple motifs the identity and proper spacing of the primary anchor residues are by their very nature only rough outlines of the binding specificity. A prediction method based on simple motif searches only identifies about 1 out of 4 binders (33), i.e. it is only moderately sensitive. In contrast, the absence of a motif speaks strongly against a given peptide being a binder, i.e. motif-based predictions are very specific. Extended motifs, which include the most important primary, secondary and disfavored residues, represent a more detailed analysis of the binding specificity. A prediction method based on extended motif searches identifies about 3 out of 4 binders (33), i.e. it has a much better sensitivity than a simple motif search. However, the improved sensitivity comes at a price since the proportion of false positives goes up. This means that somebody who uses extended motif searches, as opposed to simple motif searches, will have to sift through many more peptides (8%, as opposed to 1%, of all possible peptides), but eventually he or she will get a more complete representation of all the binders (3 out of 4, as opposed to 1 out of 4, binders will be identified). The most elaborate descriptions of peptide binding consist of statistical matrices representing the frequency of each amino acid in each position (Fig. 6). Many of these matrices are crude and incomplete, even including values that have been assigned (13, 31, 34). They have typically been obtained by the analysis of large series of analog peptides. Using these matrices one can derive at semi-quantitative predictions allowing scoring and sorting of potential binders ( and syfpeithi.bmi-heidelberg.com). Complete quantitative matrices can be obtained using such library approaches as the PSCPL described above (14, 32). Predictions based upon PSCPL-generated matrices are significantly better than those based upon analog-generated matrices (14). The primary assumption behind any matrix-driven predictions is that each amino acid in each position contributes a certain binding energy independent of the neighboring residues (31). Binding of a given peptide can then be predicted by combining the contributions of the different residues. In the case of the PSCPL library-derived matrixes, multiplying the relative binding factors of the different residues of a given peptide indicates to what extent the peptide binds better or worse than a completely random library (14). Since the binding of the random library can be determined experimentally, one can predict the affinity of the peptide in question. In other words, in this case the output is quantitative. The relative success of matrix driven predictions would support sequence independence as a reasonable first approximation. Crystal structures of peptide-mhc complexes, however, have clearly identified examples of sequence specific long-range effects, which violates this assumption (35). One should therefore be able to improve predictions if one could incorporate such sequence specific effects. Unfortunately, it is difficult to envision any simple algorithm, which could include these non-linear relationships. For example, one could contemplate making sub-sequence specific PSCPL libraries. For HLA-A2 there is a strong C-terminal preference for the primary anchor residue V, the conservative substitutions of A, I and L are less preferred, whereas F, M and T are tolerated. One could address whether the recognition of the first 8 positions depends upon the identity of the 9th residue by generating PSCPL libraries scanning through the first 8 positions keeping the C-terminal locked at each of the above preferred or tolerated amino acids. This would involve (9 1) sub-libraries for each of the locked C-terminals, or sublibraries to address the above question alone. The resulting 7 different matrices could then be used to predict peptide binding to HLA-A2 based upon the identity of the C-terminal residue. This might improve predictions, but a systematic scanning of the effect of all the possible two, three, four etc. amino acid combinations would be a very large undertaking. Even then, the resulting predictions would still be based on a matrix approach with the built-in assumption of sequence-independence between the positions included in the scan. Therefore, generating sequence specific combinatorial PSCPL matrices does not appear to be an attractive solution. Fig. 7. General view of the construction of an artificial neural network (ANN). Shown is the topology of an input layer corresponding to 20 amono acids in four positions connected though four hidden neurons to the output neuron..

9 Reviews in Immunogenetics 2001: 3: Another way to predict binding would be to use knowledge based approaches such as simulated annealing, molecular dynamics and threading. These could incorporate some of the sequence dependent effects, and might therefore improve binding predictions. However, these methods are currently (by end 2000) very computer intensive (i.e. slow) and none have yet been generally successful (36 38). At this time, none of these approaches would allow genomewide scanning for immune epitopes. Artificial Neural Networks (ANN) are particularly well suited to perform the complex pattern recognition, which is needed to incorporate sequence specific effects and hopefully improve binding predictions. The principle of ANN is that information can be trained and distributed into a computer network with input layers, hidden layers and output layer all connected in a certain structure through weighted connections (39) (Fig. 7). Such ANN can be trained to recognize inputs (peptides) associated with a given output (MHC binding). Once trained, the network should be able to recognize the complicated peptide patterns including the correlated or non-linear effects compatible with binding. ANN have already shown promise in predicting MHC binding (40 44) and it has been reported that the accuracy of ANN-driven predictions is better than that of simple and extended motif searches (unfortunately, none of these ANN s are publicly available). Interestingly, ANN appears to be particularly well suited to eliminate false positive predictions, which happens to be the major problem of the extended motif searches. One particular advantage of the ANN approach is that it is a computational method that lends itself to fast and large-scale analysis. Thus, it has a capacity that matches the requirements for genome-wide epitope scanning. Using the ANN approach, the size and quality of the training sets become of major importance; they must be representative and accurate. Data selection and generation must therefore be carefully considered. This is particularly true for MHC since less than 1% of a random set of peptides will bind to any given MHC. Using a random selection strategy, one would have to synthesize and test more than peptides to generate a training set of just 100 examples of good peptide binders. Although such a panel of peptides could be reused, the testing at least would have to be done for every polymorphic MHC molecule. A selection strategy that identifies a representative sample of MHC binding peptides could reduce this problem significantly. Being unbiased, predictions based upon the PSCPL-derived matrices might be the ideal way to sample MHC binding peptides in a rational manner. As described above, the MHC is extremely polymorphic. A very large number of alleles per MHC locus have been registered, totaling many hundred different MHC molecules (8, 45). Generating predictions of peptide binding to each of these many MHC haplotypes would be a very large task, indeed. Fortunately, the different haplo- types are not evenly distributed. Some are very frequently represented in the various populations, whereas others are rarely represented. This affects the initial priorities. Starting out by characterizing the most frequent haplotypes, one could rapidly obtain a good coverage. It has recently been proposed that MHC molecules should be grouped according to their peptide binding specificity. MHC molecules, which have been clustered together by such a functional classification, share some common specificity features also known as supertypes. At this time, nine different supertypes have been defined and they cover more than 99% of all the major populations of the world (46). But one doesn t have to resort to all nine supertypes to obtain an impressive coverage. The three most common supertypes cover more that 85% of the various populations. A reasonable strategy would therefore be to start with common representatives of each of these three to nine supertypes. Yet, there are subtle differences between individual members of the same supertype (47). This would argue that specificity descriptions and peptide binding predictions eventually should be generated for all human MHC haplotypes. Describing and predicting the specificity of antigen processing Although the selectivity of peptide binding to MHC class I far outweighs that of peptide generation by antigen processing, the latter is still of considerable interest. As many as four out of every five peptides do not make it through antigen processing and are never offered to MHC class I (3). The overall prediction of immunogenicity could be improved if these processes could be described and predicted. Within the MHC class I pathway, antigen processing includes several consecutive steps, which generate and transport peptides of the appropriate sizes. Firstly, native proteins antigens, perhaps mostly those copies that are defective and/or misfolded (48), are ubiquitinated and thereby targeted for degradation by the 26S proteasome (reviewed in 4). This is a multicatalytic complex with several distinct sub-specificities including patterns where cleavage occurs after hydrophobic residues (chymotrypsin-like activity), after basic residues (trypsin-like activity) and after acidic residues. In reality, it is a longer sequence surrounding the cleavage site, which is recognized by the proteasome. Thus, there are several complicated cleavage patterns of the 26S proteasome. Adding to the complexity, the composition, activity and specificity of the proteasome is influenced by IFN-g, leading to a shift from a constitutive to

10 10 Reviews in Immunogenetics 2001: 3: an immune cleavage pattern. The differences between these two proteasome cleavage patterns may be highly relevant; a vaccine candidate against an infectious microorganism should fit the immune cleavage pattern, whereas a tumor vaccine candidate might fit the constitutive cleavage pattern better (49). The proteasome is not the only proteolytic activity involved in epitope generation. Work from Ken Rock suggest that there is sufficient amino peptidase activity to perform extensive N-terminal trimming of proteasome generated peptides (50). In contrast, there is very little carboxypeptidase activity, which can assist in performing C-terminal trimming. Thus, there is no requirement for the proteasome to generate the N- terminus of an epitope, whereas there is a stringent requirement for the proteasome to generate the C-terminus directly. Obviously, the epitope itself should not be degraded by the proteasome. The specificity of the proteasome cleavage has been described using in vitro digestion of model peptide and protein substrates (51 53). Predictions of proteasome cleavage are currently being developed by several groups (54, 55). The realization that the proteasome must generate the final C-terminal of an epitope (56), a Cflush requirement, suggests another approach to deduce the in vivo specificity of the proteasome. The C-terminal of any natural ligand should by this token be part of a proteasome cleavage site. The entire cleavage site can be found at the corresponding position within the originating protein (the latter can frequently be extracted from the available protein databases). With the large number of known natural ligands it becomes possible to assemble a training set of known cleavage sites, which can be used for developing ANN (Kesmir, Schild, Nussbaum, Rammensee and Brunak, submitted). A server, which predicts how frequent every peptide bond throughout a protein is cleaved by the proteasome, has been set-up ( Secondly, the peptides generated in the cytosol by the proteasome (and trimmed by aminopeptidase) must be translocated into the ER. This is frequently being done by the transporter associated with antigen processing (TAP) transporter complex (57, 58). Several in vitro assays have been generated to address TAP binding (59 61) and in some cases combinatorial peptide libraries have been used to describe the specificity of TAP (62, 63). Brusic and co-workers have used a large series of experimental data to train an ANN capable of predicting TAP binding (64) (this is, however, not publicly available). The TAP molecule appears to prefer peptides 8 16 amino acids long, however, it will accept even longer peptides. It has been suggested that human TAP is specific for peptides with C-terminal hydrophobic amino acids and that it also exhibits some specificity for the amino terminal end of a peptide (63). Note, however, that if the N-terminus can be trimmed by aminopeptidases both in the cytosol and in the ER, then each pro- teasome-generated peptide will have several chances of being trimmed, translocated and if need be re-trimmed to fit the MHC class I. In that case, the C-terminal specificity would be the functionally important specificity of the TAP system. Finally, it can not be ruled out that other functionally important events will be identified in the future (binding to chaperones, TAPasin, etc.). Any process that involves significant selection should eventually be described, and relevant prediction tools be developed. Integrating predictions and applying them to entire genome/proteomes As described above, T cell epitopes are selected in a series of events featuring individual specificities. It is difficult to envision one single rule to identify T cell epitopes. The strategy advocated here is to obtain detailed descriptions and predictions for each of the selective events involved and then re-approach the physiology of antigen presentation by combining the predictions of the different events (1). This has the advantage that it should allow the development and optimization of quantitative analytical methods for each event separately. In this context, the value of quantitative analyses and predictions should be stressed. Quantitative predictions lend themselves to computational analyses, and would make it easier to compare different peptides in terms of how efficiently they are generated and selected. They would facilitate the integration of the predictions of the different events involved in antigen presentation, thereby generating an overall quantitative prediction that should allow the identification of peptides of high, intermediate and low efficiency presentation. In a simplistic way this integration could be achieved in the following way: The proteasome cleavage frequency for each peptide bond within a given protein is predicted and used to calculate how efficiently any internal peptide will be generated. The efficiency of TAP translocation is predicted and used to calculate how efficiently each peptide is transported and offered to MHC. The MHC binding affinity is predicted and used to calculate the relative MHC occupancy. The overall efficiency of antigen presentation could be derived by dividing the predicted MHC occupancy by the predicted proteasome generation and by the predicted TAP translocation. From any given protein, one would then be able to predict the relative presentation of the different possible epitopes within that protein. It should be possible to compare these epitopes and select the optimal one. If the relative levels of different proteins are known, one might even compare epitopes generated from differ-

11 Reviews in Immunogenetics 2001: 3: ent proteins. This should enable a genome- or proteome-wide search for optimal epitopes. One of the most important sorting criteria will be the efficiency of presentation. Another important sorting parameter will be the degree of conservation since the ideal vaccine candidate in general will be highly conserved. With the present sequencing effort, we can expect that all pathogenic microorganisms will be represented by many different full-length sequenced isolates. Predicting epitopes, even from a small genome like that of a virus, is a large task at present. As an example, the HIV virus genome encodes for nine proteins, totaling some 3000 amino acids. Every HIV genome could potentially generate as much as about 9000 peptides of eight to ten amino acids in length. More than 100 full-length sequenced HIV isolates are presently available in public databases. Predicting epitopes from a larger genome is an immense task. An intracellular bacteria like Mycobacterium tuberculosis (exemplified by the H37Rv isolate) encodes for 3918 proteins, totaling some 1.33 million amino acids, which could potentially generate about 4 million peptides of eight to ten amino acids in length. Analyzing several isolates of the same microorganism in the search for conserved epitopes could multiply the task. Powerful computational tools to handle the input of a whole proteome and sort the output predictions are therefore going to be essential. It should also be possible to use genomic data as input data. When comparing several different isolates from the same microorganism, one might translate the genome in all six reading frames and do an epitope search. Assuming that the most interesting epitopes are conserved at least at the protein level one should find them by an epitope search that includes sorting by degree of conservation. Logistically, this might be a simpler search strategy, and any suggested epitope could afterwards be looked for in the open or alternative reading frames. Identifying and designing novel peptide epitopes At a frequency of one out of 2000, the number of immunodominant epitopes is limited. This is not a problem when dealing with something as big as bacteria. However, for a virus the size of HIV, a priori one would not expect to find many vaccine candidates per MHC. Quantitative predictions might remedy this situation since they will identify intermediary MHC binders (IC 50 between 50 and 500 nm) and there will be many more intermediary binders than high affinity binders. Although these peptides are less likely to be immunodominant, they might still be subdominant. It has been shown that immune responses raised against subdominant epitopes (in the simplest version, this could be achieved by immunizing with the subdominant peptide itself) are perfectly valid immune targets and can provide immune protection (65 68). Potentially, there might a wealth of such epitopes awaiting exploitation in vaccine formulations. An interesting application of quantitative predictions is the identification of natural epitopes that are intermediary binders and whose binding can be improved by directed design. Peptides with improved MHC binding tend to be better immunogens (69 71). Such enhanced peptides may be very useful, in particular if the T cell responses that can be raised against the designer epitopes crossreact with the parental epitope. This situation is likely to occur where the intermediary binding parental peptide features suboptimal primary anchor residues. Structurally, primary anchor residues are deeply embedded in the MHC and are not available for T cell recognition. Thus, replacing a suboptimal anchor residue with an optimal residue is likely to improve binding without grossly affecting T cell recognition. It is therefore reasonable to expect that anchor-improved epitopes should be able to induce protective immunity against the natural infection. Using genomes as input data might allow one to identify other rare epitopes with potentially interesting applications. In addition to the conventional start codon, AUG, at least six other alternative translation initiation sites are used, albeit, at a low frequency (72). Nonetheless, some of the protein products resulting from these alternative reading frames can be recognized by the immune system (73 75). It would be a rather simple addition to the suggested prediction programs to add a search for epitopes from alternative reading frames. It also seems reasonable to suggest that similar rare and unconventional epitopes could be deduced from protein products resulting from genetic recombination events (76 78). All of the above alternative epitopes might be protective. From a theoretical point of view, they may have interesting properties. When dealing with epitopes of foreign origin, highly expressed epitopes are more likely to be immunogenic than are scarcely expressed epitopes. The latter epitopes are more likely to be subdominant, if they are not simply ignored by the immune system. Subdominant and ignored epitopes might have unique evolutionary properties since the immune system has disregarded these epitopes under conditions of natural infections. When dealing with epitopes of self-origin, highly expressed epitopes would be expected to be more likely to induce near perfect tolerance than would scarcely expressed self-epitopes (79). In contrast, intermediately expressed self-epitopes could be expected to be more likely to circumvent tolerance and may therefore have interesting properties, e.g. in identifying autoimmune epitopes and also in identifying targets for tumor therapy.

12 12 Reviews in Immunogenetics 2001: 3: approach, where the specificity of each polymorphic sub-site is described experimentally. The specificity of an entire MHC can then be derived by combining the sub-site specificities relevant for the MHC class II in question, and the resulting set of matrices used to predict the binding of peptides (87) ( Any MHC class II molecule composed of the experimentally determined subsites can be addressed, even MHC class II molecules that have not been examined previously. Note, however, that this method assumes peptide sequence independence. Others have generated ANN to predict peptide binding to MHC class II, thereby attempting to include sequence specific correlated effects (43). Box 4. The future Prediction of CD4 π T helper cell epitopes Antigen presentation and MHC are involved in an important dichotomy in the T cell immune system. Endogenously derived proteins are presented by MHC class I molecules to CD8π T cytotoxic cells, whereas exogenously derived proteins are presented by MHC class II molecules to CD4π T helper cells. The structures of MHC class I and II are similar, but the rules of peptide binding to MHC class II molecules are slightly different. In particular, MHC class II molecules bind longer peptides and readily accept protrusions out of the binding cleft (80 82). Thus, the gamesh of peptides bound are not in register and this complicates the interpretation of pool sequencing experiments (83, 84). This is also true of binding experiments using a positional scanning combinatorial peptide library approach where the discriminatory power between different amino acids within each position is reduced (85). The ability to bind peptides extending out of the MHC class II, however, does allow physical selection principles like those of phage display of peptides (pioneered by Sinigaglia & Hammer (86)). Once a cohort of peptides has been identified, they are aligned to get them in register, and then analyzed to identify the motif. Recently, this group has developed an ingenious multicombinatorial The long-term goal is to translate genomic/proteomic information into predictions of T cell epitopes (Box 4). The short-term goals should be to describe the peptide binding specificities of the most common MHC molecules, and develop the corresponding prediction tools. This would rapidly generate significant population coverage. Eventually, all human MHC class I and II molecules should be included, and so should all other relevant and selective processes involved. MHC specificities should be described in terms of natural ligands as well as in terms of detailed matrices. Computational prediction tools should be developed. Descriptions and predictions should be made available, preferably as web-based services linked to other proteomic tools. This would allow researchers and clinicians to search for immunogenic epitopes from individual proteins as well as entire proteomes. Standard protocols and reagents should be developed that would allow the same users to validate these predictions. Scientifically, these tools will allow the functional implications of MHC polymorphism to be pinpointed and explained. From a practical point of view, these tools will allow immune epitopes to be mapped with great precision. This will be useful in the identification of vaccine candidates from the genomes of pathogenic microorganisms, of autoimmune epitopes from the human genome, and possibly also of tumor vaccine candidates from tumor expression profiles (88).

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