HIV PEPTIDES AND NUCLEIC ACIDS ENCODING THEM FOR DIAGNOSIS AND CONTROL OF HIV INFECTION

Abstract: The present invention relates to the identification of CTL epitopes by the combination of biochemical assays, statistical matrix calculations, and artificial neural networks. A set of peptide libraries are used to generate complete unbiased matrices representing peptide-MHC interactions used to generate a primary prediction of MHC binding for all possible non-redundant peptides. The best binders are subject to a quantitative biochemical binding assay and subsequently a computerised artificial neural network prediction program built from these in vitro experimental MHC-I binding data. The method further comprises improving the identified epitope by replacing amino acids, and testing the identified CTL epitopes in in vitro and in vivo models. Thus, one aspect of the invention relates to the identification of a CTL component of a vaccine and the development of said CTL component. Another aspect of the invention relates to the identified epitopes of said CTL component.

Description: HIV PEPTIDES AND NUCLEIC ACIDS ENCODING THEM FOR DIAGNOSIS AND CONTROL OF HIV INFECTION FIELD OF INVENTION The present invention relates to the identification of CTL epitopes by the combination of biochemical assays, statistical matrix calculations, and artificial neural networks. The invention further relates to the testing of identified CTL epitopes in biochemical in vitro assays and in in vivo animal models. One aspect of the invention relates to the use of the identified and tested CTL epitopes in medicine e.g. for the preparation of diagnostic, prophylactic and therapeutic agents. BACKGROUND OF THE INVENTION A virus is an intracellular organism. Therefore, cytotoxic T-cell lymphocytes (CTL) are a major protective mechanism against viral diseases. Antibodies may neutralise extracellular Human Immune deficiency Virus (HIV) and thus limit or prevent infection of cells in the host (Shibata et al., 1999), whereas CTL will limit the viral production by killing the cell, inducing apoptosis, inducing antiviral substances, and/or inducing increased intracellular lysis in already infected cells and thus cure or prevent the disease. A considerable amount of evidence suggests that CD8 + CTL plays a major role both in preventing and limiting the initial HIV infection (Koup et al., 1994) as well as being active throughout the HIV infection (Harrer et al., 1994). Correlations between CTL activity and protection have been observed in monkeys (Schmitz et al., 1999) and in challenge experiments (Gallimore et al., 1995). Thus, in order to prevent and/or treat HIV infections by a vaccination activating CTL, the identification of new CTL epitopes is of great importance. Progress made over the last two decades has conclusively demonstrated that one of the most important immune recognitions - that of T-cells - uses peptides as one part of a complicated target structure. T-cells are specific for peptides presented in the context of Major Histocompatibility Complex (MHC) molecules; a phenomenon which is known as MHC restriction. During ontogeny, the MHC molecules available to the host are involved in shaping the T-cell repertoire through selection processes, which results in T-cell recognition being restricted by the host MHC molecules. Later in life, only pathogen- derived peptides presented in the context of one of the host MHC molecules can be recognised by the host T-cell immune system. Thus, the particular MHC molecules available to the host have a very direct impact on the specificity of the T-cell immune system. The specificity of the MHC molecules is therefore of considerable scientific and practical interest. A good correlation between the MHC restriction and the ability of the MHC molecule to bind e.g. a putative CTL epitope in a biochemical assay has been established. The major advantage of the biochemical approach is that binding can be accurately quantified and the MHC specificity can be addressed in isolation, very pointedly and under highly controlled conditions. The present belief is that the MHC specificities, such as the primary anchors, secondary anchors, and disfavoured amino acids, can be investigated through the interaction between purified MHC and the peptide. The biochemical specificity of some MHC molecules have now been described in great detail (Falk et al., 1991, Ruppert et al., 1993) and explained structurally (Madden et al., 1995). As expected, the peptide binding specificity is quite broad. This broad specificity is obtained through the recognition of peptide motifs; a recognition mode which requires the presence of important structural requirements such as the presence and proper spacing of particular amino acids in certain anchor positions (Falk et al., 1991 , Sette et al., 1987, Rammensee et al., 1995). Thus, peptides binding to the same MHC allotype exhibit some degree of similarity, but there is no requirement for identity. X-ray crystallography has identified a unique peptide binding site at the outer polymorphic domains of the MHC (Fremont et al., 1992). These outer domains form a groove which can be subdivided into various pockets, A through F (Garrett et al., 1989). The majority of the peptide-MHC bonds involve peptide main chain (or backbone) atoms including the termini for MHC class I (Fremont et al., 1992). This explains how one MHC haplotype can perform high affinity (K D = 10 "8 - 10 "9 M) binding of a large and diverse repertoire of peptides, as backbone atoms are common to all peptides. Only the minority of the binding energy involves peptide side chain atoms. These interactions, however, are believed to explain the specificity of the MHC (Matsumura et al., 1992). The MHC specificities have been represented in various ways, the most elaborate of these being complete statistical matrices representing an indication of the frequency of a particular amino acid in a given position of the epitope. Such matrix can be based on the analysis of large series of analogues (a biased matrix), or on the analysis of peptide libraries (an unbiased matrix) - the latter yielding significantly better predictions. All matrix- driven predictions, however, make the assumption that the specificity of each sub-site is independent of the residues present in the neighbouring sub-sites. The reasonable success of the matrix-driven predictions would support this as a general assumption. However, crystal structures have clearly identified examples that violate this assumption. Further improvement in binding predictions must therefore take the entire sequence into account including more or less long ranging correlated effects. It is difficult to envision any simple set of rules that would include these non-linear effects. It was recently demonstrated that a completely random 8-mer peptide library contains sufficient peptide binders to be detected in an MHC binding assay. These findings suggest a strategy which requires that all possible peptides of a given size and composition under consideration are represented by a systematic set of sub-libraries. In each sub-library, one amino acid in one position is kept constant whereas the remaining positions contain mixtures of amino acids. A similar design was recently reported by Houghten and co-workers who termed it "positional scanning combinatorial peptide library" (PSCPL). To avoid labour intensive classical experimental epitope mapping, the identification of CTL epitopes in computerised predictions from linear protein sequences may be used to limit the amount of putative epitopes to be tested. For this purpose, more or less accurate prediction programs have been developed (Schafer et al., 1998) and some of these are also available on the internet. Artificial Neural Networks are particularly well suited to perform complex pattern recognition and have already shown some promise in predicting MHC binding. Detailed disclosure of the invention The broadest aspect of the invention relates to a method to identify a CTL epitope comprising the steps of: (a) generating primary position specific prediction means from experimental MHC class I structural or peptide binding data; (b) identifying potentially high affinity binding epitopes by scanning a set of protein sequences and calculating the binding affinity according to the primary prediction means obtained in step (a); (c) optionally reducing the high number of peptides identified in step (b) by exclusion means based on sequence similarity; (d) generating experimental binding data for the peptides identified in step (c) or (b); (e) training one or more artificial neural networks to predict binding affinities to MHC class I, using the experimental binding data from step (d) such that the individual peptide binding data examples, weighted according to their frequency in sub-intervals in the binding affinity range of 1nM to 50,000 nM, are equally presented; and (f) estimating the binding affinity of a query peptide by testing said query peptide in each of the artificial neural networks trained in step (e) obtaining an approximate binding affinity of the query peptide from each of the artificial neural networks, and calculating the weighted average of the approximate bindings thereby obtaining the estimated binding affinity of the query peptide; the CTL epitope having a weighted average of the MHC class I binding affinity of less than 500 nM. The described method can be further improved by steps described in the following. It should be noted that any combinations of the suggested modifications can be added to the method. In one aspect, the method described in steps (a) through (f) can be further improved by the steps of: (g) identifying a query peptide from step (f) for which at least one artificial neural network predicts a high affinity binder and at least one other artificial neural network does not; (h) generating experimental binding affinity data for the peptide identified in step (g); (i) training one or more artificial neural networks on the combined experimental binding affinity data from step (d) and (h) such that the individual peptide binding affinity data examples are weighted according to their frequency in sub-intervals in the binding affinity range of 1nM to 50,000 nM are thus equally represented; and (j) estimating the binding affinity of a query peptide by testing said query peptide in each of the artificial neural networks trained in step (i) obtaining an approximate binding affinity of the query peptide from each of the artificial neural networks, and calculating the weighted average of the approximate bindings thereby obtaining the estimated binding affinity of the query peptide; the CTL epitope having a weighted average of the MHC class I binding affinity of less than 500 nM. The iterative process described in steps (g)-(i) can be repeated for all query peptides estimated to have high affinity binding to MHC class I by at least one artificial neural network and to have a low affinity binding to MHC class I by at least one other artificial neural network. The CTL epitopes where the artificial neural networks "disagree" are presently contemplated to contain structural features on which the MHC class I binding affinity is complex to estimate. By determining experimentally the true binding affinity of these CTL epitopes the predictability of the MCH class I binding affinity by the artificial neural networks is improved. Primary position specific prediction means can be generated in many ways. In one embodiment of the invention, primary position specific prediction means are generated by Pool sequencing as described by Falk et al., 1991. This procedure leads to the identification of a simple natural motif and can be represented as rough classifications of more or less preferred amino acids in various positions. In another embodiment, primary position specific prediction means are generated by extended motifs as described by Ruppert et al., 1993. This procedure containing biochemical experiments can be reported as a complete statistical matrix stating the frequency of all amino acids in each position. Such experiments are based on experiments using panels of more or less randomly selected peptides (e.g. as described by Ruppert et al., 1993, Parker et al., 1992) or on peptide library approaches (e.g. as described by Stryhn et al., 1996). In yet another embodiment, primary position specific prediction means are generated by structural information. It is preferred that the simple motifs and the statistical binding matrices are used to perform a crude search for MHC binding peptides (e.g. as described by Sette et al., 1989, Meister et al., 1995). Predictions are improved considerably when the extended motifs rather than the simple motifs are used. Statistical matrices, such as those generated by the PSCPL approach, are used in a straightforward fashion to calculate the predicted binding. Assuming that each amino acid in each position contributes with a certain binding energy independent of the neighbouring residues ('independent binding of side-chains') and that the binding of a given peptide is the result of combining the contributions from the different residues (Parker et al., 1992), multiplying the relevant matrix values gives an indication of the binding affinity of the corresponding peptide. Yet other embodiments of the invention relates to knowledge-based predictions using approaches such as molecular dynamics (e.g. as described by Rognan et al., 1994, Mata et al., 1998), computational threading (e.g. as described by Altuvia et al., 1995, Altuvia et al., 1997), and computational approaches to address the specificity of MHC pockets (e.g. as described by Vasmatzis et al., 1996, Zhang et al., 1998). The PSCPL approach has several important advances compared to previous approaches used to determine MHC class I specificities. In particular, it is universal and unbiased. The PSCPL approach identifies all functionally important components of MHC class I binding. The PSCPL approach appears to be more sensitive to secondary anchors and disfavored amino acids than an analogue approach. In fact, it allows a detailed quantitative description of the combinatorial specificity of MHC class I specificities and generates a complete binding matrix specific for the MHC class I in question. These matrices can be used to improve predictions of binding (Stryhn et al., 1996). The PSCPL approach is universal since many different MHC molecules can be addressed with the very same set of PSCPL sub-libraries. This will reduce the costs, experiments and data handling that are otherwise associated with past technologies significantly. A detailed mapping of many MHC specificities can therefore easily be envisioned. A very robust, low-tech, yet accurate and high-throughput technology for analysing peptide-MHC binding has been developed. A modified spun column gel filtration assay was optimized for efficient separation of free peptides and MHC-bound peptides through a novel principle, which has been termed "gradient centrifugation" (Buus et al., 1995). It has several advantages compared to the classical gel filtration assay: The sensitivity and the throughput is much better, it demands fewer resources both in terms of unique reagents and labour, and it generates less hazardous waste. Once a binding assay is set up, it is easy to determine the binding capacity of any other preparation by using this unknown preparation as inhibitor of the known interaction. The less inhibitor needed to obtain 50% inhibition (the IC 50 ), the better the binding. The uninhibited maximum binding must be less than 30% so that the measured IC 50 will be an approximation of the K D (otherwise ligand depletion is experienced and the IC 50 values cannot be trusted). Natural HLA molecules are commonly obtained from EBV transformed B cell lines (e.g. from homozygous cell lines from the 12.th IHWC). They are usually grown in large scale in vitro culture, extracted in detergent, purified by affinity chromatography and concentrated. In the present working examples, complete 8-mer, 9-mer and 10-mer sets of PSCPL were synthesised as described in table 1 , table 6 and in example 1. Table 1 : PSCPL design and nomenclature Position 1 Position 2 Position 3 Position 8 AXXXXXXX (AX7) XAXXXXXX (XAX6) XXAXXXXX (X2AX5) etc XXXXXXXA (X7A) CXXXXXXX (CX7) XCXXXXXX (XCX6) XXCXXXXX (X2CX5) etc XXXXXXXC (X7C) DXXXXXXX (DX7) XDXXXXXX (XDX6) XXDXXXXX (X2DX5) etc XXXXXXXD (X7D) etc etc etc etc etc YXXXXXXX (YX7) XYXXXXXX (XYX6) XXYXXXXX (X2YX5) etc XXXXXXXY (X7Y) For each position, 20 sub-libraries were synthesized, each representing one of the 20 naturally occurring L-amino acids at that position. All other positions contained mixtures of amino acids. This lead to the synthesis of a total of 8 x 20 = 160 sub-libraries representing all 25.6 x 10 9 possible 8-mer peptides etc. Each PSCPL sub-library was tested for its ability to bind to three different class I molecules in a biochemical binding assay. Strikingly, strong MHC dependent signals were detected (anchor residues represented up to 90% of the amino acids found at the corresponding anchor positions, whereas less than 1% disfavored amino acids were found in some positions) showing that the MHC indeed did select peptides from these unbiased peptide libraries. The PSCPL approach clearly identified the primary anchor residues, and it also identified secondary anchor residues and disfavoured residues. Corresponding to every position throughout the 8-mer motifs of the three MHC class I molecules tested, the PSCPL identified amino acids which could increase or decrease binding, ie. every position can contribute to the overall binding specificity. Reassuringly, the known specificities were confirmed for three out of three MHC class I molecules examined (Stryhn et al., 1996). Furthermore, several minor, but significant, positive contributions and so far unknown numbers of disfavored amino acids were identified. Particularly noteworthy, 12-14 of the 20 amino acids were unacceptable in the known anchor positions, and disfavoured amino acids were also frequently found scattered throughout the remaining positions. The data can be reported as statistical matrices consisting of relative binding factors. These are calculated for each amino acid at each position as IC 50 (X8) divided with IC 50 (PSCPL sub-library), as whether a given amino acid in a particular position increases (RB> 1) or decreases (RB< 1) binding compared to an "average amino acid" in that position. The data can be tabulated and used in a convenient way to illustrate the features of the binding specificity in quantitative terms (see table 6 for an example). Improved predictions can be performed using matrix values generated by the PSCPL. Assuming that each amino acid in each position contributes with a certain binding energy independent of the neighbouring residues and that the binding of a given peptide is the result of combining the contributions from the different residues (Parker et al., 1992), 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 complete random library. The relative success of the PSCPL derived matrix-driven prediction would suggest that the binding of an unknown peptide to a given MHC molecule is to some extent the result of a combinatorial specificity - confirming the assumption. Thus, in a preferred method according to the invention a PSCPL is used to generate primary position specific prediction means in step (a). The Major Histocompatibility Complex (MHC) is essential for vertebrate immunology especially for the T-cell mediated immune response. In humans, the term HLA complex is used, whereas the term H2 complex is used in mice. The structural characteristics of the HLA and H2 complexes are fairly similar. Thus, in a preferred embodiment, the MHC class I structural or peptide binding affinity data in step (a) are HLA structural or peptide binding affinity data. By using HLA structural or peptide binding affinity data, the CTL epitopes identified by the method according to the invention will be CTL epitopes binding to the HLA complex, and thus especially preferred in the treatment, prophylaxis and/or diagnosis of humans. In a preferred embodiment the HLA subtypes A1 , A2, AB, A11 and/or A24 are selected. Throughout the present application, the term "MHC class I" is used. However, as it should be obvious to the person skilled in the art, if CTL epitopes are to be used in human medicine they should bind to HLA. One aspect of the present invention relates to the use of primary position specific prediction means, such as unbiased matrix predictions, to scan the non-redundant part of a protein database, such as the TrEMBL database or the SWISS-PROT databases, for potentially high affinity binding epitopes. In this context, high affinity binding epitopes are defined as epitopes with a predicted binding affinity in the primary position specific prediction matrix of IC 50 less than 100 nM, that is an approximation of K D < 100 nM. It is contemplated that this scanning will result in a large selection of potentially high affinity binding epitopes. Therefore, the high number of peptides are optionally reduced by exclusion means based on sequence similarity. In a preferred embodiment of the invention, the sequence similarity reduction is performed ad modum Hobohm. The next step in the method of the invention implies that the resulting representative sample of potentially high affinity binders is synthesised and tested in the biochemical binding assay. An illustrative, but not limiting, example of synthesising and testing epitopes is given in example 1. Although the sequence independent combinatorial specificity may be correct as an average consideration, it is certainly known to be wrong for individual peptides. Based on crystal structures of the MHC molecules, it is unlikely that independent combinatorial specificity is correct. In contrast, it has been demonstrated that artificial neural networks are particularly well suited to handle and recognise any non-linear sequence dependent information or interaction. Such relations can be trained into neural network algorithms comprising an input layer, one or more hidden layers, and an output layer, where the units in one layer are connected by weights to the units in the subsequent layer. Such artificial neural networks can be trained to recognise inputs (i.e. peptides) associated with a given output (i.e. MHC binding affinity values). Once trained, the network will recognise the complicated peptide patterns compatible with binding. In the artificial neural network approach, the size and quality of the training set become of major importance. This is particularly true for the HLA, since only about 0,1-1% of a random set of peptides will bind to any given HLA. Thus, to generate as little as e.g. 100 examples of peptide binders will, if random peptides are screened, require the synthesis and testing of some 100 000 peptides. This will be a very resource and labor intensive proposition even at this modest number of binders in the training set. However, by initial PSCPL selection of peptides, the number of peptides needed to be synthesised to get a reasonable amount of true HLA binders is dramatically reduced. These data are subsequently used to train the artificial neural networks. The network is 5 trained to predict the actual real-valued strength of the MHC binding, and not, as in previous approaches, artificially selected categories of binders and non-binders. Using a balanced training scheme, where the original data, having a skew distribution over the binding affinity interval ranging from 1 nM to 50 000 nM, is selected, such that examples from sub-intervals are presented equally often, results in a network that is able to predict 10 almost equally well over the entire range. Thereby, over-prediction, or under-prediction is avoided that would bias the network such that it mostly would identify either binders or non-binders. The next step of the method according to the invention comprises estimating the binding 15 affinity of a query peptide. The query peptide can be derived from any source of peptide libraries such as random peptide combinations or commercial, private, or public databases of protein sequences. In a preferred aspect of the invention, the protein database is a HIV-1 or HIV-2 protein sequence database, or a part thereof. A preferred database comprises full-length sequenced HIV-1 genes, such as The Los Alamos 20 1998/1999 HIV sequences database (http://hiv-web.lanl.gov/). These proteins are the products of translations of all available full-length sequenced HIV-1 genes and reflect the principal genetic diversity of HIV-1. In one embodiment of the invention, the artificial neural network comprises three layers of 25 neurons. In another embodiment, the query peptide is presented to the artificial neural network as a binary number of 20 digits. As a result of this embodiment, the input layer will consist of 20 times the number of amino acids in the query peptide. As described above, it can be of significant value to have the binding affinity estimated by 30 several artificial neural networks. Thus, in one embodiment of the invention, one or more artificial neural networks are trained. The training is preferably performed on different parts of the training data to incorporate as many features as possible to the network. In one aspect of the invention, only one artificial neural network is trained. In another embodiment 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25 or 30 artificial neural 35 networks are trained. In a most preferred embodiment, 7 artificial neural networks are trained. In a preferred embodiment, the training algorithm is a gradient descent type, where the adjustable network weights iteratively are modified in order to make the network produce the correct output upon presentations of the sequence fragments. In an even more preferred embodiment, the networks are trained using the error function suggested by McClelland. In one embodiment of the invention, each artificial neural network is tested. The testing is preferably performed by cross validation. An illustrative example of cross validation testing is given in example 2. The essential step in the use of the artificial neural networks trained relates to estimating the binding of a query peptide. This is carried out by testing said query peptide in each of the artificial neural networks trained obtaining an approximate binding affinity of the query peptide from each of the artificial neural networks, and calculating the weighted average of the approximate bindings, thereby obtaining the estimated binding affinity of the query peptide. Based on phylogenetic studies, HIV-1 viruses are classified into 3 genetic groups; group M for Major, group O for Outer and group N for New. While HIV-1 group M viruses are responsible for the majority of HIV-infections world-wide, HIV-1 group O and N are mainly found in restricted areas in Central Africa (mostly Cameroon). HIV-1 group M is currently subdivided into 8 reference genetic subtypes: A, B, C, D, F, G, H, J (available at http://hiv- web.lanl.gov/ ). However, recombination events between distinct subtypes have been demonstrated in individuals who are co-infected with more than one genetic subtype. Subtypes AB, AC, AD, ADI, AE, AG, AGI, AGJ, BF, CD are intersubtype-recombinant HIV-1 sequences. Albeit, viruses belonging to group M are responsible for most of the HIV-1 infection cases reported by WHO. The world-wide spreading potential of HIV- 1 group O and N viruses is not known and should not be underestimated. Moreover, the recent characterisation of HIV-1 M/O recombinant viruses raises new speculations on the pandemic spreading of such viruses. After the identification of a CTL epitope, based on the artificial neural network estimation of the binding affinity of a query peptide, the CTL epitope must, in most embodiments of the invention, have further properties to be well suited in medical use such as incorporation into a vaccine or a diagnostic agent. One such property is a certain conservation of the CTL epitope among HIV groups and subtypes. Thus, in one embodiment of the present invention, the method to identify a CTL epitope further comprises the step of: (k) determining the global conservation of the query peptide across a set of HIV protein sequences; the CTL epitope of step (k) having an MHC class I binding affinity of less than <500 nM. In this context, the term "global conservation" should be understood as a number representative for, among the 9 HIV-1 proteins Gag, Pol, Env, Vif, Vpr, Vpu, Tat, Rev and Nef, how often the CTL epitope is found in all subtypes and groups of HIV-1. The percent global conservation is calculated as the number of protein sequences harbouring the query sequence divided by the total number of protein sequences times 100. The term "intra-subtype conservation" should be understood as a number representative for, among the 9 HIV proteins Gag, Pol, Env, Vif, Vpr, Vpu, Tat, Rev and Nef, how often the CTL epitope is found within one subtype of HIV. For example, if one epitope is found in 6 out of 8 HIV-1 subtype A Gag proteins, but also in 10 out of 32 HIV-1 subtype B Gag proteins and in 4 out of 8 HIV-1 subtype C Gag proteins, its intra-subtype A, B and C conservation will be 75%, 31,25% and 50% respectively. In one embodiment of the invention, the global conservation is determined by comparing across all subtypes of HIV protein sequences in the Los Alamos HIV-1 protein database. In a preferred embodiment of the invention, the global conservation is more than 1%. In a related aspect, it is preferred that the method according to the invention is carried out with a global conservation of more than 1 % and a cut off value for the weighted average of the MHC class I binding affinity for the query peptide of less than 100 nM. Hereby, a CTL epitope with high affinity binding and an intermediate global conservation is obtained. This especially preferred embodiment is further described in example 4. An example of specific CTL epitopes identified by the method according to the invention, satisfying these criteria are given in table 5A. In another embodiment of the invention, the global conservation is more than 2%, such as more than 3%, 4%, 5%, 6%, 7% 8%, 9%, 10%, 11%, 12%, 14%, 16%, 18%, or more than 20%. In a preferred embodiment, the global conservation is more than 8% across HIV protein sequences. In a related aspect, it is preferred that the method according to the invention is carried out with a global conservation of more than 8% and a cut off value for the weighted average of the MHC class I binding affinity for the query peptide of less than 500 nM. Hereby, a CTL epitope with an intermediate affinity binding and a high global conservation is obtained. In a especially related embodiment, only a CLT epitope with intermediate binding is identified by the further criteria that the weighted average of the MHC class I binding affinity is more than 50 nM. This especially preferred embodiment is further described in example 4. An example of specific CTL epitopes identified by the method according to the invention, satisfying these criteria are given in tables 5B and 5C. In a general aspect of the invention, the weighted average MHC class I binding affinity is less than 1000 nM, such as less than 500, less than 300, 200, 100, 50, 25 or even less than 10 nM. As will be recognised by the person skilled in the art, peptides naturally presented by MHC class I molecules are 8 to 10 amino-acid long and contain specific anchor residues that share properties like hydrophobicity or charge. The anchor residue side chains are deeply embedded within the antigen-presenting groove of the MHC molecule, suggesting that anchor residues are primarily involved in MHC class I binding. Because anchor residue side chains are buried in the peptide binding groove, it is believed that anchor residues are not directly involved in the T-cell response. This suggest that anchor residues can be replaced without affecting T-cell recognition. In the case of the HLA complex, it is generally accepted that the binding affinity of an HLA-A2 peptide epitope can be improved by replacing non-optimal amino acids in primary anchor positions with more optimal amino acids in said primary anchor positions. It is contemplated that this is the case for all CTL epitopes according to the invention. Thus, using HLA-A2 as an example, the primary anchor positions are defined as the second amino acid in the epitope (2) and the terminal amino acid in the epitope (Ω). For example, the primary anchor positions of a 9 amino acid long HLA-A2 binding motif are located at position 2 and position 9. In a preferred embodiment, the amino acids in anchor position 2 are replaced with Leucine, Methionine, Glutamine, or Isoleucine. The most preferred optimal primary anchor amino acids in this context is Leucine at position 2. In another embodiment of the invention, the amino acids replaced in anchor position Ω are Valine, Isoleucine, Leucine, or Alanine. The most preferred optimal primary anchor amino acids in this context is a Valine at position 9. It is presently contemplated that immunisation with a modified CTL epitope with improved binding affinity will induce a higher CTL response than that induced with the natural CTL epitope. Therefore, the identification of intermediate CTL epitope binders in the present inventionn is very important. Such intermediate binders are potential new targets against HIV infected cells. During the infection, these intermediate binding epitopes bind to MHC-I with an affinity that is too low to induce sufficient immunity but high enough to provide an excellent target if such sufficient immunity can be induced. In one preferred embodiment of the invention, the non-immunogenic intermediate binders, identified by the method described in the present invention, can be turned into immunogenic by exchange of amino acids in anchor positions. The possibilities to exchange anchor positioned amino acids can further be evaluated for effects on the binding affinity of the epitope with the artificial neural network. Thus, the combination of identifying intermediate binders which are antigenic but not sufficient immunogenic, taken together with the exchange of amino acids in anchor positions for improving MHC-I binding affinity is new and provides many new possibilities for a successful HIV vaccine. The CTL effectors raised against the improved epitope will be able to recognise and subsequently lyse target cells presenting the natural epitope at their surface. Table 3 Anchor pos.2 Anchor pos.9 LEU X X X X X X VAL MET ILE GLN LEU ILE ALA All combinations of amino acid modification in table 3 at positions 2 and/or Ω are possible, however, they will not yield the same binding affinity for MHC class I. The binding affinity is also influenced by secondary anchor positions. Thus, in one embodiment of the invention, the method of identifying a CTL epitope further comprises the steps of: (I) modifying the CTL epitope by computationally replacing amino acids in the anchor positions; and (m) estimating the binding affinity of the modified CTL epitope of step (I) by testing said modified CTL epitope in each of the artificial neural networks trained in steps (e) or (i) obtaining an approximate binding affinity of the CTL epitope from each of the artificial neural networks, and calculating the weighted average of the approximate bindings thereby obtaining the estimated binding affinity of the CTL epitope; the modified CTL epitope having an improved predicted MHC class I binding affinity compared to the natural CTL epitope. By improved binding affinity is obviously understood a lower IC 50 value. An illustrative example of the improvement of the binding affinity by substitution of amino acids at the anchor positions is given in example 4. An example of specific CTL epitopes identified by the method according to the invention, satisfying these criteria are given in table 5D. Thus, in an especially preferred embodiment of the present invention, a modified CTL epitope will be able to raise an immune response against a plethora of natural CTL epitopes sharing immuno-dominant parts of the epitope, that is epitopes that only differ in the amino acids in the anchor positions. A method to validate if improved CTL epitope is able to raise a CTL immune response that cross-reacts with the natural epitope is described in Example 7. When the term "improved binding" or the like is used herein it is reflected as a decreased binding affinity (or in short binding). Thus, a CTL epitope binding with 50nM has a binding of less than 500nM, and the CTI epitope binding with 50nM has an improved binding compared to the CTL eptipe with a binding for 400 nM. The CTL epitope identified by the present invention must satisfy the criteria which the cytotoxic T-cells respond to. Presently, data suggest that T-cells will respond to CTL epitopes consisting of 7, 8, 9, 10 or 11 amino acids. As should be evident from the method according to the invention, one purpose is the identification of a CTL epitope. Thus, one aspect of the invention relates to a CTL epitope identified by the method. In a further embodiment, the invention relates to a CTL epitope, which is an HIV epitope. In one aspect, the epitope identified by the method according to the invention is a CTL epitope selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ ID NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ ID NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ ID NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ ID NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41, SEQ ID NO: 42, SEQ ID NO: 43, SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ ID NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ ID NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ ID NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ ID NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ ID NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ ID NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ ID NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQ ID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 92, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO 99, SEQ ID NO: 100, SEQ ID NO: 101, SEQ ID NO: 102, SEQ ID NO: 103, SEQ ID NO 104, SEQ ID NO 105, SEQ ID NO 106 SEQ ID NO 107, SEQ ID NO 108, SEQ ID NO: 109, SEQ ID NO 110, SEQ ID NO 111 SEQ ID NO 112, SEQ ID NO 113, SEQ ID NO 114, SEQ ID NO 115, SEQ ID NO 116 SEQ ID NO 117, SEQ ID NO 118, SEQ ID NO 119, SEQ ID NO 120, SEQ ID NO 121 SEQ ID NO 122, SEQ ID NO 123, SEQ ID NO 124, SEQ ID NO 125, SEQ ID NO 126 SEQ ID NO 127, SEQ ID NO 128, SEQ ID NO 129, SEQ ID NO 130, SEQ ID NO 131 SEQ ID NO 132, SEQ ID NO 133, SEQ ID NO: 134, SEQ ID NO 135, SEQ ID NO 136 SEQ ID NO 137, SEQ ID NO 138, SEQ ID NO 139, SEQ ID NO 140, SEQ ID NO 141 SEQ ID NO 142, SEQ ID NO 143, SEQ ID NO 144, SEQ ID NO 145, SEQ ID NO 146 SEQ ID NO 147, SEQ ID NO 148, SEQ ID NO 149, SEQ ID NO 150, SEQ ID NO 151 SEQ ID NO 152, SEQ ID NO 153, SEQ ID NO: 154, SEQ ID NO 155, SEQ ID NO 156 SEQ ID NO 157, SEQ ID NO 158, SEQ ID NO 159, SEQ ID NO 160, SEQ ID NO 161 SEQ ID NO 162, SEQ ID NO 163, SEQ ID NO 164, SEQ ID NO 165, SEQ ID NO 166 SEQ ID NO 167, SEQ ID NO 168, SEQ ID NO 169, SEQ ID NO 170, SEQ ID NO 171 SEQ ID NO 172, SEQ ID NO 173, SEQ ID NO 174, SEQ ID NO 175, SEQ ID NO 176 SEQ ID NO 177, SEQ ID NO 178, SEQ ID NO 179, SEQ ID NO 180, SEQ ID NO 181 SEQ ID NO 182, SEQ ID NO 183, SEQ ID NO 184, SEQ ID NO 185, SEQ ID NO 186 SEQ ID NO 187, SEQ ID NO 188, SEQ ID NO: 189 SEQ ID NO 190 SEQ ID NO: 191 SEQ ID NO 192 SEQ ID NO: 193, SEQ ID NO: 194 SEQ ID NO 195 SEQ ID NO: 196 SEQ ID NO 197 SEQ ID NO: 198, SEQ ID NO: 199 SEQ ID NO 200 SEQ ID NO: 201 SEQ ID NO 202 SEQ ID NO: 203, SEQ ID NO: 204 SEQ ID NO 205 SEQ ID NO: 206 SEQ ID NO 207 SEQ ID NO: 208, SEQ ID NO: 209 SEQ ID NO 210 SEQ ID NO: 211 SEQ ID NO 212 SEQ ID NO: 213, SEQ ID NO: 214, SEQ ID NO 215, SEQ ID NO: 216 SEQ ID NO 217 SEQ ID NO: 218, SEQ ID NO: 219 SEQ ID NO 220 SEQ ID NO: 221 SEQ ID NO 222 SEQ ID NO: 223, SEQ ID NO: 224 SEQ ID NO 225 SEQ ID NO: 226, SEQ ID NO 227 SEQ ID NO: 228, SEQ ID NO: 229 SEQ ID NO 230 SEQ ID NO: 231 SEQ ID NO 232 SEQ ID NO: 233, SEQ ID NO: 234 SEQ ID NO 235 SEQ ID NO: 236 SEQ ID NO 237 SEQ ID NO: 238, SEQ ID NO: 239 SEQ ID NO 240 SEQ ID NO: 241 SEQ ID NO 242 SEQ ID NO: 243, SEQ ID NO: 244 SEQ ID NO 245 SEQ ID NO: 246 SEQ ID NO 247 SEQ ID NO: 248, SEQ ID NO: 249 SEQ ID NO 250 SEQ ID NO: 251 SEQ ID NO 252 SEQ ID NO: 253, SEQ ID NO: 254 SEQ ID NO 255 SEQ ID NO: 256 SEQ ID NO 257 SEQ ID NO: 258, SEQ ID NO: 259 SEQ ID NO 260 SEQ ID NO: 261 SEQ ID NO 262 SEQ ID NO: 263, SEQ ID NO: 264 SEQ ID NO 265 SEQ ID NO: 266 SEQ ID NO 267 SEQ ID NO: 268, SEQ ID NO: 269 SEQ ID NO 270 SEQ ID NO: 271 SEQ ID NO 272 SEQ ID NO: 273, SEQ ID NO: 274 SEQ ID NO 275 SEQ ID NO: 276 SEQ ID NO 277 SEQ ID NO: 278, SEQ ID NO: 279 SEQ ID NO 280 SEQ ID NO: 281 SEQ ID NO 282 SEQ ID NO: 283, SEQ ID NO: 284 SEQ ID NO 285 SEQ ID NO: 286 SEQ ID NO 287 SEQ ID NO: 288, SEQ ID NO: 289 SEQ ID NO 290 SEQ ID NO: 291 SEQ ID NO 292 SEQ ID NO: 293, SEQ ID NO: 294 SEQ ID NO 295 SEQ ID NO: 296 SEQ ID NO 297 SEQ ID NO: 298, SEQ ID NO: 299 SEQ ID NO 300 SEQ ID NO: 301 SEQ ID NO 302 SEQ ID NO: 303, SEQ ID NO: 304 SEQ ID NO 305 SEQ ID NO: 306 SEQ ID NO 307 SEQ ID NO: 308, SEQ ID NO: 309 SEQ ID NO 310 SEQ ID NO: 311 SEQ ID NO 312 SEQ ID NO: 313, SEQ ID NO: 314 SEQ ID NO 315 SEQ ID NO: 316 SEQ ID NO 317 SEQ ID NO: 318, SEQ ID NO: 319 SEQ ID NO: 320 SEQ ID NO: 321 SEQ ID NO 322 SEQ ID NO: 323, SEQ ID NO: 324 SEQ ID NO 325 SEQ ID NO: 326 SEQ ID NO 327 SEQ ID NO: 328, SEQ ID NO: 329 SEQ ID NO 330 SEQ ID NO: 331 SEQ ID NO 332 SEQ ID NO: 333, SEQ ID NO: 334 SEQ ID NO 335 SEQ ID NO: 336 SEQ ID NO 337 SEQ ID NO: 338, SEQ ID NO: 339 SEQ ID NO 340 SEQ ID NO: 341 SEQ ID NO 342 SEQ ID NO: 343, SEQ ID NO: 344, SEQ ID NO 345 SEQ ID NO: 346 SEQ ID NO 347 SEQ ID NO: 348, SEQ ID NO: 349 SEQ ID NO 350 SEQ ID NO: 351 SEQ ID NO 352 SEQ ID NO: 353, SEQ ID NO: 354 SEQ ID NO 355 SEQ ID NO: 356 SEQ ID NO 357 SEQ ID NO: 358, SEQ ID NO: 359 SEQ ID NO 360 SEQ ID NO: 361 SEQ ID NO 362 SEQ ID NO: 363, SEQ ID NO 364, SEQ ID NO 365, SEQ ID NO: 366 SEQ ID NO 367, SEQ ID NO: 368, SEQ ID NO 369, SEQ ID NO 370, SEQ ID NO: 371 SEQ ID NO 372, SEQ ID NO: 373, SEQ ID NO 374, SEQ ID NO 375, SEQ ID NO: 376 SEQ ID NO 377, SEQ ID NO: 378, SEQ ID NO 379, SEQ ID NO 380, SEQ ID NO: 381 SEQ ID NO 382, SEQ ID NO: 383, SEQ ID NO 384, SEQ ID NO 385, SEQ ID NO: 386 SEQ ID NO 387, SEQ ID NO: 388, SEQ ID NO 389, SEQ ID NO 390, SEQ ID NO: 391 SEQ ID NO 392, SEQ ID NO: 393, SEQ ID NO 394, SEQ ID NO 395, SEQ ID NO: 396 SEQ ID NO 397, SEQ ID NO: 398, SEQ ID NO 399, SEQ ID NO 400, SEQ ID NO: 401 SEQ ID NO 402, SEQ ID NO: 403, SEQ ID NO 404, SEQ ID NO 405, SEQ ID NO: 406 SEQ ID NO 407, SEQ ID NO: 408, SEQ ID NO 409, SEQ ID NO 410, SEQ ID NO: 411 SEQ ID NO 412, SEQ ID NO: 413, SEQ ID NO 414, SEQ ID NO 415, SEQ ID NO: 416 SEQ ID NO 417, SEQ ID NO: 418, SEQ ID NO 419, SEQ ID NO 420, SEQ ID NO: 421 SEQ ID NO 422, SEQ ID NO: 423, SEQ ID NO 424, SEQ ID NO 425, SEQ ID NO: 426 SEQ ID NO: 427, SEQ ID NO: 428, SEQ ID NO 429, SEQ ID NO 430, SEQ ID NO: 431 SEQ ID NO 432, SEQ ID NO: 433, SEQ ID NO 434, SEQ ID NO 435, SEQ ID NO: 436 SEQ ID NO 437, SEQ ID NO: 438, SEQ ID NO 439, SEQ ID NO 440, SEQ ID NO: 441 SEQ ID NO 442, SEQ ID NO: 443, SEQ ID NO 444, SEQ ID NO 445, SEQ ID NO: 446 SEQ ID NO 447, SEQ ID NO: 448, SEQ ID NO 449, SEQ ID NO 450, SEQ ID NO: 451 SEQ ID NO 452, SEQ ID NO: 453, SEQ ID NO 454, SEQ ID NO 455, SEQ ID NO: 456 SEQ ID NO 457, SEQ ID NO: 458, SEQ ID NO 459, SEQ ID NO 460, SEQ ID NO: 461 SEQ ID NO 462, SEQ ID NO: 463, SEQ ID NO 464, SEQ ID NO 465, SEQ ID NO: 466 SEQ ID NO 467, SEQ ID NO: 468, SEQ ID NO 469, SEQ ID NO 470, SEQ ID NO: 471 SEQ ID NO 472, SEQ ID NO: 473, SEQ ID NO 474, SEQ ID NO 475, SEQ ID NO: 476 SEQ ID NO 477, SEQ ID NO: 478, SEQ ID NO 479, SEQ ID NO 480, SEQ ID NO: 481 SEQ ID NO 482, SEQ ID NO: 483, SEQ ID NO 484, SEQ ID NO 485, SEQ ID NO: 486 SEQ ID NO 487, SEQ ID NO: 488, SEQ ID NO 489, SEQ ID NO 490, SEQ ID NO: 491 SEQ ID NO 492, SEQ ID NO: 493, SEQ ID NO 494, SEQ ID NO 495, SEQ ID NO: 496 SEQ ID NO 497, SEQ ID NO: 498, SEQ ID NO 499, SEQ ID NO 500, SEQ ID NO: 501 SEQ ID NO 502, SEQ ID NO: 503, SEQ ID NO 504, SEQ ID NO 505, SEQ ID NO: 506 SEQ ID NO 507, SEQ ID NO: 508, SEQ ID NO 509, SEQ ID NO 510, SEQ ID NO: 511 SEQ ID NO 512, SEQ ID NO: 513, SEQ ID NO 514, SEQ ID NO 515, SEQ ID NO: 516 SEQ ID NO 517, SEQ ID NO: 518, SEQ ID NO 519, SEQ ID NO 520, SEQ ID NO: 521 SEQ ID NO 522, SEQ ID NO: 523, SEQ ID NO 524, SEQ ID NO 525, SEQ ID NO: 526 SEQ ID NO 527, SEQ ID NO: 528, SEQ ID NO 529, SEQ ID NO 530, SEQ ID NO: 531 SEQ ID NO 532, SEQ ID NO: 533, SEQ ID NO 534, SEQ ID NO 535, SEQ ID NO: 536 SEQ ID NO 537, SEQ ID NO: 538, SEQ ID NO: 539 SEQ ID NO: 540 SEQ ID NO: 541 SEQ ID NO 542, SEQ ID NO: 543, SEQ ID NO: 544 SEQ ID NO: 545 SEQ ID NO: 546 SEQ ID NO 547, SEQ ID NO: 548, SEQ ID NO: 549, SEQ ID NO: 550 SEQ ID NO: 551 SEQ ID NO 552, SEQ ID NO: 553, SEQ ID NO: 554 SEQ ID NO: 555 SEQ ID NO: 556 SEQ ID NO 557, SEQ ID NO: 558, SEQ ID NO: 559, SEQ ID NO: 560 SEQ ID NO: 561 SEQ ID NO 562, SEQ ID NO: 563, SEQ ID NO: 564, SEQ ID NO: 565, SEQ ID NO: 566 SEQ ID NO 567, SEQ ID NO: 568, SEQ ID NO: 569 SEQ ID NO: 570 SEQ ID NO: 571 SEQ ID NO 572, SEQ ID NO: 573, SEQ ID NO: 574 SEQ ID NO: 575 SEQ ID NO: 576 SEQ ID NO 577, SEQ ID NO: 578, SEQ ID NO: 579, SEQ ID NO: 580 SEQ ID NO: 581 SEQ ID NO 582, SEQ ID NO: 583, SEQ ID NO: 584 SEQ ID NO: 585 SEQ ID NO: 586 SEQ ID NO 587, SEQ ID NO: 588, SEQ ID NO: 589 SEQ ID NO: 590 SEQ ID NO: 591 SEQ ID NO 592, SEQ ID NO: 593, SEQ ID NO: 594 SEQ ID NO: 595 SEQ ID NO: 596 SEQ ID NO 597, SEQ ID NO: 598, SEQ ID NO: 599 SEQ ID NO: 600 SEQ ID NO: 601 SEQ ID NO 602, SEQ ID NO: 603, SEQ ID NO: 604 SEQ ID NO: 605 SEQ ID NO: 606 SEQ ID NO 607, SEQ ID NO: 608, SEQ ID NO: 609 SEQ ID NO: 610 SEQ ID NO: 611 SEQ ID NO 612, SEQ ID NO: 613, SEQ ID NO: 614 SEQ ID NO: 615 SEQ ID NO: 616 SEQ ID NO 617, SEQ ID NO: 618, SEQ ID NO: 619 SEQ ID NO: 620 SEQ ID NO: 621 SEQ ID NO 622, SEQ ID NO: 623, SEQ ID NO: 624 SEQ ID NO: 625 SEQ ID NO: 626 SEQ ID NO 627, SEQ ID NO: 628, SEQ ID NO: 629 SEQ ID NO: 630 SEQ ID NO: 631 SEQ ID NO 632, SEQ ID NO: 633, SEQ ID NO: 634 SEQ ID NO: 635 SEQ ID NO: 636 SEQ ID NO 637, SEQ ID NO: 638, SEQ ID NO: 639 SEQ ID NO: 640 SEQ ID NO: 641 SEQ ID NO 642, SEQ ID NO: 643, SEQ ID NO: 644 SEQ ID NO: 645 SEQ ID NO: 646 SEQ ID NO 647, SEQ ID NO: 648, SEQ ID NO: 649 SEQ ID NO: 650 SEQ ID NO: 651 SEQ ID NO 652, SEQ ID NO: 653, SEQ ID NO: 654 SEQ ID NO: 655 SEQ ID NO: 656 SEQ ID NO 657, SEQ ID NO: 658, SEQ ID NO: 659 SEQ ID NO: 660 SEQ ID NO: 661 SEQ ID NO 662, SEQ ID NO: 663, SEQ ID NO: 664 SEQ ID NO: 665 SEQ ID NO: 666 SEQ ID NO 667, SEQ ID NO: 668, SEQ ID NO: 669, SEQ ID NO: 670 SEQ ID NO: 671 SEQ ID NO 672, SEQ ID NO: 673, SEQ ID NO: 674 SEQ ID NO: 675 SEQ ID NO: 676 SEQ ID NO 677, SEQ ID NO: 678, SEQ ID NO: 679 SEQ ID NO: 680 SEQ ID NO: 681 SEQ ID NO 682, SEQ ID NO: 683, SEQ ID NO: 684 SEQ ID NO: 685 SEQ ID NO: 686 SEQ ID NO 687, SEQ ID NO: 688, SEQ ID NO: 689 SEQ ID NO: 690 SEQ ID NO: 691 SEQ ID NO 692, SEQ ID NO: 693, SEQ ID NO: 694 SEQ ID NO: 695 SEQ ID NO: 696 SEQ ID NO 697, SEQ ID NO: 698, SEQ ID NO: 699 SEQ ID NO: 700 SEQ ID NO: 701 SEQ ID NO 702, SEQ ID NO: 703, SEQ ID NO: 704 SEQ ID NO: 705 SEQ ID NO: 706 SEQ ID NO 707, SEQ ID NO: 708, SEQ ID NO: 709 SEQ ID NO: 710 SEQ ID NO: 711 SEQ ID NO 712, SEQ ID NO: 713, SEQ ID NO: 714 SEQ ID NO 715, SEQ ID NO: 716 SEQ ID NO 717 SEQ ID NO: 718, SEQ ID NO: 719 SEQ ID NO 720, SEQ ID NO: 721 SEQ ID NO 722 SEQ ID NO: 723, SEQ ID NO: 724 SEQ ID NO 725, SEQ ID NO: 726 SEQ ID NO 727 SEQ ID NO: 728, SEQ ID NO: 729 SEQ ID NO 730, SEQ ID NO: 731 SEQ ID NO 732 SEQ ID NO: 733, SEQ ID NO: 734, SEQ ID NO 735, SEQ ID NO: 736 SEQ ID NO 737 SEQ ID NO: 738, SEQ ID NO: 739 SEQ ID NO 740, SEQ ID NO: 741 SEQ ID NO 742 SEQ ID NO: 743, SEQ ID NO: 744 SEQ ID NO 745, SEQ ID NO: 746 SEQ ID NO 747 SEQ ID NO: 748, SEQ ID NO: 749, SEQ ID NO 750, SEQ ID NO: 751 SEQ ID NO 752 SEQ ID NO: 753, SEQ ID NO: 754 SEQ ID NO 755, SEQ ID NO: 756 SEQ ID NO 757 SEQ ID NO: 758, SEQ ID NO: 759 SEQ ID NO 760, SEQ ID NO: 761 SEQ ID NO 762 SEQ ID NO: 763, SEQ ID NO: 764, SEQ ID NO 765, SEQ ID NO: 766 SEQ ID NO 767 SEQ ID NO: 768, SEQ ID NO: 769 SEQ ID NO 770, SEQ ID NO: 771 SEQ ID NO 772 SEQ ID NO: 773, SEQ ID NO: 774 SEQ ID NO 775, SEQ ID NO: 776 SEQ ID NO 777, SEQ ID NO: 778, SEQ ID NO: 779, SEQ ID NO 780, SEQ ID NO: 781 SEQ ID NO 782 SEQ ID NO: 783, SEQ ID NO: 784 SEQ ID NO 785, SEQ ID NO: 786 SEQ ID NO 787 SEQ ID NO: 788, SEQ ID NO: 789 SEQ ID NO 790, SEQ ID NO: 791 SEQ ID NO 792 SEQ ID NO: 793, SEQ ID NO: 794 SEQ ID NO 795, SEQ ID NO: 796 SEQ ID NO 797 SEQ ID NO: 798, SEQ ID NO: 799 SEQ ID NO 800, SEQ ID NO: 801 SEQ ID NO 802 SEQ ID NO: 803, SEQ ID NO: 804 SEQ ID NO 805, SEQ ID NO: 806 SEQ ID NO 807 SEQ ID NO: 808, SEQ ID NO: 809 SEQ ID NO 810, SEQ ID NO: 811 SEQ ID NO 812 SEQ ID NO: 813, SEQ ID NO: 814 SEQ ID NO 815, SEQ ID NO: 816 SEQ ID NO 817 SEQ ID NO: 818, SEQ ID NO: 819 SEQ ID NO 820, SEQ ID NO: 821 SEQ ID NO 822 SEQ ID NO: 823, SEQ ID NO: 824 SEQ ID NO 825, SEQ ID NO: 826 SEQ ID NO 827 SEQ ID NO: 828, SEQ ID NO: 829 SEQ ID NO 830, SEQ ID NO: 831 SEQ ID NO 832 SEQ ID NO: 833, SEQ ID NO: 834 SEQ ID NO 835, SEQ ID NO: 836 SEQ ID NO 837 SEQ ID NO: 838, SEQ ID NO: 839 SEQ ID NO 840, SEQ ID NO: 841 SEQ ID NO 842 SEQ ID NO: 843, SEQ ID NO: 844 SEQ ID NO 845, SEQ ID NO: 846 SEQ ID NO 847 SEQ ID NO: 848, SEQ ID NO: 849 SEQ ID NO 850, SEQ ID NO: 851 SEQ ID NO 852 SEQ ID NO: 853, SEQ ID NO: 854 SEQ ID NO 855, SEQ ID NO: 856 SEQ ID NO 857 SEQ ID NO: 858, SEQ ID NO: 859 SEQ ID NO 860, SEQ ID NO: 861 SEQ ID NO 862 SEQ ID NO: 863, SEQ ID NO: 864, SEQ ID NO 865, SEQ ID NO: 866 SEQ ID NO 867 SEQ ID NO: 868, SEQ ID NO: 869 SEQ ID NO 870, SEQ ID NO: 871 SEQ ID NO 872 SEQ ID NO: 873, SEQ ID NO: 874 SEQ ID NO 875, SEQ ID NO: 876 SEQ ID NO 877 SEQ ID NO: 878, SEQ ID NO: 879 SEQ ID NO 880, SEQ ID NO: 881 SEQ ID NO 882 SEQ ID NO: 883, SEQ ID NO: 884 SEQ ID NO 885, SEQ ID NO: 886 SEQ ID NO 887 SEQ ID NO: 888, SEQ ID NO 889, SEQ ID NO 890, SEQ ID NO: 891 SEQ ID NO: 892, SEQ ID NO: 893, SEQ ID NO 894, SEQ ID NO 895, SEQ ID NO: 896 SEQ ID NO: 897, SEQ ID NO: 898, SEQ ID NO 899, SEQ ID NO 900, SEQ ID NO: 901 SEQ ID NO: 902, SEQ ID NO: 903, SEQ ID NO 904, SEQ ID NO 905, SEQ ID NO: 906 SEQ ID NO: 907, SEQ ID NO: 908, SEQ ID NO 909, SEQ ID NO 910, SEQ ID NO: 911 SEQ ID NO: 912, SEQ ID NO: 913, SEQ ID NO 914, SEQ ID NO 915, SEQ ID NO: 916 SEQ ID NO: 917, SEQ ID NO: 918, SEQ ID NO 919, SEQ ID NO 920, SEQ ID NO: 921 SEQ ID NO: 922, SEQ ID NO: 923, SEQ ID NO 924, SEQ ID NO 925, SEQ ID NO: 926 SEQ ID NO: 927, SEQ ID NO: 928, SEQ ID NO 929, SEQ ID NO 930, SEQ ID NO: 931 SEQ ID NO: 932, SEQ ID NO: 933, SEQ ID NO 934, SEQ ID NO 935, SEQ ID NO: 936 SEQ ID NO: 937, SEQ ID NO: 938, SEQ ID NO 939, SEQ ID NO 940, SEQ ID NO: 941 SEQ ID NO: 942, SEQ ID NO: 943, SEQ ID NO 944, SEQ ID NO 945, SEQ ID NO: 946 SEQ ID NO: 947, SEQ ID NO: 948, SEQ ID NO 949, SEQ ID NO: 950, SEQ ID NO: 951 SEQ ID NO: 952, SEQ ID NO: 953, SEQ ID NO 954, SEQ ID NO 955, SEQ ID NO: 956 SEQ ID NO: 957, SEQ ID NO: 958, SEQ ID NO 959, SEQ ID NO 960, SEQ ID NO: 961 SEQ ID NO: 962, SEQ ID NO: 963, SEQ ID NO 964, SEQ ID NO 965, SEQ ID NO: 966 SEQ ID NO: 967, SEQ ID NO: 968, SEQ ID NO 969, SEQ ID NO 970, SEQ ID NO: 971 SEQ ID NO: 972, SEQ ID NO: 973, SEQ ID NO 974, SEQ ID NO 975, SEQ ID NO: 976 SEQ ID NO: 977, SEQ ID NO: 978, SEQ ID NO 979, SEQ ID NO 980, SEQ ID NO: 981 SEQ ID NO: 982, SEQ ID NO: 983, SEQ ID NO 984, SEQ ID NO 985, SEQ ID NO: 986 SEQ ID NO: 987, SEQ ID NO: 988, SEQ ID NO 989, SEQ ID NO 990, SEQ ID NO: 991 SEQ ID NO: 992, SEQ ID NO: 993, SEQ ID NO: 994, SEQ ID NO: 995, SEQ ID NO: 996, SEQ ID NO: 997, SEQ ID NO: 998, SEQ ID NO: 999, SEQ ID NO: 1000, SEQ ID NO: 1001, SEQ ID NO: 1002, SEQ ID NO: 1003, SEQ ID NO: 1004, SEQ ID NO: 1005, SEQ ID NO: 1006, SEQ ID NO: 1007, SEQ ID NO: 1008, SEQ ID NO: 1009, SEQ ID NO: 1010, SEQ ID NO: 1011, SEQ ID NO: 1012, SEQ ID NO: 1013, SEQ ID NO: 1014, SEQ ID NO: 1015, SEQ ID NO: 1016, SEQ ID NO: 1017, SEQ ID NO: 1018, SEQ ID NO: 1019, SEQ ID NO: 1020, SEQ ID NO: 1021, SEQ ID NO: 1022, SEQ ID NO: 1023, SEQ ID NO: 1024, SEQ ID NO: 1025, SEQ ID NO: 1026, SEQ ID NO: 1027, SEQ ID NO: 1028, SEQ ID NO: 1029, SEQ ID NO: 1030, SEQ ID NO: 1031 , SEQ ID NO: 1032, SEQ ID NO: 1033, SEQ ID NO: 1034, SEQ ID NO: 1035, SEQ ID NO: 1036, SEQ ID NO: 1037, SEQ ID NO: 1038, SEQ ID NO: 1039, SEQ ID NO: 1040, SEQ ID NO: 1041, SEQ ID NO: 1042, SEQ ID NO: 1043, SEQ ID NO: 1044, SEQ ID NO: 1045, SEQ ID NO: 1046, SEQ ID NO: 1047, SEQ ID NO: 1048, SEQ ID NO: 1049, SEQ ID NO: 1050, SEQ ID NO: 1051 , SEQ ID NO: 1052, SEQ ID NO: 1053, SEQ ID NO: 1054, SEQ ID NO: 1055, SEQ ID NO: 1056, SEQ ID NO: 1057, SEQ ID NO: 1058, SEQ ID NO: 1059, SEQ ID NO: 1060, SEQ ID NO: 1061, SEQ ID NO: 1062, SEQ ID NO: 1063, SEQ ID NO: 1064, SEQ ID NO: 1065, SEQ ID NO: 1066, SEQ ID NO: 1067, SEQ ID NO: 1068, SEQ ID NO: 1069, SEQ ID NO: 1070, SEQ ID NO: 1071, SEQ ID NO: 1072, SEQ ID NO: 1073, SEQ ID NO: 1074, SEQ ID NO: 1075, SEQ ID NO: 1076, SEQ ID NO: 1077, SEQ 5 ID NO: 1078, SEQ ID NO: 1079, SEQ ID NO: 1080, SEQ ID NO: 1081 , SEQ ID NO: 1082, SEQ ID NO: 1083, SEQ ID NO: 1084, SEQ ID NO: 1085, SEQ ID NO: 1086, SEQ ID NO: 1087, SEQ ID NO: 1088, SEQ ID NO: 1089, SEQ ID NO: 1090, SEQ ID NO: 1091, SEQ ID NO: 1092, SEQ ID NO: 1093, SEQ ID NO: 1094, SEQ ID NO: 1095, SEQ ID NO: 1096, SEQ ID NO: 1097, SEQ ID NO: 1098, SEQ ID NO: 1099, SEQ ID NO: 1100, SEQ ID NO: 10 1101, SEQ ID NO: 1102, SEQ ID NO: 1103, SEQ ID NO: 1104, SEQ ID NO: 1105, SEQ ID NO: 1106, SEQ ID NO: 1107, SEQ ID NO: 1108, SEQ ID NO: 1109, SEQ ID NO: 1110, SEQ ID NO: 1111, SEQ ID NO: 1112, SEQ ID NO: 1113, SEQ ID NO: 1114, SEQ ID NO: 1115, SEQ ID NO: 1116, SEQ ID NO: 1117, SEQ ID NO: 1118, SEQ ID NO: 1119, SEQ ID NO: 1120, SEQ ID NO: 1121 , SEQ ID NO: 1122, SEQ ID NO: 1123, SEQ ID NO: 1124, 15 SEQ ID NO: 1125, SEQ ID NO: 1126, SEQ ID NO: 1127, SEQ ID NO: 1128, SEQ ID NO: 1129, SEQ ID NO: 1130, SEQ ID NO: 1131, SEQ ID NO: 1132, SEQ ID NO: 1133, SEQ ID NO: 1134, SEQ ID NO: 1135, SEQ ID NO: 1136, SEQ ID NO: 1137, SEQ ID NO: 1138, SEQ ID NO: 1139, SEQ ID NO: 1140, SEQ ID NO: 1141, SEQ ID NO: 1142, SEQ ID NO: 1143, SEQ ID NO: 1144, SEQ ID NO: 1145, SEQ ID NO: 1146, SEQ ID NO: 1147, SEQ 20 ID NO: 1148, SEQ ID NO: 1149, SEQ ID NO: 1150, SEQ ID NO: 1151, SEQ ID NO: 1152, SEQ ID NO: 1153, SEQ ID NO: 1154, SEQ ID NO: 1155, SEQ ID NO: 1156, SEQ ID NO: 1157, SEQ ID NO: 1158, SEQ ID NO: 1159, SEQ ID NO: 1160, SEQ ID NO: 1161, SEQ ID NO: 1162, SEQ ID NO: 1163, SEQ ID NO: 1164, SEQ ID NO: 1165, SEQ ID NO: 1166, SEQ ID NO: 1167, SEQ ID NO: 1168, SEQ ID NO: 1169, SEQ ID NO: 1170, and SEQ ID 25 NO: 1171. In a more preferred embodiment, the invention relates to a CTL epitope with a global conservation of more than 1% and a weighted average of the MHC class I binding affinity for the query peptide of less than 100 nM. In a related embodiment, the invention relates 30 to a CTL epitope selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ ID NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ ID NO: 23, SEQ ID NO: 35 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ ID NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ ID NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41, SEQ ID NO: 42, SEQ ID NO: 43, SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ ID NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ ID NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ ID NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ ID NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ ID NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ ID NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ ID NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQ ID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 92, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, SEQ ID NO: 102, SEQ ID NO 103, SEQ ID NO 104, SEQ ID NO 105, SEQ ID NO 106 SEQ ID NO 107, SEQ ID NO 108, SEQ ID NO 109, SEQ ID NO 110, SEQ ID NO 111 SEQ ID NO 112, SEQ ID NO 113, SEQ ID NO 114, SEQ ID NO 115, SEQ ID NO 116 SEQ ID NO 117, SEQ ID NO 118, SEQ ID NO 119, SEQ ID NO 120, SEQ ID NO 121 SEQ ID NO 122, SEQ ID NO 123, SEQ ID NO 124, SEQ ID NO 125, SEQ ID NO 126 SEQ ID NO 127, SEQ ID NO 128, SEQ ID NO 129, SEQ ID NO 130, SEQ ID NO 131 SEQ ID NO 132, SEQ ID NO 133, SEQ ID NO 134, SEQ ID NO 135, SEQ ID NO 136 SEQ ID NO 137, SEQ ID NO 138, SEQ ID NO 139, SEQ ID NO 140, SEQ ID NO 141 SEQ ID NO 142, SEQ ID NO 143, SEQ ID NO 144, SEQ ID NO 145, SEQ ID NO 146 SEQ ID NO 147, SEQ ID NO 148, SEQ ID NO 149, SEQ ID NO 150, SEQ ID NO 151 SEQ ID NO 152, SEQ ID NO 153, SEQ ID NO 154, SEQ ID NO 155, SEQ ID NO 156 SEQ ID NO 157, SEQ ID NO 158, SEQ ID NO 159, SEQ ID NO 160, SEQ ID NO 161 SEQ ID NO 162, SEQ ID NO 163, SEQ ID NO 164, SEQ ID NO 165, SEQ ID NO 166 SEQ ID NO 167, SEQ ID NO 168, SEQ ID NO 169, SEQ ID NO 170, SEQ ID NO 171 SEQ ID NO 172, SEQ ID NO 173, SEQ ID NO 174, SEQ ID NO 175, SEQ ID NO 176 SEQ ID NO 177, SEQ ID NO 178, SEQ ID NO 179, SEQ ID NO 180, SEQ ID NO 181 SEQ ID NO 182, SEQ ID NO 183, SEQ ID NO 184, SEQ ID NO 185, SEQ ID NO 186 SEQ ID NO 187, SEQ ID NO 188, SEQ ID NO 189, SEQ ID NO 190, SEQ ID NO 191 SEQ ID NO 192, SEQ ID NO 193, SEQ ID NO 194, SEQ ID NO 195, SEQ ID NO 196 SEQ ID NO 197, SEQ ID NO 198, SEQ ID NO 199, SEQ ID NO: 200, SEQ ID NO 201 SEQ ID NO 202, SEQ ID NO 203, SEQ ID NO 204, SEQ ID NO 205, SEQ ID NO 206 SEQ ID NO 207, SEQ ID NO: 208, SEQ ID NO: 209, SEQ ID NO: 210, SEQ ID NO: 211, SEQ ID NO: 212, and SEQ ID NO: 213. In another aspect, the invention relates to a CTL epitope wherein the anchor amino acids have been modified In a related aspect the invention relates to CTL epitopes selected from the group consisting of SEQ ID NO: 214, SEQ ID NO: 215, SEQ ID NO: 216, SEQ ID NO: 217, SEQ ID NO: 218, SEQ ID NO: 219, SEQ ID NO: 220, SEQ ID NO: 221, SEQ ID NO: 222, SEQ ID NO: 223, SEQ ID NO: 224, SEQ ID NO: 225 SEQID NO: 226 SEQID NO: 227, SEQ ID NO: 228, SEQ ID NO: 229, SEQ ID NO: 230 SEQID NO: 231 SEQID NO: 232, SEQ ID NO: 233, SEQ ID NO: 234, SEQ ID NO: 235 SEQID NO: 236 SEQID NO: 237, SEQ ID NO: 238, SEQ ID NO: 239, SEQ ID NO: 240 SEQID NO: 241 SEQID NO: 242, SEQ ID NO: 243, SEQ ID NO: 244, SEQ ID NO: 245 SEQID NO: 246 SEQID NO: 247, SEQ ID NO: 248, SEQ ID NO: 249, SEQ ID NO: 250 SEQID NO: 251 SEQID NO: 252, SEQ ID NO: 253, SEQ ID NO: 254, SEQ ID NO: 255 SEQID NO: 256 SEQID NO: 257, SEQ ID NO: 258, SEQ ID NO: 259, SEQ ID NO: 260 SEQID NO: 261 SEQID NO: 262, SEQ ID NO: 263, SEQ ID NO: 264, SEQ ID NO: 265 SEQID NO: 266 SEQID NO: 267, SEQ ID NO: 268, SEQ ID NO: 269, SEQ ID NO: 270 SEQID NO: 271 SEQID NO: 272, SEQ ID NO: 273, SEQ ID NO: 274, SEQ ID NO: 275 SEQID NO: 276 SEQID NO: 277, SEQ ID NO: 278, SEQ ID NO: 279, SEQ ID NO: 280 SEQID NO: 281 SEQID NO: 282, SEQ ID NO: 283, SEQ ID NO: 284, SEQ ID NO: 285 SEQID NO: 286 SEQID NO: 287, SEQ ID NO: 288, SEQ ID NO: 289, SEQ ID NO: 290 SEQID NO: 291 SEQID NO: 292, SEQ ID NO: 293, SEQ ID NO: 294, SEQ ID NO: 295 SEQID NO: 296 SEQID NO: 297, SEQ ID NO: 298, SEQ ID NO: 299, SEQ ID NO: 300 SEQID NO: 301 SEQID NO: 302, SEQ ID NO: 303, SEQ ID NO: 304, SEQ ID NO: 305 SEQID NO: 306 SEQID NO: 307, SEQ ID NO: 308, SEQ ID NO: 309, SEQ ID NO: 310 SEQID NO: 311 SEQID NO: 312, SEQ ID NO: 313, SEQ ID NO: 314, SEQ ID NO: 315 SEQID NO: 316 SEQID NO: 317, SEQ ID NO: 318, SEQ ID NO: 319, SEQ ID NO: 320 SEQID NO: 321 SEQID NO: 322, SEQ ID NO: 323, SEQ ID NO: 324, SEQ ID NO: 325 SEQID NO: 326 SEQID NO: 327, SEQ ID NO: 328, SEQ ID NO: 329, SEQ ID NO: 330 SEQID NO: 331 SEQID NO: 332, SEQ ID NO: 333, SEQ ID NO: 334, SEQ ID NO: 335 SEQID NO: 336 SEQID NO: 337, SEQ ID NO: 338, SEQ ID NO: 339, SEQ ID NO: 340 SEQID NO: 341 SEQID NO: 342, SEQ ID NO: 343, SEQ ID NO: 344, SEQ ID NO: 345 SEQID NO: 346 SEQID NO: 347, SEQ ID NO: 348, SEQ ID NO: 349, SEQ ID NO: 350 SEQID NO: 351 SEQID NO: 352, SEQ ID NO: 353, SEQ ID NO: 354, SEQ ID NO: 355 SEQID NO: 356 SEQID NO: 357, SEQ ID NO: 358, and SEQ ID NO: 359. It should be noted, that the following natural HLA-A2 restricted CTL epitopes are disclaimed: LVGPTPVNI, SLYNTVATL, FLQSRPEPT, and KLTPLCVTL. To meet the high diversity of HIV, a preferred therapeutic or prophylactic vaccine shall contain multiple CTL epitopes. An example of a preferred vaccine will be embodied by DNA vaccines that induce CTL because of the intracellular production of the vaccine gene product (that is the CTL epitope encoded by the DNA vaccine) and thus render MHC-I presentation. Another example would contain subdominant epitopes that together represent a great global and/or intra-subtype conservation and therefore would be preferred instead of immune dominant epitopes in variable areas of the HIV. Many epitopes are known so far (e.g. the databases: http://wehih.wehi.edu.au/mhcpep/ and http://hiv-web.Ianl.gov/immuno/index.html); however, many of them may be the immune dominant epitopes that may be immune escaped or represent epitopes in only a limited numbers of (primary, clinical) HIV strains. Thus, one embodiment of the invention relates to a polytope comprising at least one epitope as described above. For the reasons stated above relating to the phylogenetic studies of the HIV-1 viruses, "tomorrow's" HIV vaccines should be designed to elicit broad immune responses against all HIV-1 genetic groups. One important aspect, after the identification of a CTL epitope, is the testing of said epitope. A wide range of tests will be evident to the person skilled in the art. In one embodiment of the invention, the first test is performed by synthesis of the epitope and by measurement of the actual binding to HLA-A2. The second test is performed by synthesis of the epitope and testing for its ability to induce an immune response. With the second test, a variety of tests are available. One example is immunising HLA-A2 transgenic mice to obtain a positive CTL response as determined by >10% lysis of target cells at E:T ratio 50:1 in a Cr-release assay with peptide pulsed cells or with transfected or infected target cells (as illustrated in detail in example 7). Another example with the second test is administration of a nucleotide sequence encoding the epitope to a cell system, e.g. a mammal, under conditions allowing expression of the nucleotide sequence product, followed by e.g. the Cr-release assay. The third test includes adverse effects, e.g. toxicology, of the epitope as will be obvious to the person skilled in the art. When the term nucleotide is used in the present application, it should be understood in the broadest sense. That is, most often the nucleotide should be considered as DNA. However, the term nucleotide can also be considered as RNA, including RNA embodiments which will be apparent for the person skilled in the art. Furthermore, the term nucleotide should also be considered as synthetic DNA or RNA analogues such as PNA and LNA. Hence, the invention also relates to a vaccine comprising a nucleic acid fragment encoding the CTL epitope, the vaccine effecting in vivo expression of antigen by an animal, including a human being, to whom the vaccine has been administered, the amount of expressed antigen being effective to confer substantially increased resistance to infections with HIV in an animal, including a human being. The efficacy of such a "DNA vaccine" can possibly be enhanced by administering the gene encoding the expression product built into a vaccine-relevant microorganism, such as e.g. vaccinia virus A. (MVA), adenovirus, simikiforest virus, or such as a bacterium e.g. lactococcus, salmonella or E.coli species, together with a DNA fragment encoding a polypeptide which has the capability of modulating an immune response. For instance, a gene encoding T-helper epitopes or lymphokine precursors or lymphokines (e.g. IFN-γ, IL- 2, or IL-12) could be administered together with the gene encoding the CTL epitope, either by administering two separate DNA fragments or by administering both DNA fragments included in the same vector. It is furthermore possible to include immunstimulatory CpG motifs in the nucleotide sequence to help inducing and/or enhancing the immunresponse to the CTL epitope. It also is a possibility to administer DNA fragments comprising a multitude of nucleotide sequences which each encode relevant CTL epitopes disclosed herein so as to effect a continuous sensitisation of the immune system with a broad spectrum of these epitopes. In one embodiment of the invention, any of the above mentioned CTL epitopes are used in the manufacture of an immunogenic composition to be used for induction of an immune response in a mammal against an infection with HIV, or to combat an ongoing HIV infection. Preferably, the immunogenic composition is used as a vaccine. The preparation of vaccines which contain peptide sequences, e.g. CTL epitopes, as active ingredients is generally well understood in the art, as exemplified by U.S. Patents 4,608,251; 4,601,903; 4,599,231 and 4,599,230, all incorporated herein by reference. Typically, such vaccines are prepared as injectables either as liquid solutions or suspen- sions; solid forms suitable for solution in liquid or suspension in liquid prior to injection may also be prepared. The preparation may also be emulsified. The active immunogenic ingredient is often mixed with excipients which are pharmaceutically acceptable and compatible with the active ingredient. Suitable excipients are, for example, water, saline, dextrose, glycose, glycerol, ethanol, or the like, and combinations thereof. In addition, if desired, the vaccine may contain minor amounts of auxiliary substances such as wetting or emulsifying agents, pH buffering agents, bupivacain, or adjuvants which enhance the effectiveness of the vaccines. The vaccines are conventionally administered parenterally, by injection, for example either subcutaneously or intramuscularly. Additional formulations which are suitable for other modes of administration include suppositories and, in some cases, oral formulations. For suppositories, traditional binders and carriers may include, for example, polyalkalene glycols or triglycerides; such suppositories may be formed from mixtures containing the active ingredient in the range of 0.5% to 10%, preferably 1-2%. Oral formulations include such normally employed excipients as, for example, pharmaceutical grades of mannitol, lactose, starch, magnesium stearate, sodium saccharine, cellulose, magnesium carbonate, and the like. These compositions take the form of solutions, suspensions, tablets, pills, capsules, sustained release formulations or powders and contain 10-95% of active ingredient, preferably 25-70%. The CTL epitope or epitopes may be formulated into the vaccine as neutral or salt forms. Pharmaceutically acceptable salts include acid addition salts (formed with the free amino groups of the peptide) and acid addition salts that are formed with inorganic acids such as, for example, hydrochloric or phosphoric acids, or such organic acids as acetic, oxalic, tartaric, mandelic, and the like. Salts formed with the free carboxyl groups may also be derived from inorganic bases such as, for example, sodium, potassium, ammonium, calcium, or ferric hydroxides, and such organic bases as isopropylamine, trimethylamine, 2-ethylamino ethanol, histidine, procaine, and the like. The vaccines are administered in a manner compatible with the dosage formulation, and in such an amount as will be therapeutically effective and immunogenic. The quantity to be administered depends on the subject to be treated, including, e.g., the capacity of the individual's immune system to mount an immune response, and the degree of protection desired. Suitable dosage ranges are of the order of several hundred micrograms of active ingredient per vaccination with a preferred range from about 0.1 μg to 1000 μg, such as in the range from about 1 μg to 300 μg, and especially in the range from about 10 μg to 50 μg. Suitable regimes for initial administration and booster shots are also variable but are typified by an initial administration followed by subsequent inoculations or other administrations. The manner of application may be varied widely. Any of the conventional methods for administration of a vaccine are applicable. Preferred routes of administration are the parenteral route such as the intravenous, intraperitoneal, intramuscular, subcutaneous, intracutaneous or intradermal routes; the oral (on a solid physiologically acceptable base or in a physiologically acceptable dispersion), buccal, sublingual, nasal, rectal or transdermal routes. The dosage of the vaccine will depend on the route of administration and will vary according to the age of the person to be vaccinated and, to a lesser degree, the weight of the person to be vaccinated. Some of the CTL epitopes are sufficiently immunogenic in a vaccine, but for some of the others, the immune response will be enhanced if the vaccine further comprises an adjuvant substance. Various methods of achieving adjuvant effect for the vaccine include use of agents such as aluminum hydroxide or phosphate (alum), commonly used as a 0.05 to 0.1 percent solution in phosphate buffered saline, admixture with synthetic polymers of sugars (Carbopol) used as a 0.25 percent solution, aggregation of the CTL epitope in the vaccine by heat treatment with temperatures ranging between 70° to 101°C for 30 second to 2 minute periods respectively. Aggregation by reactivating with pepsin treated (Fab) antibodies to albumin, mixture with bacterial cells such as C. parvum or endotoxins or lipopolysaccharide or lipid A components of gram-negative bacteria, emulsion in physiologically acceptable oil vehicles such as mannide mono-oleate (Aracel A) or emulsion with 20 percent solution of a perfluorocarbon (Fluosol-DA) used as a block substitute may also be employed. According to the invention, DDA (dimethyldi- octadecylammonium bromide) is an interesting candidate for an adjuvant, but also Freund's complete and incomplete adjuvants as well as QuilA, Quil A derivates and RIBI adjuvants are interesting possibilities. Other possibilities to enhance the immunogenic effect involve the use of immune . modulating substances such as lymphokines (e.g. IFN-γ, IL-2 and IL-12) or synthetic IFN-γ inducers such as poly l:C in combination with the above-mentioned adjuvants. In many instances, it will be necessary to have multiple administrations of the vaccine, usually not exceeding six vaccinations, more usually not exceeding four vaccinations and, usually at least about three vaccinations, preferably one or two vaccinations. The vaccinations will normally be at from two to twelve week intervals, more usually from three to five week intervals. Periodic boosters at intervals of 1-25 years, such as 20 years, preferably 15 or 10 years, more preferably 1-5 years, usually three years, will be desirable to maintain the desired levels of protective immunity. Thus, the invention also relates to the use of a CTL epitope for the manufacture of a vaccine and the use of a nucleotide sequence encoding a CTL epitope for the manufacture of a vaccine. In a related embodiment, the invention relates to a method of treating, in a person in need thereof, an HIV infection by administration of a vaccine, or an immunogenic composition as described in detail above to the person. In a preferred embodiment of the invention, the CTL epitope is a HIV virus epitope and the vaccine is against HIV virus. A relevant HIV vaccine could potentially be used not only as a prophylactic vaccine but also as a therapeutic vaccine in HIV infected patients e.g. during antiviral therapy. An HIV specific vaccine would have the possibility to induce or re- induce the wanted specific immunity and help the antiviral therapy in limiting or even eliminating the HIV infection. In HIV positive patients, the therapeutic vaccine may postpone the need for antiviral therapy or limit the HIV infection in patients during antiviral therapy if the virus develops resistance to the antiviral therapy. In a preferred embodiment of the invention, a clinically relevant A2-specific CTL epitope is used for manufacturing a vaccine. In this context, clinically a relevant A2-specific epitope is a natural or improved CTL epitope that fulfils the following criteria: - has a weighted average of HLA-A2 binding affinity of less than 100 nM; - is able to elicit a CTL immune response in HLA-A2 transgenic mice as determined by >10% lysis of target cells at E:T ratio 50:1 in a Cr-release assay with peptide pulsed cells or with transfected or infected target cells; - is able to elicit a CTL immune response in HLA-A2 transgenic mice as determined by >10% lysis of target cells at E:T ratio 50:1 in a Cr-release assay with cells presenting the native CTL epitopes from which the improved vaccine CTL-epitope was derived and - being more than 10% conserved within at least one HIV-1 genetic subtype or being more than 8% conserved among several genetic subtypes within a HIV-1 genetic group. In a preferred embodiment, the clinically relevant CTL epitope is conserved among HIV-1 of group M, O or N, most preferably group M virus. In a related embodiment the CTL epitope is conserved among HIV-1 subtype A, B, C, D, E, F, G, H, I, J, or K, most preferably subtype B virus. Examples Example 1: Peptide binding Cells: An EBV transformed cell line, RML, were used from the HLA-A * 0204 production. MHC purification: Cells were lysed with detergent and MHC class I molecules were affinity purified as previously described (Buus et al., 1995). The monoclonal antibody used for affinity purifications was BB7.2. Human β2-microglobulin was obtained from the urine of uraemic patients and purified to homogeneity by gelfiltration and chromatofoccusing, or purchased from Sigma. Peptide synthesis: Peptides were synthesised on a parallel multiple column peptide synthesiser (Holm et al., 89) on a PepSyn KA resin with the first amino acid attached and using the FMOC-protection strategy. After completion of all coupling according to the sequence, the protection groups were removed, and the peptides cleaved off the resin with 95% aqueous trifluoro acetic acid for 2 hours. The peptides were precipitated with ether and ether/ethylacetate, and lyophilised. The peptides were analysed by reversed- phase high performance liquid chromatography (HPLC), and by mass spectroscopy (MS). Peptide radiolabelling: 2-1 Oμg peptide was labelled with 1mCi 125 lodine (Amersham, Sweden) using chloramine T as previously described (Buus et al., 1986). The specific activity was measured to 1-5 x 10 8 cpm/mg peptide. Peptide-MHC class I binding assay: In general, affinity purified MHC class I molecules at 2-10 μM were incubated at 18°C for 48 h with approximately 10-100 nM radiolabelled peptide in a reaction mixture containing a protease inhibitor cocktail as previously described (Buus et al., 1986). The final detergent concentration in the reaction mixture was 0.05% NP-40 (Sigma). Peptide-MHC class complexes were separated (in duplicate) from free peptide by gel filtration using G25 spun columns (Buus et al., 1995).The radioactivity of the excluded "void" volume and of the included volume were measured by gamma spectrometry (Packard). The fraction of peptide bound to MHC class I relative to the total amount of offered peptide was calculated and corrected by subtracting the fraction of peptide (usually less than 1%) which appeared in the void volume in the absence of MHC. The spun columns and the previously reported Sephadex G50 column chromatography assay gave similar results (Buus et al., 1995). The recovery of complexes and the exclusion of ligand were 95-100%. Binding data are illustrated in table 11. Example 2: Neural Networks A data driven neural network algorithm was used, with network architectures comprising an input layer encoding the sequence information, one layer of hidden units, and a single output unit representing the floating point value for the binding affinity (normalized to the interval between zero and one). During training on the data consisting of pairs of sequence fragments and the associated binding affinity, the weights were adjusted essentially according to the method described by Rumelhart et al. (1986). Hence, each neuron (unit) besides those in the input layer, calculates a weighted sum of its inputs and passes this sum through a sigmoidal function to produce the output = σ(∑w n I„ -t) »=ι where N is the number of neurons in a layer, l n is the nth input to the neuron and w n is the weight of this input, σ is the sigmoidal function σ(x) = 1 + exp(-x) and t is its threshold. The training algorithm was of the gradient descent type, where the adjustable network weights iteratively are modified in order to make the network produce the correct output upon presentations of the sequence fragments. When training the networks we used a slightly more powerful error function suggested by McClelland instead of the conventional error function This logaritmic error function reduced the convergence time considerably, and has also the property of making a given network learn more complex tasks (compared to the standard error measure) without increasing the network size. The network was trained in experimentally determined relations between amino acid sequence and the experimentally determined binding affinity. The purpose of the training is to adjust the weights and thresholds in the network to quantitatively minimize the error between the prediction and the experimentally determined binding affinity. As the experimental data typically is not evenly distributed over a given range of binding affinities, a balancing scheme was designed with the purpose of avoiding that the network only would obtain sufficient prediction accuracy in the most populated part of the range. Typically, the most populated part of the range is "non-binders" which can be defined quantitatively using a specific threshold. The output interval (zero to one) was divided into a number of bins, and for each bin, the number of examples found was calculated. In the balancing training scheme, the examples were selected for presentation to the neural network such that all intervals were represented equally often. Specifically, the examples from the least populated bin were always selected in a training epoch, while randomly selected examples from the other more populated bins were skipped, such that, on average, all bins were represented equally often. Each amino acid in the sequence fragments was represented as a binary string of 20 bits thus using 20 input neurons for each residue. Alanine and cysteine were, for example, represented as the strings 10000000000000000000 and 01000000000000000000, respectively. The output was a single real value representing the affinity renormalized using various forms of logarithmic scaling. To find the best network, the performance of different network architectures (different numbers of units in the input (8, 9, or 10-mers) and in the hidden layer) were tested by training using part of the data as described above and testing the performance of the network on the remaining sequence fragments, using the classification correlation coefficient C (Mathews 1975, Brunak 1991) as quality measure as well as the standard Pearson correlation measure. For example, the classification coefficient is defined as follows (when a specific threshold separating binders and non-binders has been defined), P x N x -N Jx P fx J(N X +N fi )(N x +P fe )(P x +N fa )(P x +P fa ) In this equation P x is the number of true positives (experimentally determined binders, predicted binders), N x the number of true negatives (experimentally non-binders, predicted non-binders), P^ the number of false positives (experimentally non-binders, predicted binders), and j* the number of false negatives (experimentally binders, predicted non-binders). It is common practice to obtain the final prediction for a query fragment by averaging over the prediction from networks of different architectures, where the networks possibly have been trained on different parts of the data available. Here, the test performances have been calculated by cross-validation: The data set was divided into seven approximately equal-sized parts, and then every network run was carried out with one part as test data and the other six parts as training data. The performance measures were then calculated as an average over the seven different data set divisions (the output from some of the networks may be renormalized before averaging). The number of cross- validation networks is typically set by the amount of data available (if the data is sparse, it makes little sense to train hundreds of networks), and the amount of computer time one wants to spend. In this case, seven networks were chosen because it gave a resonable size of the training and test sets. For four out of four peptide-MHC class I combinations examined, the artificial neural networks performed better than the matrix-driven prediction. The predictions had been generated in a fashion predicting the actual binding IC 50 value rather than an arbitrary classification into binders vs. non-binders. Indeed, it had been possible to predict binders over a large range leading to the identification of high affinity binders as well as binders of lower affinity and non-binders. For the purpose of the invention, the fidelity of the HLA- A*0204 prediction is most important. 316 8-mer and 398 9-mer peptides were synthesised and tested for binding. Each of the 7 artificial neural networks were trained on 6/7 of the peptides leaving the remaining 1/7 for test. Each of the 7 artificial neural networks used a different 1/7 of the peptides in a cross-validation scheme. Compiling the average predicted vs. the observed values (after a logarithmic transformation) allowed a linear regression analysis which (without correction) yielded a line close to the expected y = x (for the 9 mers the line was log (obs) = log (pred) x 0.9734 + 0.1807 with a Pearson coefficient of 0.87). Analysing the same data set with alternative predictions gave the following Pearson coefficients: 0,78 (Rammensee http://134.2.96.221/scripts/hlaserver.dll/home.htm), 0,83 (Parker, http://bimas.dcrt.nih.gov/molbio/hla_bind/) and 0,85 (Stryhn et al., 1996). Even a first generation artificial neural network is better than any of the alternative prediction methods. The final network ensemble thus consists of 7 artificial neural networks, where the prediction on query peptides results from an average over the 7 output values. Example 3: Selection Criteria The HIV-1 protein sequences for the query epitope were obtained from The Los Alamos 1998/1999 HIV sequences database (http://hiv-web.lanl.gov/). These proteins are the products of translation of all available full-length sequenced HIV-1 proteins and reflect the principal genetic diversity of HIV-1 (Reference Sequences Representing the Principal Genetic Diversity of HIV-1 in the Pandemic available at http://hiv-web.lanl.gov/ ). All available Gag (96 seq.), Pol (86 seq.), Vif (265 seq.), Vpr (173 seq.), Vpu (156 seq.), Tat (101 seq.), Rev (105 seq.), Env (213 seq.) and Nef (251 seq.) protein sequences were extracted from the HIV-1 /SIVcpz protein alignments. All residual gaps that were inserted in the sequences for alignment purposes were removed. Amino acid sequences from Los Alamos HIV database (containing also all gene bank HIV sequences) were used and only complete sequences of each protein were selected. Table 7 summarises the number of HIV-1 protein sequences from which HLA-A2 epitopes were predicted and their distribution within the genetic subtypes composing group M (subtypes A, AB, AC, AD, ADI, AE, AG, AGI, AGJ, B, BF, C, CD, D, F, G, H, J) or within the groups N or group O. HIV-1-related sequences such as SIVcpz were included since SIVcpz viruses share a high genetic homology with HIV-1 group N in Env. All proteins were individually scanned for peptides of 9 or 8 residues length that displayed HLA-A2 binding motifs using the trained artificial neural network (as described above). The cut-off for the maximum value of the predicted peptide-HLA-A2 binding affinity was set at 500nM. A total of 4215 HLA-A2 binding motifs with IC 50 < 500nM and with a global HIV-I conservation >0% were found after the scanning of all HIV proteins (see table 2). Example 4: Characterisation of the predicted epitopes Clinically relevant HLA-A2 epitopes to be included in a vaccine should be able to potentially elicit an immune response against either most of the viruses of a same HIV-1 subtype or against viruses belonging to more than one HIV-1 genetic subtype or against viruses belonging to different HIV-1 genetic groups. Table 8 shows that the HIV-1 subtype B sequences often dominated in the database that was used for predicting HLA-A2 epitopes. For example, 66,5% of the Nef proteins that were used for predicting Nef-specific HLA-A2 epitopes belonged to the HIV-1 subtype B. This bias renders difficulty for the evaluation of whether or not a single epitope will cover different HIV-1 subtypes or will represent only HIV-1 subtype B. During a first attempt to select the most clinically relevant HLA-A2 epitopes, the global conservation cut-off was placed at 50%. Later it was observed, that the epitopes were counterselected that were found in HIV-1 subtypes other than subtype B (but not in subtype B). These subtypes were less represented in the database. For example, an epitope predicted in Nef with a global conservation of 10,35% could in fact be present in all proteins within subtype C (7,97%), G (1 ,19%) and H (1,19%). In order to take this bias of the database into account in the selection criteria, all epitopes with a global conservation of more than 8% were analysed. This cut-off allowed for picking up epitopes that are conserved among minor subtypes. It was identified which HIV-1 subtypes were related to each epitope that harboured at least a global conservation of 8%. According to the database nomenclature, all HIV-1 sequences are named according to the genetic subtypes they belong. Thus, the first letter appearing in their names corresponds to the subtype. The names of the protein sequences that harbour 100% amino acid sequence homology with each epitope were sorted and it was counted how many times any of the letters A, AB, AC, AD, ADI, AE, AG, AGI, AGJ, B, BF, C, CD, D, F, G, H, J or O or N appeared in the names. Positioning of each epitope within the reference HIV-1 strain HXB2. In order to better evaluate whether some epitopes could be variants of each other's, the epitopes within the 9 proteins of the HXB2 reference strain were mapped. This positioning was performed by locating the region of HXB2 that harboured the best homology with each epitope analysed. Each epitope was aligned with the corresponding HXB2 protein using classical pairwise homology search. Ranking the predicted epitopes by their binding affinity for the molecule HLA-A2 Peptide epitopes with a binding affinity of IC 5 o < 50nM are generally recognised as good binders towards HLA-A2, whereas peptide epitopes with a IC 50 binding affinity between 50nM and 500nM are considered intermediate binders. Good binders will harbour a good capacity in eliciting a CTL response whereas intermediate binders will not elicit such a good CTL response,. On the other hand, they provide good targets, since infected cells in the host can present these CTL epitopes that can function as a target without necessarily being able to induce CTL immunity. However, the neural network could mispredict the binding affinity by a factor 2. Thus, in order to be inclusive, the IC 50 affinity threshold that defined HLA-A2 good binders was raised to 100nM and such HLA-A2 epitopes were considered as natural immunogenic. All HLA-A2 epitopes with a binding affinity IC 50 < 100nM and with more than 1% of global conservation were sorted separately. They were considered natural immunogenic HIV-1 HLA-A2 epitopes. These peptide epitopes are listed in table 5A. Table 5A contains 213 HLA-A2 epitopes. They are grouped by protein family and ranked according to their length (9 or 8 amino acid residues) and to their global conservation. All predicted HLA-A2 epitopes with a binding affinity IC 50 between 50nM and 500nM and with a global conservation above 8% were sorted and considered natural HLA-A2 intermediate binders with potentially low capacity to elicit a good CTL response. Those intermediate binders were regarded as low immunogenic but antigenic. All predicted HLA-A2 epitopes with a binding affinity encomprised between 50nM and 500nM and with a global conservation above or equal to 8% were sorted separately in tables 5B and 5C. 158 (110 9-mers (table 5B) and 48 8-mers (table 5C)) HLA-A2 natural epitopes were found. These 158 HLA-A2 epitopes were regarded as natural intermediate binders whose binding affinity could be increased to obtain an improved binding affinity of IC 50 below 100nM by modifying one or two of the primary anchor positions. The improved epitopes, and their predicted binding, is listed in tables 5B and 5C. In a few cases an improved epitope will cover more than one natural epitope. Thus table 5B lists 100 improved epitopes, and table 5C lists 46 improved epitopes. The selection criteria for "good and intermediate HLA-A2 binders" are listed in table 2. Table 2. Criteria for selection of predicted 8+9 mer HIV HLA-A2 CTL epitopes for vaccines Table IC 5 0 binding affinity global HIV conservation # epitopes immunogenic characterisation < 500 nM > 0% 4215 all possible, natural 5A ≤ IOO nM > 1% 213 all immunogenic, natural 5B, 5C 50-500 nM > 8% 158 low immunogenic but antigenic with possibility of optimising immunogenicity by designing best anchor residues 5D 50-500 nM > 8% 812 immunogenic relevant, new designed 5E 50-500 nM >8% 32 low immunogenic that cannot be improved by anchor exchange Improvement prediction from natural intermediate binders It was evaluated, whether or not the binding affinity of each of the best 146 intermediate binders (table 5B) could be improved by exchanging one or two of the primary anchor residues. For that purpose, each peptide sequence was designed by permutating the primary anchor residues with each of the amino acids described above. All combinations were analysed. For a natural intermediate binder whose sequence is XLXXXXXXA and binding affinity IC 50 >100nM, 15 epitopes were artificially designed, as seen in table 4: Table 4 XLXXXXXXV XMXXXXXXV XQXXXXXXV XIXXXXXXV XLXXXXXXI XMXXXXXXl XQXXXXXXI XIXXXXXXI XLXXXXXXL XMXXXXXXL XQXXXXXXL XIXXXXXXL XMXXXXXXA XQXXXXXXA XIXXXXXXA The HLA-A2 binding affinity of these 15 designed HLA-A2 epitopes were then predicted using the artificial neural networks described in the present invention. The affinity threshold of the predicted binding was set at 100nM in order to obtain immunogenicity. All designed epitopes with a predicted binding affinity IC 50 below 100nM and with an IC 50 value lower than the IC 50 value of the natural epitope were regarded as improved HLA-A2 epitopes. The total amount of 958 improved epitopes is listed in tables 5B, 5C and 5D. From these, the best new improved binder for each natural epitope was selected (see tables 5B and 5C). All HLA-A2 predicted epitopes from table 5B that showed an affinity between 50nM and 100nM and with a global conservation above 8% were assayed for affinity improvement. 20 peptide epitopes were sorted from table 5B in which the affinity could theoretically be improved and were compared to the binding affinity of the respective natural epitope. Example 5: Selection of clinically relevant new HLA-A2 epitopes for building HIV-1 polytope vaccines. An ideal "cover-all" candidate vaccine will be composed of 9 epitope-sets. Each set will be specific to one of the 9 HIV-1 proteins and will be composed of 1 to 10 epitopes fulfilling the above-described criteria. Set 1 1 to 10 epitopes being specific for Vpu Set 2 Vpr Set 3 Vif Set 4 Rev Set 5 Tat Set 6 Nef Set 7 Gag Set 8 Pol Set 9: " " Env A minimum of one epitope per set could be envisaged, however, several reasons advice against a "minimal selective approach": -1) very few single epitopes can fulfil all selection criteria and in particular the "cover-all" conservation criteria. This is especially true for the A2-epitopes predicted in regulatory or accessory viral proteins such as Rev, Tat, Vpu, Vpr, Vif and Nef. The combination of several epitopes is necessary in order to cover as much as possible the diversity of HIV-1 strains. -2) since the virus is able to escape the host immune response by changing its protein sequence, choosing only one single epitope per viral protein may enhance the chance for the virus to escape the CTL response elicited by the vaccine. In order to fight the virus, as many epitopes as possible should be included in a set. Two strategies can be chosen in order to overcome the viral escape. One strategy is to select all A2-epitopes that form an "epitopic" family and thus are epitope variants of each other. By immunising with such a family of variants one expects to induce an immune response directed against as many escape variants as present in the database. However, such epitope variant families are located in some region of the viral protein that can undergo high mutation rates without being too damaging for the virus. The virus has then more chance to introduce new escape mutations in this region under the immune selective pressure due to the vaccine. Another and complementary approach is to select highly conserved single epitopes located within highly conserved region of the viral proteins. Such a high conservation might reflect functional domains of the protein that cannot undergo escape mutations without being deleterious for the virus. -3) Incomplete data are nowadays available on the selective mechanisms that rule the antigen-processing of a protein into peptides (see www.cbs.dtu.dk/services). It is believed that the excision of a particular peptide from a whole protein depends on the recognition by proteolytic enzymes of particular residues that flank that peptide. Since these flanking residues are not well known, it is uncertain if each selected epitope will be properly processed from the native viral protein in the form of a peptide (at the surface of the virus infected cells) and then be antigenically available. Albeit the selected epitopes are able to raise a good immune response through vaccination, such immune response can only be efficient if these epitopes are presented in vivo as peptide-HLA-A2 complexes at the surface of infected cells. In order to overcome this uncertainty, several epitopes located in different regions (thus theoretically in distinct flanking environments) of the viral protein should be selected. Based on the criteria described above, 53 out of 354 epitopes were selected to be incorporated in a synthetic polytope vaccine. These 53 epitopes are shown in tables 9 and lO.They are divided in 8 sets: Set 1 : 4 epitopes located in Vpu Set 2: 4 epitopes located in Vpr Set 3: 6 epitopes located in Vif Set 4: 4 epitopes located in Rev Set 5: 5 epitopes located in Nef Set 6: 10 epitopes located in Pol from which 4 can be located in the Reverse Transcriptase polypeptide (p51) and one epitope in the Integrase polypeptide (p31). Set 7: 10 epitopes located in Gag Set 8: 10 epitopes located in Env Few epitopes can be selected for regulatory and auxiliary proteins such as Vpu, Vpr, Vif, Rev and Nef, compared to structural proteins such as Pol, Gag and Env. This is explained by the smaller size and the higher variability of some of these regulatory and auxiliary proteins that renders it difficult for all selective criteria to be fulfilled by the epitopes found. For example, no epitope could be selected for Tat, since the few epitopes predicted by the artificial neural networks did not full fill the conservation criteria. The few epitopes selected for Vpu, Vpr, Vif, Rev were not highly conserved in all HIV-1 subtypes, however they presented a certain interest because they were conserved among more than 20 % of the strains within at least one HIV-1 subtype. For example, in set 1, the natural epitope IVGLIVAL was conserved in 66,6% of HIV-1 AE recombinant strains despite its global conservation of 3,2% among all HIV-1 strains. This epitope was regarded as a good candidate immunogen for raising a CTL response against HIV-1 AE recombinant strains. Most of the selected epitopes were improved for their binding to HLA-A2. The use of improved epitopes was dictated by the observation that most of the best-conserved natural epitopes were intermediate or low A2-binders, also, these improved epitopes could be expected to raise a CTL response against the natural epitopes from which they were derived. In some cases, a particular improved epitope was an improved version of several natural epitopes, thus the immune response raised against such improved epitope was able to be directed against all HIV-1 strains expressing the different natural epitopes. For example the improved epitope ILAIWWW was related to two natural epitopes: IIAIWWTI (Kd=373,1nM) and ILAIWWTI (Kd=39,9nM) that were found respectively, in 32,1% and 9,6% of all HIV-1 strains tested. Thus an immunisation performed with this improved epitope was expected to raise an immune response directed against 41 ,7% (32,1%+9,6%) of HIV-1 strains (% of global coverage). The natural epitope IIAIWWTI was conserved in 33,3% of HIV-1 subtype A strains, 32% of HIV-1 subtype B strains, 54% of HIV-1 subtype C strains, 56,2% of HIV-1 subtype D strains, 33,3% of recombinant AE strains, 66,65% of subtype F and 66,65 of subtype H. The natural epitope ILAIWWTI was found in 41,6% of HIV-1 subtype A strains, 33,3% of AC recombinant strains, 3,1% of HIV-1 subtype B strains, 31,2% of HIV-1 subtype D strains. The improved epitope ILAIWWW was therefore expected to be able to induce a CTL immune response directed against 74,9% (33,3% + 41,6%) of HIV-1 subtype A strains, 35%(32%+3%) of subtype B strains, 54% of subtype B strains, 87,4% (56,2%+31,2%) of subtype D strains. Example 6: Designing and building of a "cover-all" polytope vaccine A "cover-all" polytope vaccine will be composed of a string of pearls, each "pearl" being one peptide-epitope. In a first step, a synthetic protein sequence will be computer-designed. All peptide-epitope sequences will be placed one after each other without incorporating any amino-acid linkers in between each peptide-epitope. The peptide-epitope order within this synthetic protein will be defined randomly or by the use of a computer programme allowing the evaluation of different proteolysis-specific signalisation sites that are created by a combinatorial positioning of all peptide-epitopes. In a second step, the designed polyepitopic protein sequence will be back-translated into a nucleotide sequence using software such as DNAstar (Lasergene) or any other commonly available programme. For each amino-acid residue, a highly expressed mammalian codon will be chosen in order to enhance the transcription and translation levels of the engineered synthetic gene. In a last step, the designed nucleotide sequence will be modified in order to introduce silent mutations that generate unique restriction enzyme sites. Such unique restriction enzyme sites will allow the building of this synthetic gene as a system of several cassettes. Each cassette will contain several peptides encoding nucleic sequences (so called minigenes). Finally, specific restriction sites will be added to the 5'- and 3'-ends of the gene in order to clone the full-length synthesised gene of the "cover-all" polytope into a suitable mammalian expression vector and/or a viral vector. Building the synthetic polytope gene The synthetic "cover-all" polytope gene will be constructed using a long oligonucleotide complementary annealing technique (Fomsgaard et al, 1999). The nucleotide sequence of the gene will be subdivided in fragments of approximately 100 to 120 base pairs. For example, a synthetic gene with a length of 1417 base pairs (39 9mer-peptides and 14 8mer-peptides) could be divided into 12 to 14 fragments. Each of these double stranded DNA fragments will be synthesised as a pair of complementary oligonucleotides (sense and anti-sense strands). Each extremity of each of these paired oligonucleotides will be designed in such a manner that after their annealing, the double stranded DNA fragment will harbour complementary overhanging extremities with the 5'and 3' neighbouring DNA fragments. Each pair of complementary sense and anti-sense oligonucleotides will be annealed individually. A ligation procedure will be performed by using 3 to 4 distinctly re- annealed double stranded DNA fragments in order to allow the assembly of a 300 to 480bp gene cassette and its insertion into a suitable vector such as pMOSBLUE or pBlueScript. XL1-Blue bacteria or any suitable E.Coli strain will be transformed with the ligation product. Positive recombinant clones will be amplified. The recombinant plasmid DNAs will be purified and their sequence will be checked by sequencing. This procedure will be performed until all designed cassettes (300-480 bp fragments) are synthesised and cloned. All cassettes (between 5 or 3 cassettes containing between 11 to 18 peptide- epitopes) will then be ligated to each other in order to reconstitute the full-length gene and be cloned into a suitable mammalian expression vector and/or a viral vector. Example 7: Mouse experiments To analyse immunogenicity of a predicted epitope, HLA-A2 transgenic mice were immunised with the peptide representing the epitope. Immunisation and CTL methods for this have been described in the literature as seen in (Sette et al., 1994,) and the experiments were performed largely in accordance to this paper. Briefly, stock solutions were made of the query peptide (4 mg/ml in water) and a T-helper epitope peptide (amino acid sequence: TPPAYRPPNAPIL) (Milich et al., 1988) at 4.8 mg/ml in water and incomplete Freund Adjuvant (from Statens Serum Institut, DK). A mixture of IFA (0.2 ml) plus Th-peptide (0.2 ml) plus the test epitope peptide (0.2 ml) was made on ice. 100 μl of the IFA/peptide mixture were injected intracutaneously (i.e.) at the root of the tail of HLA- A2 transgenic mice (a gift from Dr Nicholas Holmes, Camebridge, UK). Usually 3 mice were injected per query peptide. CTL was measured at day 10. Cytotoxic T Lymphocyte (CTL) assay CTL assay methods have been described in the literature (Sette et al., 1994), and the experiments were performed largely in accordance to that paper. Briefly, the spleen of peptide immunised HLA-A2 transgenic mice were collected aseptically 10 days after immunisation and placed in 5 ml cell medium (RPM1 1640, peniccilin+steptomycin (P+S), 2% Hepes buffer, 10% Fetal calf serum) on ice. The splenocytes were cultured for 6 days in the presence of LPS blasts coated with 100 μg/ml of the peptide (stimulator cells) and then assayed for peptide-specific A * 0201/Kb-restricted CTL activity by using EL4-A2 and EL4 cell lines in the presence or absence of the query peptide as the target cells. The splenocytes from immunised mice were cultured for 6 days mixed with stimulator cells at a responder/stimulator cell ratio of 2.5/1 are called "effector cells". LPS blasts were prepared from 1 normal spleen (non-immunised HLA-A2 mouse). 3-5 x 10 6 splenocytes per ml of LPS medium in a final volume of 40 ml. LPS medium was RPMI 1640, 1% penicillin-streptomycin, 2% Hepes, 10% FCS, 7 μg/100 ml medium of dextrane sulphate (Pharmacia #17-0340-01) and 25 ug/100 ml medium of LPS (Sigma #L-2387). After 3 days of culturing the splenocytes in LPS medium, the LPS blasts were treated with mitomycin to inhibit cell division. LPS blasts were treated with mitomycin at the same time as they were loaded with the target peptide. 2 ml of blasts were mixed with 20 μg of peptide and 100 μg of mitomycin in PBS. Incubate 1 hour at 37 °C in 5% C0 2 in an incubator. LPS blasts were then washed in media and adjusted to 2 x 10 7 cells/ml of medium (RPMI 1640, P+S, Hepes, 50 μM mercaptoethanol). In the present invention EL-4-A2 or EL4 cells or EL4-A2kb Cell line are interchangeable and described as follows: The plasmid pA2kb is a kind gift from Nikolas Holmes. 10 7 EL4 cells (ATCC TIB-39) are electroporated with 20μg of pA2kb using the Gene Pulser system (BIO-RAD)(Potter et al.,1993). Electroporation was performed according to the manufactor's recommendations. Selection is undertaken after 48hr with 600μg/ml geneticin (G418 Gibco BRL). Cell clones are screened with the HLA-A2 specific monoclonal antibody (BB7.2 ATCC-HB82) by FACS analysis. A standard 51 Cr release CTL assay was done according to (Marker et al., 1973). Briefly, target cells were EL-4-A2 or EL4 cells that had been incubated for 1 hour at 37 °C in the presence of 200 μl of sodium 51 Cr chromate, washed 3 times, and resuspended in RPMI 1640, 10% FCS at a concentration of 10 5 cells/ml in the absence or presence of 10 μg/ml of the appropriate peptide. For the assay, 100 μl of target cells were incubated with 100μl of different concentrations of effector cells in U-bottom 96-well plates. Supematants (100 μl) were removed after 6 hours at 37 °C and the percent lysis determined by the formula: % release = 100 x (experimental release - spontaneous release) / (maximum release - spontaneous release) An example of the CTL assay data is shown in figure 1. Of 3 HLA-A2 transgenic C57BU6 mice (4-4, 5-1, 5-2) all (A2+H3 (4-4), A2+H3 (5-2), A2+H3 (5-1)) responded (>10% specific lysis at E: T=50) to vaccination with a peptide (H3) representing a predicted CTL epitope (amino acid sequence ILKEPVHGV) that is already described in the literature as an HLA-A2 CTL epitope. The higher the effector cell to EL4-A2 Target cell ratio (E: T ratio) the higher the specific % lysis. No unspecific lysis of non-peptide loaded EL4-A2 target cells were seen (A2 -pep (4-4), A2 -pep (5-1 ), A2 - pep (5-2)). No unspecific lysis of HLA-A2 negative EL4 target cells were seen whether H3 peptide loaded (EL4+H3 (4-4), EL4+H3 (5-1), EL4+H3 (5-2)) or not (EL4 -pep (4-4), EL4 - pep (5-1), EL4 -pep (5-2)). Two control experiments were performed to secure that the process of in vitro stimulation of effector cells for 5 days could not in it self generate effector cells that would be able to lyse target cells. "Not immunised re-stimulated w. H3": HLA-A2 transgenic mice which were not immunised with the H3 peptide, but whose effector splenocytes had been stimulated in vitro with H3 loaded stimulator blasts did not react against EL4-A2 target cells loaded with H3 peptide (A2+H3) or not (A2 -pep). This demonstrated that the process of in vitro stimulation for 5 days in itself could not result in effector cells reacting towards specific peptide loaded EL4-A2 target cells. This confirmed the specific induction of CTL by the peptide immunisation. The in vitro stimulated effector cells from these non-immunised HLA-A2 mice did not react against HLA-A2 negative EL4 target cells whether H3 loaded (EL4+H3) or not (EL4 -pep). The control experiment "Not immunised re-stimulated w. LV9" showed that splenocytes from non immunised HLA-A2 transgenic mice could not be made reactive to another known HLA-A2 restricted epitope peptide (LV9, sequence LLGRNSFEV) from the tumour antigen p53 by 5 days of in vitro stimulation with LV9 peptide loaded blasts cells. Thus, the figure showed no reaction to EL4 target cells (Kd positive, HLA-A2 negative) or EL4- A2 target cells loaded with LV9 (EL4+LV9 and A2+LV9, respectively) or not peptide loaded (EL4 -pep and A2 -pep, respectively) (the results are shown in figure 1). Example 8: Mouse experiment to validate cross-reaction of the improved epitope and the natural epitope The method to validate an improved epitope for its ability to raise a CTL immune response that cross-reacts with the natural epitope is similar to that described in example 7. A2 transgenic immunisation: Mice were immunised with the selected improved epitope using 100μl out of 600μ of a mixture containing IFA (30%), HBV T-helper epitope (960μg) and test epitope (800μg). CTL was measured at day 10 post-immunisation. In vitro stimulation of CTL effectors: Splenocytes obtained from the immunised mice were divided in two parts. The first half of the splenocytes were stimulated in the presence of LPS blasts loaded with the related natural epitope peptide. Stimulation was allowed 5 to 6 days (the results are shown in figure 2). Cytotoxic T-lymphocyte assays: The stimulated CTL effectors were assayed for their ability to lyse peptide-loaded or unloaded EL4-A2 + and EL4 (A2 " ) cell lines. CTL effectors that were in vitro stimulated with improved epitope were tested for a specific lysis of target cells loaded with improved peptide. Reciprocally, CTL effectors in vitro stimulated with the natural epitope were tested for a specific lysis of natural peptide loaded target cells. The percent of specific lysis obtained with improved peptide loaded target cells was then compared with that obtained with natural peptide loaded target cells. A A2-restricted specific lysis of more than 10% of target cells loaded with the improved peptide was indicative of this improved peptide being immunogenic. A A2-restricted specific lysis of more than 10% of target cells loaded with the natural peptide was indicating that CTL effectors raised in vivo against the improved peptide were able to recognise the natural peptide. In a parallel experiment, the natural epitope-peptide was tested for its ability to induce a CTL response in A2 transgenic mice. Immunogenicity of the natural and improved peptides was then compared. An enhanced immunogenicity of the improved peptide towards the natural peptide was concluded if the specific lysis directed against the natural peptide was significantly higher when CTL effectors originating from mice immunised with improved peptide were used. See figure 3 and the legend thereto for specific results. Tables Table 5A: CTL epitopes with high affinity binding and an intermediate global conservation, meaning with a global conservation of more than 1% and a cut off value for the weighted average of the MHC class I binding for the query peptide of less than 100 nM. This especially preferred embodiment is further described in example 4. Table 5A contains 213 new HLA- A2 epitopes. They are grouped by protein family and ranked according to their length (9 or 8 amino acid residues) and to their global conservation. Table 5A 01/55177 50 GLOBAL % OF INTRA-SUBΪYPES Table 5B: 9-mers representing one best new improved binder for each natural epitope with a binding affinity IC 50 between 50nM and 500nM and with a global conservation above 8%. 100 HLA-A29-mer-epitopes were found. These 110 HLA-A2 epitopes were regarded as natural intermediate binders whose binding affinity could be increased to obtain an improved binding affinity of IC 50 below 100nM by modifying one or two of the primary anchor positions. 100 optimally (best) improved HLA-A2 restricted HIV epitopes were identified. Table 5B CONSERVATION in INTRA-SUBTYPES NAME IMPROVED Kd (nM) NATURAL Kd (nM) HXB2 GLOBAL A B C D AE F G H J N O MAPPING 8.1 VLAAIIAIV 19.1 WAAIIAIV 140,7 vpu(13-21 9,6 22 6 8.2 ILAIWWTV 12.1 l(l/L)AIWWTI 373,1/ 39,9 Vpu(17-25) 41,8 74 35 54 87,4 8.3 ALVEMGHHV 35.5 ALVEMGHHA 123,7 Vpu(66-74) 17,3 8 35 25 8.4 FLRPWLHGV 10.2 FPRPWLHGL 499,7 Vpr(34-42) 17,3 62,5 8.5 SLGQHIYEV 17.8 SLGQHIYET 161 Vpr(41-49) 19,7 29 40 8.6 SLGQYIYEV 28.9 SLGQYIYET 314,4 Vpr(41-49) 13,3 16,5 60 8.7 LLITTYWGL 24 LVITTYWGL 189,1 Vif(64-72) 38,5 39 12,5 n 8.8 LLVRTYWGV 11.7 LWRTYWGL 146,3 Vif(64-72) 9,8 75 8,7 8.9 LLVTTYWGV 20.1 LWTTYWGL 328,2 Vif(64-72) 9,8 12 8.10 KLKPPLPSV 48.8 K(I/T)KPPLPSV 444,4/ 231,1 Vif(158-166) 56,3 35 12 25 87 8.11 GLADQLIHV 47.0 GLADQLIH(UM) 90,6/ 279,4 Vif(101-109) 23,4 25 87,5 91 8.12 ALAALITPV 11.9 ALAALITPK 137,6 Vif(149-157) 18,5 29,6 8.13 ALTALITPV 27.5 ALTALITPK 440,5 Vif(149-157) 17 5 20,6 39,5 8.14 LLLPPIERV 19.5 LQLPPIERL 422,6 Rev(73-81) 15,2 36 42 8.15 GLGSPQILV 37.7 G(V/M)GSPQILV 347,6/ 55,9 Rev(96-104) 18,1 52,8 8.16 ILVESPAW 82.6 ILVESPAVL 169,7 Rev(102-110) 8,6 26.4 8.17 ALVEICTEV 31.8 ALVEICTEM 183,4 p51 (33-41) 26,7 78,5 8.28 ALTEICTEV 32,8 ALTEICTEM 194,7 p51(33-41) 16,3 8.18 LLIPHPAGV 6.6 LGIPHPAGL 317,9 p51(92-100) 88,4 75 90 100 100 100 100 66,6 100 100 100 8.19 YLAFTIPSV 53,1 YTAFTIPSV 266,2 p51(127-135) 12,8 10,7 12,5 50 100 8.20 HLLRWGFTV 33.4 HLLRWGFTT 351,8 p51(208-216) 36 61 37,5 40 66,6 66,6 8.21 FLWMGYELV 29 FLWMGYELH 288,8 p51 (227-235) 97,7 87,5 100 100 100 100 100 100 100 100 100 100 8.22 KLNWASQIV 30 KLNWASQIY 256,7 p51(263-271) 94,2 100 92,8 100 80 100 50 100 100 100 100 100 CONSERVATION in % INTRA-SUBTYPES NAME IMPROVED Kd (nM) NATURAL Kd (nM) HXB2 GLOBAL A B C D AE F G H J N 0 MAPPING 8.23 SLIYAGIKV 27.9 SQIYAGIKV 268 p51 (268-276) 43 75 71 20 100 8.24 SLIYPGIKV 21.3 SQIYPGIKV 205,1 p51(268-276) 39,5 12,5 21,4 100 80 50 100 66,6 50 8.25 ALTEVIPLV 29.2 ALTEVIPLT 319,4 p51 (288-296) 24,4 61 80 8.29 ALTDIVPLV 31.8 ALTDIVPLT 348,8 p51 (288-296) 26,7 8.26 ALTDIVTLV 22.7 ALTDIVTLT 231,8 p51 (288-296) 10,5 75 12,5 33,3 8.27 ALTEWPLV 26.4 ALTEWPLT 281 p51 (288-296) 10,5 25 20 8.30 KLWYQLEKV 17.7 KL YQLEK(E/D) 445,3/ 397,8 p51 (424-432) 71 88 87,5 50 100 100 100 8.31 LLGRWPVKV 8.0 LAGRWPVKV 91,9 p31(104-112) 40,7 75 75 80 66,6 33,3 50 100 8.32 ALKAACW V 17.6 A(V/M)KAACWWA 483,1/ 77,7 p31 (125-133) 50 50 87,5 60 100 50 100 100 100 8.33 LLTAVQMAV 7 LKTAVQMAV 140,2 P31(172-180) 90,7 100 96 87,5 100 100 100 100 33,3 100 100 50 8.34 KLMAGADCV 35.2 KQMAGADCV 450,7 p31 (273-281) 9,3 87,5 8.35 NLAFPQGEV 83,3 NLAFPQGEA 330,7 Pol(5-13) 15,1 17,8 87,5 8.36 LLQRPLVTV 13.3 LWQRPLVTV 227,5 Pol(61-69) 29,1 87,5 66,6 66,6 8.37 ILLWQRPLV 74,1 ITLWQRPLV 400,8 Pol(59-67) 73,3 87,5 75 100 60 100 100 66,6 66,6 100 8.38 ILLWQRPIV 70.7 ITLWQRPIV 378,6 Pol(59-67) 8,1 14,3 33,3 33,3 8.39 LLGPTPVNV 13.1 LVGPTPVNI 468,4 Pol(132-140) 86 100 100 100 100 100 100 100 100 100 8.40 FLISPIETV 13.6 FPISPIETV 467,6 Pol(155-163) 84,9 8.41 KLGKAGYW 26 KLGKAGYVT 245,2 Pol(606-614) 57 87 5 78,5 80 66,6 100 33,3 100 8.42 YLAWVPAHV 15.4 YLAWVPAHK 220,1 Pol(687-695) 38,4 12,5 96,4 80 8.43 ALNADCAWV 44.1 ATNADCAWL 458 Nef(50-58) 20,3 28 15 8.44 FLVRPQVPV 6 FPVRPQVPL 222 Nef(68-76) 74,9 62 73 90 8.45 LLFGWCFKL 10 L(T/C)FGWCFKL 29,6/ 115,2 Nef(137-145) 79,2 100 78 100 95,4 50 100 100 50 8.46 LLWKFDSRV 66,5 LMWKFDSRL 218,3 Nef(181-189) 10,4 12,5 13,2 100 8.47 . .. - - RLAFHHMAV 16,2 RLAFHHMAR 223,6 Nef(188-196) 26,3 40 8.48 SLYNTVATV 33,4 SLYNTVATL 63,8 Pr55(77-85) 32,3 50 50 12,5 20 100 8.49 ' NLVATLYCV 36 NTVATLYCV 169,7 Pr55(80-88) 57,3 62,5 69 87 80 66,6 33,3 100 8.50 NLVAVLYCV 38.4 NTVAVLYCV 179 Pr55(80-88) 8,3 25 19 CONSERVATION in INTRA-SUBTYPES NAME IMPROVED Kd (nM) NATURAL Kd (nM) HXB2 GLOBAL A B C D AE F G H J N 0 MAPPING 8.51 TLYCVHQKV 72,6 TLYCVHQKI 385,6 Pr55(84-92) 15,6 12,5 25 12,5 66,6 8.52 TLWCVHQRV 71.6 TLWCVHQRI 388,8 Pr55(84-92) 9,4 12,5 66,6 8.53 LLGQMVHQV 14 LQGQMVHQA 481 Pr55(138-146) 28,1 40 62,5 60 50 8.54 RLLNAVWKV 73,2 RTLNAWVKV 366,8 pr55(150-158) 94,8 100 100 100 100 100 100 100 100 100 100 8.55 RLHPVQAGV 18.1 RLHPVQAGP 359,6 Pr55(214-222) 10,4 15,6 12,5 20 16,6 8.56 TLQEQIGWV 42,3 TLQEQIGWM 335,9 Pr55(242-250) 43,8 87,5 20 66,6 50 8.57 TLQEQIAWV 32.6 TLQEQIAWM 189,6 Pr55(242-250) 14,6 6 62 40 66,6 8.58 MLNNPPIPV 18.5 MTNNPPIPV 71,3 Pr55(250-258) 29,2 78 66,6 8.59 MLSNPPIPV 21.7 MTSNPPIPV 87,8 Pr55(250-258) 29,2 38 9 80 33,3 16,6 33,3 8.60 MLSNPPVPV 19.9 MTSNPPVPV 78,1 Pr55(250-258) 8,3 25 50 33,3 8.61 KLVRMYSPV 20 KIVRMYSPV 157,1 Pr55(272-280) 59,4 100 100 80 66,6 83,3 100 100 100 8.62 RLYSPVSIV 34 RMYSPVSIL 104,6 Pr55(275-283) 58,3 100 100 80 66,6 83,3 100 100 100 100 8.63 RLYSPTSIV 81 RMYSPTSIL 272,9 Pr55(275-283) 22,9 65 8.64 ALGPAATLV 14.9 ALGPAATLE 392,9 Pr55(336-344) 26 72 8.65 ALLEEMMTV 26.6 ATLEEMMTA 431 Pr55(341-349) 75 87 97 80 100 83,3 66,6 100 100 8.142 SLEEMMTAV 32.4 SLEEMMTAC 295,8 Pr55(342-350) 8.66 ELMTACQGV 85 EMMTACQGV 134,2 pr55(345-353) 91,7 87,5 100 100 80 100 83,3 66,6 100 100 8.67 MLQRGNFRV 16.7 MMQRGNFR(N/G) 347,4/ 439,4 Pr55(377-385) 30,2 37,5 62,5 8.68 MLQRGNFKV 12.3 MMQRGNFKG 247,4 Pr55(377-385) 13,5 37,5 60 66,6 16,6 8.69 FLQSRPEPV 13 FLQSRPEPT 96,5 Pr55(448-456) 43,8 72 62 60 50 66,6 100 8.70 FLQNRPEPV 14.2 FLQNRPEPT 114,9 Pr55(448-456) 13,5 12,5 16,6 66,6 8.71 HLWRWGTMV 56 HLWRWGTML 111,5 gp160(9-21) 14,1 25 28,6 8.72 MLLGMLMIV 19.2 MLLGMLMIC 146,4 gp160(19-27) 23,5 45 8.73 KLWVrVYYV 18.3 KLWVTVYYG 297,7 gp160(33-41) 18,3 18,75 30 7,1 11,1 33,3 8.74 VLVYYGVPV 60 VTVYYGVPV 299,2 gp160(35-44) 90,1 87,5 98 91 85,7 100 75,0 100 100 100 100 8.75 NLWATHACV 50 N(V/I)WATHACV 490,6/486,1 gp160(67-75) 90,7 100 94 95,4 85,5 88,/ 100 100 100 100 8.76 KLTPLCVTV 57 KLTPLCVTL 114,7 gp160(121-129) 82,2 87,5 87 95,4 85,7 77,7 100 100 100 CONSERVATION in INTRA-SUBTYPES NAME IMPROVED Kd (nM) NATURAL Kd (nM) HXB2 GLOBAL A B C D AE F G H J N 0 MAPPING 8.143 YLAPAGFAV 5,9 YCAPAGFAI 204,5 gp160(217-225) 52,1 8.77 YLAPAGYAV 8.3 YCAPAGYAI 420,5 gp160(217-225) 16,9 12,5 1 77,2 7 11,1 100 75 8.78 SLAEEEVW 46 SLAEEEW(I/L) 218,3/90,7 gp160(264-272) 31 63 8.79 SLAEEEIIV 57.3 SLAEEEIII 281 gp160(264-272) 9,9 18,2 64,3 66,6 8.80 ALYAPPIRV 13.0 AMYAPPIRG 276,8 gp160(433-441) 22,1 6 45 33,3 8.81 ALYAPPIEV 15 AMYAPPIEG 354,1 gp160(433^441) 12,2 6 7 22,7 65 8.82 AMYAPPIKV 12.1 AMYAPPIKG 154,6 gp160(433-441) 8,9 12,5 8 27,0 33,3 8.83 AMYAPPIAV 18.6 AMYAPPIAG 298 gp160(433-441) 8,5 7 22,7 7 75 8.84 PLGIAPTKV 68,6 PLGIAPTKA 264,4 gp160(493-501) 11,3 15 27,3 22,2 8.85 TLGAASITV 41 TMGAASITL 126,8 gp160(529-537) 46,9 81 25 100 100 75 75 100 100 100 8.86 TLGAASLTV 81 TMGAASLTL 273,2 gp160(529-537) 13,1 17 42,8 25 8.87 RLIEAQQHV 13.5 RAIEAQQHL 489,9 gp160(557-565) 65,3 62,5 77 27,2 71,4 88,8 75 100 100 8.88 ALEAQQHLV 14 AIEAQQHLL 200 gp160(558-566) 70 81,2 77 27,3 78,5 88,8 75 100 50 100 8.89 ALEAQQHMV 19.4 AIEAQQHML 310,2 gp160(558-566) 14,1 12 63,6 8.90 LLKLTVWGV 27 LLKLTVWGI 120,4 gp160(565-573) 14,1 93,7 100 8.91 CLTAVPWNV 15.3 CTTAVPWNA 202,5 gp160(604-612) 17,4 37 8.92 SLWNWFSIV 40.0 SLWNWFSIT 442,3 gp160(668-676) 11,7 13 50 11,1 33,3 8.93 YLKIFIMIV 33 YIKIFIMIV 286,3 gp160(681-689) 60,1 37,5 68 72,7 57,1 66,6 100 75 33,3 8.94 YLRIFIMIV 42.3 YIRIFIMIV 378,9 gp160(681-689) 9,9 31,2 8.95 FLMIVGGLV 41.3 FIMIVGGLV 391,4 gp160(685-693) 22,5 47 8.96 ILFAVLSIV 24.9 l(V/l)FAVLSIV 203,1/200,6 gp160(697-705) 40,3 41 72,7 7 100 75 100 100 8.97 LLAARTVEV 14.6 LIAARTVEL 317,2 gp160(776-784) 26,3 62,5 9 55,5 50 8.98 LLVARIVEV 17.9 LIVARIVEL 240,5 gp160(776-784) 8,9 18 Table 5C: 8-mers representing one best new improved binder for each natural epitope with a binding affinity IC 50 between 50nM and 500nM and with a global conservation above 8%. 47 HLA- A28-mer-epitopes were found. These 47 HLA-A2 epitopes were regarded as natural intermediate binders whose binding affinity could be increased to obtain an improved binding affinity of IC 50 below 100nM by modifying one or two of the primary anchor positions.45 optimally (best) improved HLA-A2 restricted HIV epitopes were identified. Table 5C % CONSERVATION INTRA- SUBTYPE! 3 NAME IMPROVED Kd nM NATURAL Kd nM HBX2 GLOBAL A B C D AE F G H J N 0 MAPPING 8.99 LLFIHFRV 92.5 LLFIHFRI 495,2 vpr(67-74 69,9 12,5 87,4 62,5 100 100 100 66,6 100 8.100 LLFVHFRV 67,1 LLFVHFRI 359 Vpr(67-74) 11 87,5 12,5 100 8.101 ILGHIVSV 20.6 ILGHIVSP 268,1 Vif(124-131) 20,4 5 27,8 17,4 8.102 KLGSLQYV 29 KVGSLQYL 151,6 Vif(141-148) 82,3 95 89,7 75 100 100 100 66,6 33,3 100 8.103 RLAEPVPV 36.8 RSAEPVPL 340,5 Rev(66-73) 19 9 23,5 25 12,5 33,3 8.144 YLSNPYPV 12,2 YQSNPYPK 241,2 Rev(23-30) 15,2 36,4 25 75,0 50,0 8.104 GLGSPQIV 31.3 GVGSPQIL 168,8 Rev(96-103 16,2 47 8.105 KLGPENPV 38.4 KIGPENPY 497 p51 (49-56) 76,7 100 89,3 87,5 20 100 50 100 66,6 8.106 QLGIPHPV 16 QLGIPHPA 161,7 p51(91-98) 88,4 75 89 100 100 100 100 66,6 100 100 100 8.107 TLNDIQKV 15 TVNDIQKL 66,5 p51(253-260) 97,7 100 100 100 80 100 50 100 100 100 100 100 8.108 ALTDIVPV 57,8 ALTDIVPL 78,9 p51 (288-295) 26,7 50 66,6 50 33,0 100 8.109 LLAEIQKV 20.4 LIAEIQKQ 311,5 p51(325-332) 53,5 75 50 100 60 50 66,6 8.110 LLEWQKV 62,2 LAEWQKV 408,2 p51 (368-375) 10,5 62,5 33,3 50 8.111 FLNTPPLV 26 FVNTPPLV 97,6 p51(416-423) 86 oo 89 100 80 100 50 100 100 100 8.112 FLFPQITV 37.8 FSFPQITL 318 Pol(54-61) 27,9 62,5 42,8 20 100 8.113 FLFPQITV 37.8 FNFPQITL 316,8 Pol(54-61) 14 14,2 12,5 20 33,3 100 8.114 FLKVKQYV 28.8 FIKVKQYD 477,2 Pol(109-116) 20,9 87,5 3,5 33 100 8.115 LLFPISPV 15 LNFPISPI 369,8 Pol(153-160) 93 100 96,4 100 100 100 50 100 66,6 100 100 100 8.116 ALGIIQAV 13 ALGIIQAQ 131,3 Pol(657-664) 84,9 87,5 93 87,5 100 100 100 100 100 100 8.117 GLGVRYPV 20.7 GPGVRYPL 417 Nef(130-137) 17,5 25 17,3 35 9 8.118 GLGTRFPV 16 GPGTRFPL 281,8 Nef(130-137) 13,9 25 11 25 100 50 8.119 TLGWCFKV 27 TFGWCFKL 254,3 Nef(138-145) 66,5 87,5 74 100 50 100 100 50 100 % CONSERVATION INTRA- SUBTYPES NAME IMPROVED Kd nM NATURAL Kd nM HBX2 GLOBAL A B C D AE F G H J N 0 MAPPING 8.120 CLGWCFKV 19.5 CFGWCFKL 168,6 Nef(138-145) 13,5 5,4 100 8.121 WLFKLVPV 49.9 WCFKLVPV 433,6 Nef(141-148) 82,5 87,5 87,4 100 100 100 66,6 50 8.145 FLHVAREV 84,4 FHHVAREL 381,9 Nef(191-198) 13,5 nd nd nd nd nd nd nd nd nd nd nd 8.146 FLHMAREV 89,1 FHHMAREL 394,7 Nef(191-198) 19,9 nd nd nd nd nd nd nd nd nd nd nd 8.122 ILGQLQPV 6,7 ILGQLQPS 94.9 Pr55(60-67) 15,6 47 8.123 TLNPPIPV 26.4 T(N/S/G)NPPIPV 164,6/160,9/380,3 Pr55(251-258) 68,8 100 87,4 80 100 17 66 8.124 PLGEIYKV 11.3 PVGEIYKR 457,5 Pr55(257-264) 61,5 93,8 25 80 50 100 100 8.125 PLGDIYKV 7.9 PVGDIYKR 211,2 Pr55(257-264) 30,2 87,5 6 75 66,6 33,3 100 8.126 ALGPAATV 51,3 ALGPAATL 70 Pr55(336-343) 27,1 75 20 8.127 FLGKI PV 4 FLGKIWPS 38,6 Pr55(433-440) 84,4 75 93,8 87,5 80 100 100 100 100 100 8.128 LLNRPEPV 29.4 LQNRPEPT 492,9 Pr55(449-456 13,5 3,1 12,5 16,6 66,6 8.129 1LGDIRQV 15.1 IIGDIRQA 190,2 gp160(322-329) 47,4 43,7 58 86,3 50 33,3 8.130 SLGDPEIV 16.8 SGGDPEIV 200,5 gp160(365-372) 37,6 79 8.131 CLGEFFYV 9.1 CRGEFFYC 490,8 gp160(378-385) 22,5 1 86,3 7 100 75 75 66,6 100 8.132 ILNMWQEV 62 IINMWQEV 95,1 gp160(423-430) 27,7 39 50 35,7 8.133 ILNMWQGV 95,6 IINMWQGV 146,4 gp160(423^30) 11,7 9 40,9 21,4 33,3 8.134 FLGFLGAV 15 FLGFLGAA 156,1 gp160(519-526) 73,2 43,7 89 86,3 78,6 25 75 100 100 8.135 FLGAAGSV 30 FLGAAGST 178,7 gp160(522-529) 88,3 94 96 95,4 78,6 100 25 75 100 100 8.136 LLARILAV 96,5 LQARILAV 281,4 gp160(576-583) 10,3 6 8 50 8.137 YLKDQQLV 56 YLKDQQLL 75,8 gp160(586-593) 50,2 50 54 68 57 50 50 66,6 100 8.138 YLRDQQLV 96,4 YLRDQQLL 132,8 gp160(586-593) 22,5 37,5 26 9 21 25 33 8.139 ILGGLVGV 84 IVGGLVGL 497,5 gp160(688-695) 24,9 52 8.140 ILFAVLSV 57 IIFAVLSI 449,3 gp160(697-704) 17,8 6 4 82 77,7 25 66,6 8.141 LLNGFLAV 74,7 LVNGFLAL 447,2 gp160(748-755) 13,1 6 15 27 50 100 Table 5D: 8-mers and 9-mers representing other improved binders for each natural epitope with a binding affinity IC 0 between 50nM and 500nM and with a global conservation above 8%. 812 HLA-A2 epitopes were found. These 812 HLA-A2 epitopes were regarded as natural intermediate binders whose binding affinity could be increased to obtain an improved binding affinity of IC 50 below 100nM by modifying one or two of the primary anchor positions. 65 Table SO Peptide Kd nM Peptide Kd nM AIEAQQHLV 95,4 AMEAQQHLI 81,4 AIGIIQAI 76,2 AMEAQQHLL 36,1 AIGIIQAL 23,5 AMEAQQHLV 20,0 AIGIIQAV 18,3 AMEAQQHMA 90,9 AIYAPPIEV 98,4 AMEAQQHML 51,3 AIYAPPIRV 77,0 AMEAQQHMV 27,5 AIAALITPV 67,8 AMGIIQAI 78,0 ALEAQQHLA 39,9 AMGIIQAL 23,8 ALEAQQHLI 52,6 AMGIIQAV 18,5 ALEAQQHLL 24,6 AMGPAATLA 65,9 ALEAQQHMA 58,4 AMGPAATLI 87,5 ALEAQQHMI 78,9 AMGPAATLL 38,3 ALEAQQHML 34,4 AMGPAATLV 20,9 ALGIIQAI 51,3 AMKAACWWA 77,7 ALGIIQAL 16,9 AMKAACWWL 45,8 ALGPAATLA 42,9 AMKAACWWV 24,6 ALGPAATLI 56,0 AMLEEMMTL 74,6 ALGPAATLL 25,9 AMLEEMMTV 38,3 ALKAACWWA 50,9 AMNADCAWV 66,6 ALKAACWWI 66,8 AMTALITPL 75,4 ALKAACWWL 31,0 AMTALITPV 39,6 ALLEEMMTA 84,2 AMTDIVPLL 90,4 ALLEE MTL 49,8 AMTDIVPLV 45,9 ALNADCAWL 86,8 AMTDIVTLL 62,4 ALTALITPA 87,9 AMTDIVTLV 32,5 ALTALITPL 49,7 AMTEICTEL 99,4 ALTDIVPLL 59,4 AMTEICTEV 49,3 ALTDIVTLA 73,1 AMTEVIPLL 84,0 ALTDIVTLI 98,0 AMTEVIPLV 42,8 ALTDIVTLL 41,3 AMTEWPLL 74,7 ALTEICTEL 63,3 AMTEWPLV 38,2 ALTEVIPLA 97,5 AMVEICTEL 94,3 ALTEVIPLL 54,8 AMVEICTEV 47,4 ALTEWPLA 86,9 AMVEMGHHV 52,4 ALTEWPLL 49,0 AMYAPPIEA 61 ,9 ALVEICTEL 60,5 AMYAPPIEI 83,3 ALVEMGHHL 68,3 AMYAPPIEL 36,1 ALYAPPIEA 41,8 AMYAPPIEV 20,6 ALYAPPIEI 55,3 AMYAPPIRA 49,5 ALYAPPIEL 25,4 AMYAPPIRI 66,2 ALYAPPIRA 34,0 AMYAPPIRL 29,5 ALYAPPIRI 44,5 AMYAPPIRV 17,4 ALYAPPIRL 21,0 AMAALITPA 44,0 ALAALITPA 30,2 AMAALITPI 59,0 ALAALITPI 39,5 AMAALITPL 27,0 ALAALITPL 19,1 AMAALITPV 15,9 AMEAQQHLA 60,9 AQGIIQAL 38,3 Peptide Kd nM Peptide Kd nM AQGIIQAV 28,9 FIQNRPEPV 93,5 AQAALITPV 91,7 FIQSRPEPV 79,4 CIGEFFYA 76,4 FIRPWLHGL 99,8 CIGEFFYC 70,2 FIRPWLHGV 52,4 C1GEFFYI 46,5 FIVRPQVPA 82,0 CIGEFFYL 16,3 FIVRPQVPL 46,9 CIGEFFYV 13,1 FIVRPQVPV 26,1 CIGWCFKL 36,3 FLFPQITL 50,2 CIGWCFKV 27,7 FLFPQITL 50,2 CLGEFFYA 50,7 FLGFLGAI 61,2 CLGEFFYC 45,8 FLGFLGAL 19,2 CLGEFFYI 29,8 FLGAAGSL 40,8 CLGEFFYL 11,2 FLISPIETA 36,7 CLGWCFKI 84,1 FLISPIETI 48,3 CLGWCFKL 25,1 FLISPIETL 22,6 CLTAVPWNA 42,4 FLKVKQYL 38,0 CLTAVPWNI 55,8 FLMIVGGLL 81,0 CLTAVPWNL 26,6 FLNTPPLL 34,6 CMGEFFYA 77,8 FLQNRPEPA 39,2 CMGEFFYC 71,8 FLQNRPEPI 50,9 CMGEFFYI 46,5 FLQNRPEPL 23,8 CMGEFFYL 16,2 FLQSRPEPA 34,1 CMGEFFYV 13,0 FLQSRPEPI 43,8 CMGWCFKL 37,1 FLQSRPEPL 21,1 CMGWCFKV 28,1 FLRPWLHGA 24,3 CMTAVPWNA 64,5 FLRPWLHGI 31,3 CMTAVPWNI 86,2 FLRPWLHGL 15,6 CMTAVPWNL 39,1 FLVRPQVPA 13,3 CMTAVPWNV 21,4 FLVRPQVPI 16,4 CQGEFFYI 68,4 FLVRPQVPL 9,3 CQGEFFYL 21,8 FLWMGYELA 97,2 CQGEFFYV 17,1 FLWMGYELL 56,0 CQGWCFKL 61 ,9 FMFPQITL 82,5 CQGWCFKV 45,7 FMFPQITL 82,5 CRGEFFYL 61,7 FMFPQITV 61,5 CRGEFFYV 46,0 FMFPQITV 61,5 CTTAVPWNV 54,9 FMGFLGAI 96,1 EMQKQITKV 92,3 FMGFLGAL 27,9 FIFPQITL 80,8 FMGFLGAV 21,8 FIFPQITL 80,8 FMGAAGSL 61,7 FIFPQITV 60,4 FMGAAGSV 45,4 FIFPQITV 60,4 FMISPIETA 54,8 FIGFLGAI 91,7 FMISPIETI 73,3 FIGFLGAL 26,7 FMISPIETL 32,3 FIGFLGAV 20,9 FMISPIETV 18,5 FIGAAGSL 58,0 FMKVKQYL 60,9 FIGAAGSV 42,7 FMKVKQYV 45,8 FIISPIETV 86,2 FMMIVGGLV 62,4 FIKVKQYL 59,2 FMNTPPLL 53,4 FIKVKQYV 44,7 FMNTPPLV 40,2 FINTPPLL 52,2 FMQNRPEPA 59,2 PIMTPPI \/ 396 FMQNRPEPI 78,5 Peptide Kd nM Peptide Kd nM FMQNRPEPL 34,6 GQGTRFPV 35,9 FMQNRPEPV 19,5 GQGVRYPL 68,4 FMQSRPEPA 51,0 GQGVRYPV 49,7 FMQSRPEPI 66,9 HLLRWGFTL 62,7 FMQSRPEPL 30,5 HMLRWGFTL 97,1 FMQSRPEPV 17,3 HMLRWGFTV 49,2 FMRPWLHGA 34,2 HMWRWGTMV 85,4 FMRPWLHGI 44,9 IIAIWWTV 71,3 FMRPWLHGL 21,3 IIFAVLSV 88,7 FMRPWLHGV 13,3 IIGAETFYV 51,8 FMVRPQVPA 18,2 IIGAETFYV 51,8 FMVRPQVPI 22,9 IIGDIRQI 94,1 FMVRPQVPL 12,2 IIGDIRQL 28,6 FMVRPQVPV 8,2 IIGDIRQV 22,1 FMWMGYELL 86,6 IIGHIVSL 39,1 FMWMGYELV 44,0 HGHIVSV 29,7 FQFPQITV 95,2 IIGQLQPA 50,8 FQFPQITV 95,2 IIGQLQPI 29,9 FQGFLGAL 45,0 IIGQLQPL 11,2 FQGFLGAV 34,2 IIGQLQPV 9,2 FQGAAGSV 75,6 ILAIWWTA 31,0 FQKVKQYL 93,6 ILAIWWTI 39,9 FQKVKQYV 69,0 ILAIWWTL 19,8 FQNTPPLL 86,8 ILFAVLSIA 79,5 FQNTPPLV 63,4 ILFAVLSIA 79,5 FQRPWLHGV 71,3 ILFAVLSIL 45,1 FQVRPQVPL 61,4 ILFAVLSIL 45,1 FQVRPQVPV 33,4 ILFAVLSL 77,6 GIGSPQIL 62,1 ILGAETFYA 23,6 GIGSPQIV 46,4 ILGAETFYA 23,6 GIGTRFPI 97,3 ILGAETFYA 23,6 GIGTRFPL 28,5 ILGAETFYI 30,4 GIGTRFPV 22,3 ILGAETFYI 30,4 GIGVRYPL 37,9 ILGAETFYI 30,4 GIGVRYPV 28,6 ILGAETFYL 15,4 GLGSPQIL 41,7 ILGAETFYL 15,4 GLGSPQILL 71,7 ILGAETFYL 15,4 GLGTRFPI 64,3 ILGDIRQI 60,6 GLGTRFPL 20,2 ILGDIRQL 19,4 GLGVRYPI 92,7 ILGHIVSI 90,8 GLGVRYPL 27,2 ILGHIVSL 26,7 GMADQLIHV 69,9 ILGQLQPA 35,2 GMGSPQIL 63,3 ILGQLQPI 19,8 GMGSPQILV 55,9 ILGQLQPL 8,0 GMGSPQIV 46,9 ILNMWQEL 85,4 GMGTRFPI 99,8 IMAIWWTA 46,4 GMGTRFPL 29,1 IMAIWWTI 60,8 GMGTRFPV 22,6 IMAIWWTL 28,5 GMGVRYPL 38,7 IMAIWWTV 16,4 GMGVRYPV 28,9 IMFAVLSIL 68,5 GQGSPQIV 79,9 IMFAVLSIL 68,5 GQGTRFPL 47,5 IMFAVLSIV 35,8 Peptide Kd nM Peptide Kd nM IMFAVLSIV 35,8 KLWYQLEKA 51,5 IMFAVLSV 91,8 KLWYQLEKI 67,2 IMGAETFYA 33,9 KLWYQLEKI 67,2 IMGAETFYA 33,9 KLWYQLEKL 31,3 IMGAETFYA 33,9 KLWYQLEKL 31,3 IMGAETFYI 44,7 KMGKAGYVL 74,0 IMGAETFYI 44,7 KMGKAGYW 38,2 IMGAETFYI 44,7 KMGPENPL 75,6 IMGAETFYL 21,3 KMGPENPV 55,8 IMGAETFYL 21,3 KMGSLQYL 57,5 IMGAETFYL 21,3 KMGSLQYV 42,6 IMGAETFYV 12,9 KMKPPLPSV 72,4 IMGAETFYV 12,9 KMKPPLPSV 72,4 IMGAETFYV 12,9 KMNWASQIL 86,3 IMGDIRQI 97,4 KMNWASQIV 44,5 IMGDIRQL 29,3 KMTPLCVTV 87,5 IMGDIRQV 22,4 KMVRMYSPA 99,5 IMGHIVSL 40,4 KMVRMYSPL 56,5 IMGHIVSV 30,6 KMVRMYSPV 29,6 IMGQLQPA 52,0 KMWVTVYYA 82,8 IMGQLQPI 30,1 KMWVTVΥYL 48,0 IMGQLQPL 11,1 KMWVT /YYV 25,9 IMGQLQPV 9,1 KMWYQLEKA 79,3 IQAIWWTV 94,3 KMWYQLEKA 79,3 IQGAETFYV 69,9 KMWYQLEKL 46,4 IQGDIRQL 44,2 KMWYQLEKL 46,4 IQGDIRQV 33,1 KMWYQLEKV 25,1 IQGHIVSL 66,1 KMWYQLEKV 25,1 IQGHIVSV 48,7 KQGSLQYV 72,5 IQGQLQPA 92,6 LCFGWCFKV 57,4 IQGQLQPI 44,7 LIAEIQKL 40,0 IQGQLQPL 15,0 LIAEIQKV 30,6 IQGQLQPV 12,0 LIEWQKV 95,2 KIGPENPL 73,9 LIFGWCFKA 88,1 KIGPENPV 54,8 LIFGWCFKL 50,7 KIGSLQYL 56,2 LIFGWCFKV 27,1 KIGSLQYV 42,3 LIFPISPI 94,7 KLGKAGYVA 82,1 LIFPISPL 28,9 KLGKAGYVL 48,3 LIFPISPV 22,7 KLGPENPL 51,5 LIGPTPVNV 84,2 KLGSLQYL 38,3 LIGQMVHQV 89,7 KLKPPLPSL 93,3 LIGRWPVKL 71,3 KLKPPLPSL 93,3 LIGRWPVKV 37,2 KLNWASQIA 94,6 LIIPHPAGA 93,0 KLNWASQIL 56,7 LIIPHPAGL 51,6 KLVRMYSPA 63,7 LIIPHPAGV 27,7 KLVRMYSPI 83,4 LIITTYWGV 88,7 KLVRMYSPL 37,5 LINRPEPL 59,1 KLWVTVYYA 54,3 LINRPEPV 44,1 KLWVT YI 71,9 LIQRPLVTV 80,5 KLWVTVYYL 32,5 LITAVQMAL 52,0 KLWYQLEKA 51,5 LITAVQMAV 28,0 Peptide Kd nM Peptide Kd nM LITAVQMAA 91,4 LMGPTPVNI 71,2 LIAARTVEV 95,2 LMGPTPVNL 31,5 LLAEIQKI 89,4 LMGPTPVNV 17,9 LLAEIQKL 26,4 LMGQMVHQA 56,6 LLEWQKL 85,6 LMGQMVHQI 75,2 LLFGWCFKA 13,9 LMGQMVHQL 33,3 LLFGWCFKI 17,1 LMGQMVHQV 18,8 LLFGWCFKV 6,7 LMGRWPVKA 25,0 LLFPISPI 57,9 LMGRWPVKI 32,3 LLFPISPL 18,8 LMGRWPVKL 16,3 LLFVHFRL 91,2 LMGRWPVKV 10,2 LLGPTPVNA 35,6 LMIPHPAGA 19,1 LLGPTPVNI 46,6 LMIPHPAGI 23,8 LLGPTPVNL 21,8 LMIPHPAGL 12,7 LLGQMVHQA 38,4 LMIPHPAGV 8,3 LLGQMVHQI 50,1 LMITTYWGA 58,2 LLGQMVHQL 23,4 LMITTYWGI 75,1 LLGRWPVKA 17,8 LMITTYWGL 34,3 LLGRWPVKI 22,5 LMITTYWGV 19,2 LLGRWPVKL 12,1 LMKLTVWGL 77,0 LLIPHPAGA 13,9 LMKLTVWGV 39,3 LLIPHPAGI 17,0 LMLPPIERA 90,4 LLIPHPAGL 9,6 LMLPPIERL 50,4 LLITTYWGA 38,6 LMLPPIERV 27,5 LLITTYWGI 49,0 LMNRPEPL 60,7 LLITTYWGV 14,0 LMNRPEPV 44,9 LLKLTVWGA 90,2 LMQRPLVTA 51,0 LLKLTVWGL 50,3 LMQRPLVTI 68,7 LLLPPIERA 59,0 LMQRPLVTL 30,4 LLLPPIERI 78,6 LMQRPLVTV 17,7 LLLPPIERL 33,9 LMTAVQMAl 24,7 LLNRPEPL 39,1 LMTAVQMAL 13,0 LLQRPLVTA 34,9 LMTAVQMAV 8,5 LLQRPLVTI 46,0 LMTAVQMAA 19,2 LLQRPLVTL 21,6 LMAARTVEA 60,5 LLTAVQMAI 17,5 LMAARTVEI 80,9 LLTAVQMAL 9,8 LMAARTVEL 36,4 LLTAVQMAA 14,0 LMAARTVEV 20,4 LLAARTVEA 39,7 LNFPISPL 94,9 LLAARTVEI 52,2 LNFPISPV 70,4 LLAARTVEL 24,7 LQAEIQKL 63,9 LMAEIQKL 40,5 LQAEIQKV 47,5 LMAEIQKV 30,7 LQFGWCFKL 66,1 LMEWQKV 97,7 LQFGWCFKV 34,5 LMFGWCFKA 19,1 LQFPISPL 41,8 LMFGWCFKI 24,1 LQFPISPV 32,0 LMFGWCFKL 13,1 LQGRWPVKV 49,0 LMFGWCFKV 8,4 LQIPHPAGL 68,7 LMFPISPI 94,6 LQIPHPAGV 35,9 LMFPISPL 28,4 LQNRPEPV 73,0 LMFPISPV 22,2 LQTAVQMAL 68,8 LMGPTPVNA 53,8 LQTAVQMAV 36,2 Peptide Kd nM Peptide Kd nM L LVVGGPPTTPPVVNNVV 8 844,,11 PMGDIYKA 67,8 L LVVIITTTTYYWWGGVV 9 900,,99 PMGDIYKI 38,2 M MIIQQRRGGNNFFKKVV 7 711,,55 PMGDIYKL 13,8 M MLLLLGGMMLLMMIIAA 5 566,,88 PMGDIYKR 77,4 M MLLLLGGMMLLMMIIJJ 7 733,,88 PMGDIYKV 10,9 M MLLLLGGMMLLMMIILL 3 344,,44 PMGEIYKI 62,4 M MLLNNNNPPPPIIPPAA 5 544,,99 PMGEIYKL 20,3 MLNNPPIPL 32,2 PMGEIYKV 15,7 MLQRGNFKA 31,4 PQGDIYKI 57,5 MLQRGNFKI 40,8 PQGDIYKL 18,8 MLQRGNFKL 20,4 PQGDIYKV 14,6 MLQRGNFRA 48,0 PQGEIYKL 30,5 MLQRGNFRI 63,8 PQGEIYKV 22,9 MLQRGNFRL 29,0 PVGDIYKI 88,0 MLSNPPIPA 66,8 PVGDIYKL 26,9 MLSNPPIPL 38,4 PVGDIYKV 20,5 MMLGMLMIA 86,2 PVGEIYKL 44,1 MMLGMLMIL 50,7 PVGEIYKV 32,7 MMLGMLMIV 27,0 QIGIPHPI 96,8 MMNNPPIPL 47,8 QIGIPHPL 28,2 MMNNPPIPV 25,8 QIGIPHPV 21,9 MMQRGNFKA 46,4 QLGIPHPI 63,5 MMQRGNFKI 61,4 QLGIPHPL 20,0 MMQRGNFKL 29,1 QLTEAVQKL 92,7 MMQRGNFKV 16,6 QMGIPHPI 99,5 MMQRGNFRA 73,4 QMGIPHPL 28,9 MMQRGNFRI 99,8 QMGIPHPV 22,4 MMQRGNFRL 42,7 QMTEAVQKV 71,0 MMQRGNFRV 23,3 QQGIPHPL 47,0 MMSNPPIPL 57,5 QQGIPHPV 35,5 MMSNPPIPV 30,9 RIAEPVPL 72,0 MQQRGNFKV 94,7 RIAEPVPV 54,0 NLVATLYCL 69,6 RIIEAQQHV 84,1 NLWATHACL 99,6 RLAEPVPL 48,4 NMADQLIHV 60,8 RLAFHHMAI 61,4 NMVATLYCV 54,4 RLAFHHMAL 28,4 NMWATHACV 77,3 RLAFHHMAA 45,9 PIGDIYKA 66,0 RLHPVQAGA 53,6 PIGDIYKI 37,9 RLHPVQAGI 71,3 PIGDIYKL 13,7 RLHPVQAGL 31,2 PIGDIYKR 75,2 RLIEAQQHA 36,1 PIGDIYKV 11,0 RLIEAQQHI 46,5 PIGEIYKI 61,4 RLIEAQQHL 22,9 PIGEIYKL 20,2 RLYSPVSIL 67,1 PIGEIYKV 15,7 RMAEPVPL 73,4 PLGDIYKA 45,2 RMAEPVPV 54,8 PLGDIYKI 24,9 RMAFHHMAI 96,4 PLGDIYKL 9,8 RMAFHHMAL 42,3 PLGDIYKR 50,7 RMAFHHMAV 23,1 PLGEIYKA 96,1 RMAFHHMAA 70,9 PLGEIYKI 40,9 RMHPVQAGA 82,5 PLGEIYKL 14,3 RMHPVQAGL 46,5 Peptide Kd nM Peptide Kd nM RMHPVQAGV 25,6 TINPPIPL 52,2 RMIEAQQHA 54,8 TINPPIPV 39,1 RMIEAQQHI 72,0 TINPPIPV 39,1 RMIEAQQHL 33,5 TLGWCFKL 35,5 RMIEAQQHV 18,8 TLGAASITL 80,6 RMYSPVSIV 51,5 TLNDIQKI 61,2 RQAEPVPV 95,1 TLNDIQKL 19,8 SIGDPEIL 31,9 TLNPPIPL 34,9 SIGDPEIV 24,7 TLNPPIPL 34,9 SLAEEEWL 90,7 TLQEQIAWL 62,2 SLEEMMTAL 61,9 TMGWCFKL 51,7 SLGDPEII 69,4 TMGWCFKV 38,6 SLGDPEIL 21,4 TMGAASITV 62,6 SLGQHIYEA 53,8 TMNDIQKL 29,7 SLGQHIYEI 70,5 TMNDIQKV 22,7 SLGQHIYEL 31,7 TMNPPIPL 53,0 SLGQYIYEA 97,8 TMNPPIPL 53,0 SLGQYIYEL 54,7 TMNPPIPV 39,6 SLIYAGIKA 85,6 TMNPPIPV 39,6 SLIYAGIKL 51,5 TMQEQIAWL 96,7 SLIYPGIKA 62,6 TMQEQIAWV 48,2 SLIYPGIKI 81,9 TMQEQIGWV 80,1 SLIYPGIKL 37,7 TQGWCFKL 91,6 SLVKHHMYA 77,5 TQGWCFKV 66,4 SLVKHHMYL 48,0 TQNDIQKL 44,4 SLWNWFSIL 77,8 TQNDIQKV 33,0 SMAEEEVW 70,5 TQNPPIPL 89,8 SMEEMMTAL 96,9 TQNPPIPL 89,8 SMEEMMTAV 49,4 TQNPPIPV 65,2 SMGDPEIL 32,7 TQNPPIPV 65,2 SMGDPEIV 25,1 TVNDIQKV 49,2 SMGQHIYEA 83,4 VLAEAMSQL 78,1 SMGQHIYEL 47,6 VMAEAMSQV 60,7 SMGQHIYEV 25,3 VMVYYGVPV 92,0 SMGQYIYEL 84,9 WIFKLVPV 78,9 SMGQYIYEV 42,7 WLFKLVPL 65,9 SMIYAGIKL 78,3 WMFKLVPV 79,9 SMIYAGIKV 40,9 YCAPAGFAL 83,0 SMIYPGIKA 96,8 YCAPAGFAV 42,5 SMIYPGIKL 56,4 YCAPAGYAV 80,7 SMIYPGIKV 30,3 YIAPAGFAI 88,0 SMVKHHMYL 72,0 YIAPAGFAL 39,9 SMVKHHMYV 38,4 YIAPAGFAV 22,1 SMWNWFSIV 60,7 YIAPAGFAA 66,8 SMYNTVATV 49,7 YIAPAGYAL 75,5 SQGDPEIL 49,8 YIAPAGYAV 38,9 SQGDPEIV 37,4 YISNPYPI 71,5 TIGWCFKL 50,9 YISNPYPL 22,4 TIGWCFKV 38,2 YISNPYPV 17,6 TINDIQKL 29,7 YLAPAGFAI 14,0 TINDIQKV 22,7 YLAPAGFAL 8,5 TINPPIPL 52,2 YLAPAGFAA 11,6 51 72 Peptide Kd nM Peptide Kd nM YLAPAGYAI 23,4 AMYAPPIKL 19,7 YLAPAGYAL 13,0 AQYAPPIKV 62,4 YLAPAGYAA 18,8 KLMAGADCL 68,4 YLAWVPAHA 43,4 KMMAGADCV 53,3 YLAWVPAHI 57,0 LIVRTYWGV 69,2 YLAWVPAHL 26,3 LLVARIVEA 52,0 YLKIFIMIL 62,8 LLVARIVEI 68,7 YLSNPYPI 45,8 LLVARIVEL 31,0 YLSNPYPK 83,8 LLVRTYWGA 30,9 YLSNPYPL 15,3 LLVRTYWGI 38,9 YMAFTIPSV 81,0 LLVRTYWGL 19,3 YMALQDSGV 69,5 LLVTTYWGA 62,5 YMAPAGFAI 19,9 LLVTTYWGI 80,9 YMAPAGFAL 11,4 LLVTTYWGL 36,1 YMAPAGFAV 7,5 LMVARIVEA 79,6 YMAPAGFAA 16,1 LMVARIVEL 45,9 YMAPAGYAI 34,9 LMVARIVEV 25,3 YMAPAGYAL 18,2 LMVRTYWGA 45,9 YMAPAGYAV 11,1 LMVRTYWGI 58,9 YMAPAGYAA 27,4 LMVRTYWGL 27,6 YMAWVPAHA 65,7 LMVRTYWGV 15,8 YMAWVPAHI 88,5 LMVTTYWGA 96,4 YMAWVPAHL 38,5 LMVTTYWGL 54,3 YMAWVPAHV 21,4 LMVTTYWGV 28,8 YMKIFIMIL 96,9 LQVRTYWGV 90,7 YMKIFIMIV 48,9 LWRTYWGV 71,3 YMSNPYPI 71,4 MLSNPPVPA 60,0 YMSNPYPL 22,2 MLSNPPVPL 35,1 YMSNPYPV 17,3 MMSNPPVPL 52,5 YQAPAGFAL 48,1 MMSNPPVPV 28,4 YQAPAGFAV 25,9 NLVAVLYCL 73,8 YQAPAGFAA 84,4 NMVAVLYCV 57,7 YQAPAGYAL 93,2 QMTEWQKV 85,3 YQAPAGYAV 46,9 SMAEEEIIV 89,3 YQSNPYPL 33,2 VLAAIIAIA 56,4 YQSNPYPV 25,3 VLAAIIAII 75,3 AIYAPPIAV 84,5 VLAAIIAIL 33,4 AIYAPPIKL 89,8 VMAAIIAIA 87,2 AIYAPPIKV 46,3 VMAAIIAIL 49,8 ALYAPPIAA 36,7 VMAAIIAIV 27,2 ALYAPPIAI 48,7 YLRIFIMIL 82,2 ALYAPPIAL 22,6 YMRIFIMIV 64,0 ALYAPPIAV 13,9 ALYAPPIKA 21,9 ALYAPPIKI 27,9 ALYAPPIKL 14,6 ALYAPPIKV 9,4 AMYAPPIAA 53,9 AMYAPPIAI 72,7 AMYAPPIAL 32,0 AMYAPPIKA 30,8 AMYAPPIKI 40,0 Table 5E: 8-mers and 9-mers representing new natural intermediate binders (predicted binding IC 50 = 50 - 500 nM) with a global conservation among HIV strains above 8% and which cannot readily be improved by changing of anchor position amino acids. Table 5E Natural % of global epitope predicted Kd (nM) HXB2 mapping conservation IIRILQQL 480,5 Vpr(60-67) 60,7 DLADQLIHL 322,5 Vif(101-109) 28,3 SLFGNDPL 451,6 Pr55(491-498) 19,8 SLFGSDPL 272,2 Pr55(491-498) 12,5 ALGTGATL 386,8 Pr55(336-343) 9,4 ALQDSGSEV 189,3 Pol(640-648) 37,2 ELQAIQLAL 268,4 Pol(633-641) 8,1 ELQAIQLAL 268,4 Pol(633-641) 8,1 NLAFQQGEA 407,8 Pol(5-13) 23,3 KLVDFREL 477,7 p51 (73-80) 98,8 ALTDIVTL 278,2 p51 (288-295) 10,5 ALTEWPL 277,1 p51 (288-295) 10,5 ELHPDKWTV 290,4 P51 (233-241) 91,9 VILVAVHV 272,7 p31 (72-79) 44,2 IILVAVHV 175,3 p31 (72-79) 39,5 PLWKGPAKL 462 p31 (233-241) 32,6 RQGFERAL 420,1 gp160(848-855) 13,6 RQGFERAL 420,1 gp160(848-855) 13,6 SLLNATAIA 434,5 gp160(813-821) 24,9 SLLNATAIA 434,5 gp160(813-821) 24,9 ALKYWWNLL 374,4 gp160(792-800) 17,8 LIVARIVEL 268,1 gp160(776-784) 8,5 RLRDFILIA 437 gp160(770-778) 9,4 RLRDFILIA 437 gp160(770-778) 9,4 FLALAWDDL 317,2 gp160(752-760) 26,3 FLAIIWVDL 240,5 gp160(752-760) 8,9 RLVSGFLAL 479,9 gp160(747-755) 22,5 LQARVLAV 487,6 gp160(576-583) 57,3 QLQARVLAV 129,4 gp160(575-583) 57,3 RLISCNTSV 103,5 gp160(192-200) 20,7 RLINCNTSV 123,6 gp160(192-200) 9,4 ALFYKLDW 180,4 gp160(174-182) 23 Table 6: Complete 8-mer, 9-mer and 10-mer sets of PSCPL were synthesised as described in the text. Table 7: HIV-1 protein sequences from which HLA-A2 epitopes were predicted and their distribution within the genetic subtypes composing group M (subtypes A, AB, AC, AD, ADI, AE, AG, AGI, AGJ, B, BF, C, CD, D, F, G, H, J) or within the groups N or group O. HIV-1-related sequences such as SIVcpz were included since SIVcpz viruses share a high genetic homology with HIV-1 group N in Env. Table 7: Number of available HIV-1 sequences in Los Alamos 1998-1999 HIV protein sequences Database according to genetic groupes and subtypes HIV-1 M N 0 CPZ Groups subtypes in all A AB AC AD ADI AE AG AGI AGJ B BF C CD D F G H J DB GAG 96 8 0 6 2 I 3 5 4 1 32 ' I 8 0 5 6 3 3 2 1 2 3 - POL 86 8 0 5 I 3 5 4 1 28 ' I 8 0 5 2 3 3 2 1 2 3 VIF 265 20 0 4 I 3 5 4 1 165 ' I 8 0 23 2 3 3 2 1 15 3 VPR 173 8 0 4 I 3 5 4 I 103 ' I 8 0 5 2 3 3 2 1 15 3 VPU 156 12 2 6 I 3 6 4 I 63 ' I 13 0 16 4 3 3 2 1 11 3 TAT 101 10 0 5 I 3 5 4 I 35 ' I 10 0 7 4 3 3 2 1 2 3 REV 105 11 1 5 I 3 5 4 I 34 ' I 12 0 8 4 3 3 2 1 2 3 ENV 213 16 1 5 5 1 9 8 4 I 100 ' I 22 1 14 4 4 3 2 1 8 3 NEF 251 8 0 3 2 1 22 5 2 I 167 ' 20 0 4 2 3 3 2 1 2 3 Table 8: Shows that the HIV-1 subtype B sequences often dominated in the database that was used for predicting HLA-A2 epitopes. Table 8: Percentage of available HIV-1 sequences in The Los Alamos 1998-1999 HIV sequences Database, according to genetic subtypes and viral proteins. HIV-l/M HIV- HIV- 1/N I/O AB AC AD ADI AE AG AGI AGJ B BF CD D H J C GAG 8,33 0 6,25 2,08 1,04 3,12 5,2 4,165 1 ,04 33,3 1,045 8,33 0 5,2 6,25 3,12 3,12 2.08 1,04 2,08 POL 9,3 0 5,81 1,16 1,16 3,5 5,81 4,65 1,16 32,55 1,16 9,3 0 5,81 2,3 3,49 3,49 2,3 1,16 2,3 VIF 7,55 0 1,5 0,3 0,3 1,13 1,88 1,5 0,3 62,26 0,3 3 0 8.68 0,75 1 ,13 1,13 0,75 0,38 5,66 VPR 4,62 0 2,3 0,5 0,5 1,7 2,89 2,3 0,5 59,54 0,5 4,62 0 2,89 1,15 1,73 1,73 1,15 0,57 8,67 VPU 7,69 1,28 3,84 0,64 0,64 1,92 3,84 2,56 0,64 40,38 0,64 8,33 0 10,25 2,56 1,92 1,92 1,28 0,64 7,05 TAT 9,9 0 4,95 0,99 0,99 2,97 5,95 3,96 0,99 34,65 0,99 9,9 0 6,93 3,96 2,97 2,97 1,98 0,99 1,98 REV 10,47 0,95 4,76 0,95 0,95 2,85 4,76 3,8 0,95 32,38 0,95 11,42 0 7,6 3,8 2,85 2,85 1,9 0,95 1,9 ENV 7,5 0,46 2,35 2,35 0,46 4,22 3,75 1,87 0,46 46,95 0,46 10,33 0,46 6,57 1,88 1,88 1,4 0,93 0,4 3,75 Tables 9 and 10: Based on the criteria described in the text, 53 out of 354 epitopes were selected to be incorporated in a synthetic polytope vaccine. They are divided in 8 sets: Set 1: 4 epitopes located in Vpu Set 2: 4 epitopes located in Vpr Set 3: 6 epitopes located in Vif Set 4: 4 epitopes located in Rev Set 5: 5 epitopes located in Nef Set 6: 10 epitopes located in Pol from which 4 can be located in the Reverse Transcriptase polypeptide (p51) and one epitope in the Integrase polypeptide (p31). Set 7: 10 epitopes located in Gag Set 8: 10 epitopes located in Env For each epitope the improved epitope or the related natural epitope with measured good binding (IC 50 <100nM) were be chosen. Table 9: example of epitopes candidates for a "cover-all" vaccine %of COVERAGE NAME EPITOPE Kd (nM] ) HXB2 RELATED GLOBAL INTRA-SUBTYPES MAPPING NATURAL A B C D AE F G H J N 0 4.8 lVGLIVAL 67,8 Vpu(9-16) 3,2 16,6 66,6 8.1 VLAAIIAIV 19.1 vpu(13-21 WAAIIAIV 9,6 22 6 8.2 ILAIW TV 12.1 Vpu(17-25) l(l/L)AIWWTI 41,8 74 35 54 87,4 8.3 ALVEMGHHV 35.5 Vpu(66-74) ALVEMGHHA 17,3 8 35 25 4.13 LHGLGQYV 96,4 Vpr(39-46) 4,0 62,5 50 0 8.4 FLRPWLHGV 10.2 Vpr(34-42) FPRPWLHGL 17,3 62,5 8.5 SLGQHIYEV 17.8 Vpr(41-49) SLGQHIYET 19,7 29 40 8.99 LLFIHFRV 92.5 vpr(67-74 LLFIHFRI 69,9 12,5 87,4 62,5 100 100 100 66,6 100 4.14 SLVKHHMYV 26,6 Vif(23-31) SLVKHHMYI 61,8 90 64 75,0 69,5 66,6 33,3 33,3 100 8.7 LLITTYWGL 24 Vif(64-72) LVITTYWGL 38,5 39 12,5 8.10 KLKPPLPSV 48.8 Vif(158-166) K(I/T)KPPLPSV 56,3 35 12 25 87 8.11 GLADQLIHV 47.0 Vif(101-109) GLADQLIH(UM) 23,4 25 87,5 91 8.12 ALAALITPV 11.9 Vif(149-157) ALAALITPK 18,5 29,6 8.102 KLGSLQYV 29 Vif(141-148) KVGSLQYL 82,3 95 89,7 75 100 100 100 66,6 33,3 100 8.103 RLAEPVPV 36.8 Rev(66-73) RSAEPVPL 19 9 23,5 25 12,5 33,3 8.14 LLLPPIERV 19.5 Rev(73-81) LQLPPIERL 15,2 36 42 8.15 GLGSPQILV 37.7 Rev(96-104) G(V/M)GSPQILV 18,1 52,8 %of COVERAGE NAME EPITOPE Kd (nM) HXB2 RELATED GLOBAL INTRA-SUBTYPES MAPPING NATURAL A B C D AE F G H J N O 8.16 ILVESPAW 82.6 Rev(102-110) ILVESPAVL 8,6 26.4 8.43 ALNADCAWV 44.1 Nef(50-58) ATNADCAWL 20,3 28 15 8.44 FLVRPQVPV 6 Nef(68-76) FPVRPQVPL 74,9 62 73 90 8.45 LLFGWCFKL 10 Nef(137-145) L(T/C)FGWCFKL 79,2 100 78 100 95,4 50 100 100 50 8.121 WLFKLVPV 49.9 Nef(141-148) WCFKLVPV 82,5 87,5 87,4 100 100 100 66,6 50 8.47 RLAFHHMAV 16,2 Nef(188-196) RLAFHHMAR 26,3 40 Table 10: example of epitopes candidates for a "cover-all" vaccine %of COVERAGE NAME EPITOPE Kd (nM) HXB2 MAPPING RELATED GLOBAL INTRA-SUBTYPES NATURAL A B C D AE F G H J N 0 8.17 ALVEICTEV 31.8 p51 (33-41) ALVEICTEM 26,7 78,5 8.21 FLWMGYELV 29 p51 (227-235) FLWMGYELH 97,7 87,5 100 100 100 100 100 100 100 100 100 100 8.111 FLNTPPLV 26 p51 (416-423) FVNTPPLV 86 100 89 100 80 100 50 100 100 100 8.30 KLWYQLEKV 17.7 p51 (424-432) KLWYQLEK(E/D) 71 88 87,5 50 100 100 100 8.33 LLTAVQMAV 7 P31 (172-180) LKTAVQMAV 90,7 100 96 87,5 100 100 100 100 33,3 100 100 50 8.37 ILLWQRPLV 74,1 Pol(59-67) ITLWQRPLV 73,3 87,5 75 100 60 100 100 66,6 66,6 100 8.115 LLFPISPV 15 Pol(153-160) LNFPISPI 93 100 96,4 100 100 100 50 100 66,6 100 100 100 8.41 KLGKAGYW 26 Pol(606-614) KLGKAGYVT 57 87,5 78,5 80 66,6 100 33,3 100 8.116 ALGHQAV 13 Pol(657-664) ALGIIQAQ 84,9 87,5 93 87,5 100 100 100 100 100 100 8.42 YLAWVPAHV 15.4 Pol(687-695) YLAWVPAHK 38,4 12,5 96,4 80 8.49 NLVATLYCV 36 Pr55(80-88) NTVATLYCV 57,3 62,5 69 87 80 66,6 33,3 100 8.54 RLLNAWVKV 73,2 pr55(150-158) RTLNAWVKV 94,8 100 100 100 100 100 100 100 100 100 100 8.56 TLQEQIGWV 42,3 Pr55(242-250) TLQEQIGWM 43,8 87,5 20 66,6 50 8.123 TLNPPIPV 26.4 Pr55(251-258) T(N/S/G)NPPIPV 68,8 100 87,4 80 100 17 66 8.124 PLGEIYKV 11.3 Pr55(257-264) PVGEIYKR 61,5 93,8 25 80 50 100 100 8.64 ALGPAATLV 14.9 Pr55(336-344) ALGPAATLE 26 72 8.65 ALLEEMM 26.6 Pr55(341-349) ATLEEMMTA 75 87 97 80 100 83,3 66,6 100 100 %of COVERAGE NAME EPITOPE Kd (nM) HXB2 MAPPING RELATED GLOBAL INTRA-SUBTYPES NATURAL A B C D AE F G H J N < 0 8.66 ELMTACQGV 85 pr55(345-353) EMMTACQGV 91,7 87,5 100 100 80 100 83,3 66,6 100 100 4.149 VLAEAMSQV 40,3 Pr55(362-370) VLAEAMSQA 49 87,5 71,8 12,5 20 100 100 100 8.127 FLGKIWPV 4 Pr55(433-440) FLGKIWPS 84,4 75 93,8 87,5 80 100 100 100 100 100 8.74 VLVYYGVPV 60 gp160(35-44) VTVYYGVPV 90,1 87,5 98 91 85,7 100 75,0 100 100 100 100 8.75 NLWATHACV 50 gp160(67-75) N(V/I)WATHACV 90,7 100 94 95,4 85,5 88 100 100 100 100 8.76 KLTPLCVTV 57 gp160(121-129) KLTPLCVTL 82,2 87,5 87 95,4 85,7 77,7 100 100 100 8.85 TLGAASITV 41 gp160(529-537) TMGAASITL 46,9 81 25 100 100 75 75 100 100 100 8.88 ALEAQQHLV 14 gp160(558-566) AIEAQQHLL 70 81,2 77 27,3 78,5 88,8 75 100 50 100 4.180 LLQLTVWGI 38 gp160(565-573) 58,2 80 27,3 92,8 88,9 75 100 8.93 YLKIFIMIV 33 gp160(681 -689) YIKIFIMIV 60,1 37,5 68 72,7 57,1 66,6 100 75 33,3 8.129 ILGDIRQV 15.1 gp160(322-329) IIGDIRQA 47,4 43,7 58 86,3 50 33,3 8.130 SLGDPEIV 16.8 gp160(365-372) SGGDPEIV 37,6 79 8.135 FLGAAGSV 30 gp160(522-529) FLGAAGST 88,3 94 96 95,4 78,6 100 25 75 100 100 Table 11: Table 11 shows an example of predicted and measured Kd of epitopes from tables 5 and 6 "cover all" epitopes. Peptides corresponding to epitopes from tables 5 and 6 including related natural versions were synthezised and binding to HLA-A2 MHC-I measured in vitro as described in example 1. Results are sorted by increasing Kd values for Related natural epitopes. Measured binding values from Related Natural Epitopes (right side of the table) can be improved by anchor optimizations (Epitopes, left site of the table) as predicted. /55177 86 5177 87 l— 3 ££ Hrr « te co ro > > > ? O- CD CO > ^ C CO CO OJ OJ Φ T Q. o Q. omooioooooooo o _ ^ CO O ^Φ ∞ N O C IO N IO CO CM CM N CM "Φ in co I s - co eo co σ> CO T- ro oo_ oo_ CM_ co co_ ro h» in CM o " in co " o " oo " m " co " o " CM" o co " o " o CM CO CM -Φ- OO T- CM OO OT CM Γ-«. O -<Φ CM C - t- CM τ- CM C C ^ CM τ- -•__ * ■•____ cu .*-» .*-_-. CO CO -k-. co O CO cσ CO 03 _____ CO CO C C O c c c O c c c co c c w o c CM o m ^ c in I s - CM -Φ oo rt co co ■ Φ- in oo - CM "Φ Φ τ- CO 00 T- T- CO oocooooocooooooooooocooooo CO o ^tnZZN» r m co M co _^ _ ^ "ΦO _ ^ _-.00 ιric " 5 σ> 0cN ) 0°.o.- " .cb_;- s ; > tO (I j N CO Figure legends Figure 1: Figure 1 demonstrates the induction of CTL after immunisation of HLA-A2 transgenicC57BL/6 mice with a peptide representing a predicted HLA-A2 CTL epitope. Figure 1A: three HLA-A2 transgenic mice (4-4, 5-1 , 5-2) were immunised with H3 peptide in Freunds incomplete adjuvans at day 0. Splenocytes from day 10 were in vitro stimulated for 5 days with H3 peptide loaded LPS-blast cells and assayed in 51 Cr release assay for immune reactivity against EL4-A2 or EL4 target cell lines loaded with H3 peptide or not. Different Effector to Target cell (E: T) ratios were tested. Figure 1 B: control experiment showing no lysis of the EL4-A2 or EL4 target cells loaded with another HLA-A2 restricted epitope peptide LV9 (sequence LLGRNSFEV) or not by effector splenocytes from non immunised HLA-A2 mice even after 5 days of in vitro stimulation with LV9 loaded LPS-blast cells. Figure 1C: control experiment showing no lysis of EL4-A2 or EL4 target cells loaded with H3 or not by effector splenocytes from non immunised HLA-A2 mice even after 5 days of in vitro stimulation with H3 peptide loaded LPS-blast cells. Figure 2: Figure 2 shows the cytotoxic activity of splenocytes obtained from peptide-immunised C57BI6/A2 transgenic mice after 5 days of in vitro restimulation. Open symbols represent specific lysis of peptide-pulsed EL4A2, whereas filled symbols represent specific lysis of peptide-pulsed EL4(Kb) target cells. Figure 2A: CTL response raised against the natural peptide. The spenocytes were obtained from two mice (m9-1 and m9-3) immunised with the natural epitope (LTFGWCFKL). No specific lysis could be observed against EL4A2 targets pulsed with the natural peptide (less than 10% of the targets were lysed). Figure 2B: CTL responses raised against the improved peptide. One mouse (m9-4) was immunised with the anchor position improved peptide (LLFGWCFKL). The splenocytes were restimulated in vitro with either the improved peptide or the natural peptide. After five days of restimulation, the splenocytes harbour a high cytotoxic activity (90% of specific lysis at E:T ratio of 50) against EL4A2 targets pulsed with the improved peptide. In addition, the splenocytes induced against the improved epitope are able to lyse EL4A2 targets pulsed with the natural peptide(14% of specific lysis at E:T ratio of 50). Figure 3: Figure 3 shows the cytotoxic activity of splenocytes obtained from C57BL6/A2 transgenic mice immunised with anchor optimized peptide 8.54 RLLNAWVKV (from tables 5B and 10) after 5 days of in vitro restimulation with the same peptide and assayed against EL4- A2 target cells pulsed with the same peptide 8.54 (squares) or the related natural 8.54Nat epitope peptide (triangles). Right panels (figures 3B and 3D) show negative controls where the effector splenocytes do not react against peptide-pulsed EL4 cells that do not have the HLA-A2 MHC-I on the surface. A clear response to the peptide used for immunisation ant with cross-reaction to the related natural epitope is seen in one of 4 mice (mouse 1, upper panels, figure 3A) and a less response in mouse 2 (lower panel, figure 3C). It is known that not all mice in a group responds to such peptide vaccination . which may be due to both technical and biological variations of the A2 heterologous mice. References Sette A, Vitiello A, Reherman B, Fowler P, Nayersina R, Kast WM, Melief CJM, Oseroff C, Yuan L, Ruppert J, Sidney J, del Guercio M-F, Southwood S, Kubo RT, Chesnut RW, Grey HM, Chisari FV. The relationship between class I binding affinity and immunogenicity of potential cytotoxic T cell epitopes. J. Immunol 1994; 153: 5586-5592. Brunak S., Engelbrecht J., and Knudsen S., Prediction of human mRNA donor and acceptor sites from the DNA sequence, J. Mol. Biol., 220, 49-65, 1991. Mathews B.W., Comparison of the Predicted and Observed Secondary Structure of {T4} Phage Lysozyme, Biochim. Biophys. Acta, 405, 442-451,1975. Hobohm U., Scharf M., Schneider R. and Sander C, Selection of representative protein data sets, Protein Science, 1, 409-417, 1992 Potter, H..1993, Application of electroporation in recombinant DNA technology. Methods Enzymol., 217,461-478 Fomsgaard A, Nielsen HV, Kirkby N, Bryder K, Corbet S, Nielsen C, Hinkula J, Buus S. Induction of cytotoxic T-cell responses by gene gun DNA vaccination with minigenes encoding influenza A virus HA and NP CTL-epitopes. Vaccine 1999; 18: 681-691. Marker O & Volkert M. Studies on cell-mediated immunity to lymphocyte choriomeningitis virus in mice. J Exp Med 1973; 137: 1511-1513. Houghten RA. Combinatorial libraries: Finding the needle in the haystack. Curr Biol 1994; 4: 564-7 Review. Milich DR, McLachlan A, Thornton GB, Hughes JL. Antibody production to the nucleocapsid and envelope of the hepatitis B virus primed by a single synthetic T cell site. Nature 1987; 6139: 547-9. Schmitz JE, Kuroda MJ, Santra S, Sasseville VG, Simon MA, Lifton MA, Racz P, Tenner- Racz K, Dalesandro M, Scallon BJ, Ghrayeb J, Forman MA, Montefiori DC, Rieber EP, Letvin NL, Reimann KA. Control of viremia in Simian immunodeficiency virus infection by CD8+ lymphocytes. Science 1999; 282: 857860. Gallimore A, Cranage M, Cook N, Almond N, Bootman J, Rud E, Silvera P, Dennis M, Corcoran T, Stott J et al. Early suppression of SIV replication by CD8+ nef-specific T cells in vaccinated macaques. Nature Med 1995; 351: 290-296 Shibata R et al. Neutralizing antibody directed against the HIV-1 envelope glycoprotein can completely block HIV-1/SIV chimeric virus infection of macaque monkeys. Nature Med 5(2):204-210,1999. Koup RA et al. Temporal association of cellular immune responses with the initial control of viremia in Primary HIV-1 syndrome. J Virol 68(7):4650-5,1994 Harrer E et al. HIV-1 -specific cytotoxic T lymphocyte response in healthy, long-term nonprogressing seropositive persons. AIDS Res Hum Retrovir 10(supp.2):77-78,1994 Falk K, Rotzschke O, Stevanovic S et al. Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules. Nature 1991; 351:290-6. Ruppert J, Sidney J, Celis E et al. 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SEQ ID NOs: 214-314 are 8-mer-epitopes representing one best new improved binder for each natural epitope with a binding affinity IC 50 between 50nM and 500nM and with a global conservation above 8%. SEQ ID NOs: 314-359 are 9-mer-epitopes representing one best new improved binder for each natural epitope with a binding affinity IC 50 between 50nM and 500nM and with a global conservation above 8%. SEQ ID: SEQ Name PEPTIDE Kd nM GLOBAL CONSERVATION 1 4.1 LLAGVDYRI 83,9 1 ,3% 2 4.2 LLAKVDYRL 44,6 1,3% 3 4.3 ILAIWWTI 39,9 9,6% 4 4.4 KLVEMGHHA 71,5 2,6% 5 4.5 ALMEMGHHA 47,9 1 ,9% 6 4.6 ALVEMGHLA 76,4 1,3% 7 4.7 ALGEMGPFI 73,8 1,3% 8 4.8 IVGLIVAL 67,8 3,2% 9 4.9 IVGLIVAV 50,2 1 ,3% 10 4.10 ALIRTLQQL 38,6 3,5% 11 4.11 ALIRILQQL 52,8 3,5% 12 4.12 ALIRMLQQL 26,9 2,3% 13 4.13 LHGLGQYV 96,4 4,0% 14 4.14 SLVKHHMYV 26,6 26,0% 15 4.15 SLVKHHMHV 51,2 1,5% 16 4.16 SLVKHHIYV 67,7 1 ,5% 17 4.17 LVIRTYWGL 88 1 ,5% 18 4.18 RLRRYSTQV 76,7 1 ,5% 19 4.19 RLKRYSTQV 60,1 1,1% 20 4.20 GLADQLIHL 90,6 14,3% 21 4.21 NLADQLIHL 78,3 10,2% 22 4.22 PLGDARLV 45,4 26,8% 23 4.23 PLGDAKLV 63,9 19,2% 24 4.24 PLGEARLV 92,6 17,0% 25 4.25 PLGDATLV 35,9 1 ,9% 26 4.26 KIGSLQYL 56,2 1 ,5% 27 4.27 TLIPKQPL 42,2 2,0% 4.28 KLLYQSNPL 46,9 3,8% 4.29 CLGRPAEPV 35,1 15,2% 4.30 YLGRPAEPV 22,4 10,5% 4.31 FLGRPAEPV 12,7 3,8% 4.32 CLGRPTEPV 37,7 3,8% 4.33 CLGRPEEPV 59,8 3,8% 4.34 FLGRPEEPV 18,7 2,9% 4.35 YLGRPEEPV 36 2,9% 4.36 CLGRPPEPV 18,3 1,9% 4.37 YLGRPTEPV 23,8 1,9% 4.38 FLGRSAEPV 46,2 1,9% 4.39 HLGRPAEPV 96,6 1,9% 4.40 GMGSPQILV 55,9 2,9% 4.41 ILVESPTVL 74,4 9,5% 4.42 VLVEPPWL 47,5 1,9% 4.43 ILVESPTIL 84,7 1,9% 4.44 GMGSPQIL 63,3 2,9% 4.45 ISGEPCMV 86,8 1,9% 4.46 ISGKPCAV 95,1 1,9% 4.47 LLGRWKPKM 65,1 1,2% 4.48 YMEAEVIPA 98 4,7% 4.49 YLEAEVIPA 63,4 3,5% 4.50 LAGRWPVKV 91 ,9 40,7% 4.51 LAARWPVKV 60,9 2,3% 4.52 LTLAGRWPV 70,2 1 ,2% 4.53 AMKAACWWA 77,7 1 ,2% 4.54 ALQKQITKI 55,2 1,2% 4.55 ELQKQITKV 60 1,2% 4.56 NLQTQILKV 85,7 1,2% 4.57 ILKIQNFRV 74,6 1,2% 4.58 DLGDAYFSV 83,4 1,2% 4.59 KLHPEQARA 62,8 1,2% 4.60 ILASQIQTT 71,4 2,3% 4.61 LTAEAEMEL 71,4 2,3% 4.62 MTAEAEMEL 84,8 1,2% 4.63 ILKEPVHGA 40,9 7,0% 4.64 ILKDPVHGV 15 5,8% 4.65 ILREPVHGV 17,7 3,5% 4.66 ILKTPVHGV 35,1 2,3% 4.67 ILKDPVHGA 40,2 2,3% 4.68 KLKEPVHGV 11,1 1,2% o o o 3 a H U α. 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OO CJJ o o o o o o o o o I s - I s - I s - I s - I s - I s - σ> T- CN CJ -tf LO CO - CO CJ) © o CO o I s - I s - I s - CO CO CO O0 CO 0O CO CO CO 0O C3J <D O) 01 0) 0) (_> 0> 0) ■ φ φ * -φ φ- • φ 'φ -φ f "t t ^ ^ ^ ^ t t ^ ^ t ^ t o _ O - CM CO Φ" LO co I s - co σ> O O - ( C ^ L CO - ∞ σ O τ- CM C 'φ L CD I s - OO C_n θ τ- CM O ' L CO I s - Cβ σ> 0 0 0 o o o o o o o ^ N. ι^- r~- -. ι- t^ ι s - . ι^ co oo co co co oo oo oo c» co OT C3J c_o c» co co co σ> c σ τ- τ- τ- o o o 3 a H U α. tf^ <_>• "S tf- LO CM CM CM CO CM 00 CM CM 00 CM CO CM CO CM CD _- _r co M- r- Φ" I s - Φ ° CO- ■ ' § I s - LO oo CO LO OJ LO CO CO CO φ- I s - lO -_ O LO ■ Φ CM φ- φ- co I s - σ CO CM LO v- φ- 00 φ- O I s - CO 00 OJ co o~ • φ " LO " O Φ- CM 00 CO " Φ-" σj" θ " CD * 00 CO OJ OJ φ CO I s - r- OJ σj CO co CO OJ I s - CO 00 00 I s - oo Φ- I s - o - cM c Φ- L co ι s - co σJ θ τ- cM co Φ- ιo cD i s - co σ o τ- cM co φ- ιo cD i s - oo σ θ τ- cM co 'φ L cD i s - oo σ o r- - - r- - - T- - - T- CM CM CM N CM CM CM CM CM CM CO C C C C C C C C C φ- φ- φ- φ- φ- φ- φ- φ- ^ o O T- CM c ^ Lθ co i s - co c_iJ θ τ- cM c φ- Lo co ι- co c_n o τ- cM c Φ- Lo cD i s - co cs o - cM oo φ- to co _ s - oo σ) θ O - τ- - τ- τ- - - - CM CM CM CM CM CM CM CM CM CM C CO C C C C0 C0 C0 C0 C0 Φ φ- φ- -φ -φ φ- φ- φ- 'Φ Φ L o o o 3 a H U α. ~s ~s ~5 *-s ~-5 o * ^ 6 s - *« ~s ^ OJ O LO LO CO OJ φ- 00 OJ 00 CO • φ φ- φ- i CO CO OJ CO CM O CO φ- O 00 OJ LO I s - " φ-~ co " coc 00 — CM CO I s - CM CM CO CM CM CM CM 00 * COo CM L T- OJ LO CM I s - I s - LO OO σ> CM co ιo σ> co co Φ- r- 00 1- OJ CO - CO LO 1- OJ CO "φ LO o 00 LO φ--- Φ 0 LO I s - c o I s o LO - I s - I s - CO I s - o Φ - " ^ CM CO 00 o CM • Φ φ- CO LO O" T-" co " o o I s - c ■ φ OJ 0 CM CO CD I s - CD LO CD 00 OJ I s - 00 CM • φ OJ I s - LO OJ LO 00 00 00 φ- Φ- - C oo φ- L co i^ co cπ o T- c c - ω i^ ca cn o T- CM c φ- ω i^ co cn o - cM c Φ- Lo cD i^ co σ O - lo iO L L LO L L L co co co ω co co co co co cD i^ i^ i^ - i^ i^ i^ i^ i^ co co oo co oo oo oo oo co co σj cΛ Φ- < Φ- Φ- Φ * Φ- Φ- Φ- Φ- Φ- Φ- Φ- Φ- Φ- Φ- 'Φ Φ' Φ- Φ- Φ- o τ- CM oθ Φ- Lo co ι s - co σ j © ι- cN o Φ- l0 CD _ s - 00 σ> O τ- CM 00 φ- L0 C0 l s - 00 σj O - CM 00 Φ' L0 C0 t s - C0 C__J O τ- O lO LO LO lO LO LO LO LO LO CD CO CΩ CO CO CO CD CD CD CO I- l s - l s - I s - t s - t- I- l s - l- l s - CO 00 00 00 00 00 00 C0 C0 00 C_!J OJ o OJ o o I s - CM °^ I s - LO LO Γ- CO ^, ■ φ 00 CO CM 00 00 00 Φ - CO ■ φ 00 CD CM CM 00 CM o I s -" φ ^ φ- OJ Φ" CO o" ^ S ffl - °s o o CM I s - „- LO OJ OJ ^ o o " C °° CO CM CM 2 w O OJ co " LO CO co " CM I s - co" -Φ in CO O Φ- 00 CM I s - 3 © co Φ- OJ OJ I s - 1- CM 00 LO 31 " cvj Φ-- OJ φ- CM CM oo " oo OJ CO CM CO I s - CM CO CO T- I I ss -- T- CO φ- T— LO CM Φ- Φ- CD CD CM oo CD LO I s -; OJ CO 00 OJ o I s -: T- OJ - CM CO 1^ LO CO φ- CD LO φ" o CD- CO~ co ¬ CD 00 C " — Φ- oo" 00 M " CM CO — CO LO 00 CM co CM CM CM 00 CM CM 00 - I s - co co I s - o CO 00 I s - CM *φ CO CD co CO 00 I s - I s - I s - τ_ φ- CO CM σ> o CM CO Φ- LO OJ CO I s - O - CM CO Φ- LO CO I^ OO O) S r- CM CO 'φ LO CO N- CO OJ O t- CM OO φ- LO CD I s - 00 OJ o CM CM CM CM CM CM CM CM CM co oq co co oo c co co co co • ■ φ 'φ ' φ 'φ τ:| : **s ' ;1 : ■* *. * *. LO LO LO LO LO LO LO LO LO LO CO oo oo 00 CO CO 03 00 c oo oo ' oo oό oo " oo " oo ' oo co " co " °° cd oo ' co ' cd co oo oo eo ' oo cό co co co eo co co oo oo oo oo o C φ- LO CO t^ CO CO O T- CM OO φ- LO CO I^ ∞ CS O T- C C φ- LO CD I^ ω CO O T- CM C φ- LΩ CD I^ O C C C O O OO O ^ Φ- Φ- Φ- - ^ ^ Φ- Φ- Φ- L LO LO LO LO LO LO IO D O D CD CD CD CD O CO O I^ I^ I^ I^ CN CN CM CM CN CM CM CN CM CN CM CM CM CM CM CM CM CM CM CN CN CM C CM CN CN CM CM CN CN CN CM CM CN C^ o o o 3 a H U α. CM LO CO LO LO CO I s - CM T— OJ CO φ- co LO o >o co °l I s - O 00 CO CM CD CM o - T- • Φ " -- I s -- o o " OJ OO oo CO φ- M oo o O o CM • φ C J OJ CO Lf T— oo " oo " I s - Φ- - coφ oo " § OJ CD φ- I s - CO 00 CD O O φ- 00 o CO CO OJ co OJ LO o φ* T- φ O CM LO o CM : CM CO CO CO Φ* OJ I s - LO o CO CM φ- φ- I s - CM CO 00 CM CO oo τ ~ — CD Φ- 00 * """ " * "" "* ~ CM φ- CO -Φ CM " * ~ OJ CM 00 CM co φ LO Φφ-- CD I s - co OJ o oo ■ φ ■ Φ I s - 00 OJ O - cM co Φ- LO co oo σ O T- M c Φ- co oo σ CO CO co CO O co CD CO CD I s - I s - I s - I s - I s - fs- t- I s - I s - I s - OO OO OO OO OO CO CO OO OO OO OJ OJ OJ OJ OJ OJ OJ OJ OJ OJ 00 00 CO 00 00 CO 00 CO CO CO 00 00 00 00 00 00 00 00 00 00 _ _ o - LO CO I s - 00 OJ O τ- CM CO φ- LO CD |s. OO OJ O τ- CM OO φ- L CO _s- Cθ σj o h- h- |s. N. |s. . CO CO OO OO OO CO CO OO OO CO OJ OJ OJ OJ OJ OJ OJ OJ OJ OJ O O O O O O O O O O τ- 1- - τ- τ- CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CO CO CO CO OO OO CO CO CO CO CO CO CO CO CO o o o 3 a H U α. 356 8.138 YLRDQQLV 96,4 22,5% 357 8.139 ILGGLVGV 84 24,9% 358 8.140 ILFAVLSV 57 17,8% 359 8.141 LLNGFLAV 74,7 13,1%

Claims: Claims 1. A method to identify a CTL epitope comprising the steps of: (a) generating primary position specific prediction means from experimental MHC class I structural or peptide binding data; (b) identifying potentially high affinity binding epitopes by scanning a set of protein sequences and calculating the binding affinity according to the primary prediction means obtained in step (a); (c) optionally reducing the high number of peptides identified in step (b) by exclusion means based on sequence similarity; (d) generating experimental binding data for the peptides identified in step (c) or (b); (e) training one or more artificial neural networks to predict binding affinities to MHC class I, using the experimental binding data from step (d) such that the individual peptide binding data examples, weighted according to their frequency in sub-intervals in the binding affinity range of 1nM to 50,000 nM, are equally presented; and (f) estimating the binding affinity of a query peptide by testing said query peptide in each of the artificial neural networks trained in step (e) obtaining an approximate binding affinity of the query peptide from each of the artificial neural networks, and calculating the weighted average of the approximate bindings thereby obtaining the estimated binding affinity of the query peptide; the CTL epitope having a weighted average of the MHC class I binding affinity of less than 500 nM. 2. A method according to claim 1 , wherein a PSCPL is used to generate primary position specific prediction means in step (a). 3. A method according to claim 1 or 2, wherein the MHC class I structural or peptide binding data in step (a) are HLA structural or peptide binding data. 4. A method according to any of the preceding claims, wherein the query peptide originates from a protein database, the protein database being a HIV-1 or HIV-2 protein sequence database, or a part thereof. 5. A method according to any of the preceding claims, further comprising the step of: (k) determining the global conservation of the query peptide across a set of HIV protein sequences; the CTL epitope of step (k) having an MHC class I binding of less than 500 nM. 5 6. A method according to claim 5, wherein the global conservation is more than 1%. 7. A method according to any of the preceding claims, wherein the weighted average MHC class I binding is less than 100 nM. 10 8. A method according to any of claims 5-7, wherein the global conservation is more than 8% across HIV protein sequences. 9. A method according to claims 5-8, wherein the weighted average of the MHC class I binding is more than 50 nM. 15 10. A method according to any of claims the preceding claims, wherein the epitope is a HLA restricted HIV epitope. 11. A method according to any of the preceding claims, further comprising the steps of: 20 (I) modifying the CTL epitope by computationally replacing amino acids in the anchor positions; and (m) estimating the binding affinity of the modified CTL epitope of step (I) by testing said modified CTL epitope in each of the artificial neural networks trained in step (i) obtaining an approximate binding affinity of the CTL epitope from each of the artificial 25 neural networks, and calculating the weighted average of the approximate bindings thereby obtaining the estimated binding affinity of the CTL epitope; the modified CTL epitope having a predicted MHC class I binding of less than 100 nM and of less binding than the natural CTL epitope. 30 12. A CTL epitope identified by the method according to any of the preceding claims. 13. A CTL epitope according to claim 12 selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 35 12, SEQ ID NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ ID NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ ID NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ ID NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31 , SEQ ID NO: 32, SEQ ID NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41, SEQ ID NO: 42, SEQ ID NO: 43, SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ ID NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ ID NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ ID NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61 , SEQ ID NO: 62, SEQ ID NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ ID NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ ID NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ ID NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQ ID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 92, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101 SEQ ID NO 102, SEQ ID NO 103, SEQ ID NO: 104, SEQ ID NO: 105, SEQ ID NO: 106 SEQ ID NO 107, SEQ ID NO 108, SEQ ID NO: 109, SEQ ID NO: 110, SEQ ID NO: 111 SEQ ID NO 112, SEQ ID NO 113, SEQ ID NO: 114, SEQ ID NO: 115, SEQ ID NO: 116 SEQ ID NO 117, SEQ ID NO 118, SEQ ID NO: 119, SEQ ID NO: 120, SEQ ID NO: 121 SEQ ID NO 122, SEQ ID NO 123, SEQ ID NO: 124, SEQ ID NO: 125, SEQ ID NO: 126 SEQ ID NO 127, SEQ ID NO 128, SEQ ID NO: 129, SEQ ID NO: 130, SEQ ID NO: 131 SEQ ID NO 132, SEQ ID NO 133, SEQ ID NO: 134, SEQ ID NO: 135, SEQ ID NO: 136 SEQ ID NO 137, SEQ ID NO 138, SEQ ID NO: 139, SEQ ID NO: 140, SEQ ID NO: 141 SEQ ID NO 142, SEQ ID NO 143, SEQ ID NO: 144, SEQ ID NO: 145, SEQ ID NO: 146 SEQ ID NO 147, SEQ ID NO 148, SEQ ID NO: 149, SEQ ID NO: 150, SEQ ID NO: 151 SEQ ID NO 152, SEQ ID NO 153, SEQ ID NO: 154, SEQ ID NO: 155, SEQ ID NO: 156 SEQ ID NO 157, SEQ ID NO 158, SEQ ID NO: 159, SEQ ID NO: 160, SEQ ID NO: 161 SEQ ID NO: 162, SEQ ID NO 163, SEQ ID NO: 164, SEQ ID NO: 165, SEQ ID NO: 166 SEQ ID NO 167, SEQ ID NO 168, SEQ ID NO: 169, SEQ ID NO: 170, SEQ ID NO: 171 SEQ ID NO 172, SEQ ID NO 173, SEQ ID NO: 174, SEQ ID NO: 175, SEQ ID NO: 176 SEQ ID NO 177, SEQ ID NO 178, SEQ ID NO: 179, SEQ ID NO: 180, SEQ ID NO: 181 SEQ ID NO 182, SEQ ID NO 183, SEQ ID NO: 184, SEQ ID NO: 185, SEQ ID NO: 186 SEQ ID NO 187, SEQ ID NO 188, SEQ ID NO: 189, SEQ ID NO: 190, SEQ ID NO: 191 SEQ ID NO 192, SEQ ID NO 193, SEQ ID NO: 194, SEQ ID NO: 195, SEQ ID NO: 196 SEQ ID NO: 197, SEQ ID NO: 198 SEQ ID NO: 199, SEQ ID NO: 200, SEQ ID NO: 201 SEQ ID NO: 202, SEQ ID NO: 203 SEQ ID NO: 204, SEQ ID NO: 205, SEQ ID NO: 206 SEQ ID NO: 207, SEQ ID NO: 208 SEQ ID NO: 209, SEQ ID NO: 210, SEQ ID NO: 211 SEQ ID NO: 212, SEQ ID NO: 213 SEQ ID NO: 214, SEQ ID NO: 215, SEQ ID NO: 216 SEQ ID NO: 217, SEQ ID NO: 218 SEQ ID NO: 219, SEQ ID NO: 220, SEQ ID NO: 221 SEQ ID NO: 222, SEQ ID NO: 223 SEQ ID NO: 224, SEQ ID NO: 225, SEQ ID NO: 226 SEQ ID NO: 227, SEQ ID NO: 228 SEQ ID NO: 229, SEQ ID NO: 230, SEQ ID NO: 231 SEQ ID NO: 232, SEQ ID NO: 233 SEQ ID NO: 234, SEQ ID NO: 235, SEQ ID NO: 236 SEQ ID NO: 237, SEQ ID NO: 238 SEQ ID NO: 239, SEQ ID NO: 240, SEQ ID NO: 241 SEQ ID NO: 242, SEQ ID NO: 243 SEQ ID NO: 244, SEQ ID NO: 245, SEQ ID NO: 246 SEQ ID NO: 247, SEQ ID NO: 248 SEQ ID NO: 249, SEQ ID NO: 250, SEQ ID NO: 251 SEQ ID NO: 252, SEQ ID NO: 253 SEQ ID NO: 254, SEQ ID NO: 255, SEQ ID NO: 256 SEQ ID NO: 257, SEQ ID NO: 258 SEQ ID NO: 259, SEQ ID NO: 260, SEQ ID NO: 261 SEQ ID NO: 262, SEQ ID NO: 263 SEQ ID NO: 264, SEQ ID NO: 265, SEQ ID NO: 266 SEQ ID NO: 267, SEQ ID NO: 268 SEQ ID NO: 269, SEQ ID NO: 270, SEQ ID NO: 271 SEQ ID NO: 272, SEQ ID NO: 273 SEQ ID NO: 274, SEQ ID NO: 275, SEQ ID NO: 276 SEQ ID NO: 277, SEQ ID NO: 278 SEQ ID NO: 279, SEQ ID NO: 280, SEQ ID NO: 281 SEQ ID NO: 282, SEQ ID NO: 283 SEQ ID NO: 284, SEQ ID NO: 285, SEQ ID NO: 286 SEQ ID NO: 287, SEQ ID NO: 288 SEQ ID NO: 289, SEQ ID NO: 290, SEQ ID NO: 291 SEQ ID NO: 292, SEQ ID NO: 293 SEQ ID NO: 294, SEQ ID NO: 295, SEQ ID NO: 296 SEQ ID NO: 297, SEQ ID NO: 298 SEQ ID NO: 299, SEQ ID NO: 300, SEQ ID NO: 301 SEQ ID NO: 302, SEQ ID NO: 303 SEQ ID NO: 304, SEQ ID NO: 305, SEQ ID NO: 306 SEQ ID NO: 307, SEQ ID NO: 308 SEQ ID NO: 309, SEQ ID NO: 310, SEQ ID NO: 311 SEQ ID NO: 312, SEQ ID NO: 313 SEQ ID NO: 314, SEQ ID NO: 315, SEQ ID NO: 316 SEQ ID NO: 317, SEQ ID NO: 318 SEQ ID NO: 319, SEQ ID NO: 320, SEQ ID NO: 321 SEQ ID NO: 322, SEQ ID NO: 323 SEQ ID NO: 324, SEQ ID NO: 325, SEQ ID NO: 326 SEQ ID NO: 327, SEQ ID NO: 328 SEQ ID NO: 329, SEQ ID NO 330, SEQ ID NO: 331 SEQ ID NO: 332, SEQ ID NO: 333 SEQ ID NO: 334, SEQ ID NO 335, SEQ ID NO: 336 SEQ ID NO: 337, SEQ ID NO: 338 SEQ ID NO: 339, SEQ ID NO 340, SEQ ID NO: 341 SEQ ID NO: 342, SEQ ID NO: 343 SEQ ID NO: 344, SEQ ID NO: 345, SEQ ID NO: 346 SEQ ID NO: 347, SEQ ID NO: 348 SEQ ID NO: 1172, SEQ ID NO: 1173, SEQ ID NO: 1174, SEQ ID NO: 1175, SEQ ID NO: 1176, SEQ ID NO: 1177, SEQ ID NO: 1178, SEQ ID NO: 1179, SEQ ID NO: 1180, SEQ ID NO: 1181, SEQ ID NO: 1182, SEQ ID NO: 1183, SEQ ID NO: 1184, SEQ ID NO: 1185, and SEQ ID NO: 1186. 14. A CTL epitope according to any of claims 12-13 for use in medicine. 15. Use of a CTL epitope according to any of claims 12-13 for the manufacture of a vaccine. 16. Use of a CTL epitope according to any of claims 12-13 for the manufacture of a diagnostic agent. ABSTRACT The present invention relates to the identification of CTL epitopes by the combination of biochemical assays, statistical matrix calculations, and artificial neural networks. A set of peptide libraries are used to generate complete unbiased matrices representing peptide-MHC interactions used to generate a primary prediction of MHC binding for all possible non-redundant peptides. The best binders are subject to a quantitative biochemical binding assay and subsequently a computerised artificial neural network prediction program built from these in vitro experimental MHC-I binding data. The method further comprises improving the identified epitope by replacing amino acids, and testing the identified CTL epitopes in in vitro and in vivo models. Thus, one aspect of the invention relates to the identification of a CTL component of a vaccine and the development of said CTL component. Another aspect of the invention relates to the identified epitopes of said CTL component.

Inventors: BRUNAK SOEREN, BUUS SOEREN, CORBET SYLVIE, FOMSGAARD ANDERS, HANSEN JAN, LAUEMOELLER SANNE LISE, BRUNAK, SOEREN, BUUS, SOEREN, CORBET, SYLVIE, FOMSGAARD, ANDERS, HANSEN, JAN, LAUEMOELLER, SANNE, LISE
Applicants: BRUNAK SOEREN, BUUS SOEREN, CORBET SYLVIE, FOMSGAARD ANDERS, HANSEN JAN, LAUEMOELLER SANNE LISE, STATENS SERUMINSTITUT, BRUNAK, SOEREN, BUUS, SOEREN, CORBET, SYLVIE, FOMSGAARD, ANDERS, HANSEN, JAN, LAUEMOELLER, SANNE, LISE, STATENS SERUM INSTITUT
Application year: 2001
Main classification: C07K7
IPC codes: A61K38, A61K39, A61P31, C07K7, C07K14, C40B30, G01N33