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Human leukocyte antigens (HLA) are the most polymorphic human proteins, with over 1300 distinct alleles (varieties) defined to date. At the individual level, the inheritance of specific HLA alleles shape the immune response in a manner that is analogous to antiretroviral drugs, in that each allele provides a specific selective pressure which may induce characteristic HIV escape mutations. At the population level, this extreme HLA diversity acts as a significant impediment to the uncontrolled spread of pathogenic viruses. Importantly, HLA diversity has evolved in the presence of different endemic and epidemic infections in different ethnic/racial populations, which is reflected in (1) differing frequency of HLA alleles according to ethnic/racial origin; and (2) non-random frequency distribution of HLA within these populations. HIV has an almost unprecedented ability to adapt rapidly to HLA-restricted immune responses both within an individual and at a population level. This enormous HIV diversity has proven the major stumbling block to HIV vaccine design. On the other hand, this capacity for HIV genetic mutation and recombination is so great that it is possible to analyse HLA-viral mutation associations at the single amino acid level and we have been able to exploit this predictable relationship for vaccine design and evaluation Here we show that knowledge of adaptation to HLA-restricted immune responses at a population level in chronically infected patients may be exploited to predict protective responses to a vaccine in a population with similar HLA diversity exposed to a similar range of HIV diversity. Importantly, the innate advantage provided by intense human HLA diversity can then be exploited to ameliorate problems posed by HIV diversity. These observations provide optimism that analysis of HLA and HIV diversity within vaccinee populations can facilitate vaccine design and ranking of the likely efficacy of potential vaccine candidates in different populations. Vaccine candidates from around the world are being evaluated by this approach providing a nice example of the application of the power of computing, mathematics and genetics to one of our most pressing global health challenges.