Etermine the potential immunogenicity [21]. Other methods, such as artificial neural networksEtermine the potential immunogenicity

Etermine the potential immunogenicity [21]. Other methods, such as artificial neural networks
Etermine the potential immunogenicity [21]. Other methods, such as artificial neural networks [27] and hidden Markov models [28], also have limitations, such as adjustable values whose optimal values are hard to find initially, over fitting, overtraining and interpreting [29]. For example, in a study by Anderson et al. (2000) on experimental binding of 84 peptides to class I MHC molecules [30], there was no correlation between predictedversus experimental binding, and a high possibility of false-negatives. Thus, in this study we develop a novel strategy to identify best epitope candidates for multiepitope vaccines from the pool of experimentally wellsupported epitopes based on the association-rule mining technique. Briefly, an association rule mining technique, which is a method that can detect association between items (frequent item sets) and formulate conditional implication rules among them [31-33], is used to examine relationships between 218 “best-defined” CTL epitopes (from the list of Frahm, Linde Brander, 2007 [26]). Our results show that some CTL epitopes PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/26080418 are significantly associated with each other so that they co-occur together in the majority of the reference viral genomes including circulating recombinant forms. At least 23 association rules were identified that involve CTL epitopes from 3 different genes, Gag, Pol and Nef, respectively. We also identified several combinations of 3 to 5 CTL epitopes that are AcadesineMedChemExpress AICA Riboside frequently found together in the same viral genome despite high mutation and recombination rates found in HIV-1 genomes, and thus, can be used as likely candidates for multi-epitope vaccine development.Materials and methodsHIV-1 genomic sequence data and alignment Genomic nucleotide sequences of 9 protein-coding genes of HIV-1 were collected for 62 HIV-1 reference genomes from the 2005 subtype reference set of the HIV sequence database by Los Alamos National Laboratory (LANL) [34,35] (Table 1). These included 44 non-recombinant sequences from the groups M, N and O, and 18 circulating recombinant forms (CRFs). The M group was comprised of representatives of sub-subtypes A1, A2, F1 and F2, and subtypes B, C, D, G, H, J, K, respectively, of approximately 4 representative sequences from each category. This set of sequences was chosen since they allowed the diversity of each subtype to be roughly the same as for all available sequences in the database, similar to an effective population size. Moreover, they had full length genomes that covered all genes and major geographical regions (for criteria of selection of reference sequences, refer to [35]). Inclusion of CRFs allowed us to identify those highly conserved CTL epitopes that are shared between non-recombinant genomes and are also present in the majority of the recombinant reference genomes. Viral sequences were aligned at the nucleotide level as per amino PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/26080418 acid alignment reconstructed with ClustalW, and were manually checked afterwards [36].The summary of the average numbers of breakpoints in the CRF genomes was based on the breakpoint maps summarized at the HIV database at Los Alamos [37].Page 2 of(page number not for citation purposes)Retrovirology 2009, 6:http://www.retrovirology.com/content/6/1/Table 1: List of 62 HIV-1 reference sequences (including 44 non-recombinant sequences, grouped by subtypes, and 18 circulating recombinant forms (CRFs) included in the study (2005 subtype reference set of the HIV sequence database, Los Alamos National Laboratory).Subtype AS.