Introduction
The ability of HIV to change rapidly, [1-5], leads directly to high levels of sequence diversity both within and between infected individuals. The extent of this diversity is the greatest impediment to producing an HIV vaccine [6]. Recently algorithmic approaches have been proposed involving the identification of high coverage fragments for inclusion in artificial sequence-constructs [7,8]. These involve the generation of multiple vaccine antigen-constructs that are optimized to maximize coverage of circulating variants within the risk group being targeted.
Our Algorithm
In Archer et al., [9] we present an algorithmic approach (Fig. 1) in order to generate artificial sequence constructs that will optimize the Cytotoxic T lymphocyte (CTL) response. Epitopes involved in the CTL response are short contiguous sequences, averaging nine residues in length (referred to as ninemers). The algorithm selects high-frequency potential epitopes in the sequence alignment and pieces them together in optimized permutations that maintain their original position within the gene. Maintaining position within the gene is important because the processibility of epitopes for presentation by MHC is partially dependent on neighboring residues [10]. Unlike previous approaches [7,8] a given number of sequence constructs guarantees a specified percentage of cover.
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Figure 1: Schematic representation of the algorithm used to make the optimised sequence-constructs. Straight arrows represent transitions between the steps of the algorithm. Circular arrows indicate where multiple iterations of a step occur. Boxes represent list objects. |
Initial Results
Using our algorithm [9] on two large HIV-1 data sets from the P17 genomic region, we derive the unique ninemer frequencies within the input datasets (Fig. 2 A and B). From these frequencies our algorithm generate construct sequences that cover the input dataset (Fig. 2C and D). Importantly this algorithm is not heuristic and the numbers generated are the minimum numbers of construct sequences possible to obtain particular levels of coverage across the input data.
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Figure 2: The distribution of nine-mers across P17 alignments for (A) 1,091 unique HIV-1 group M and (B) 1,405 unique subtype B sequences. The coverage provided by sequence-constructs for the same (C) HIV-1 group M and (D) subtype B alignments as determined by our model. |
Future Work
To be realized as a viable vaccine, the optimized sequence construct set will require site-by-site design taking into account further structural and functional knowledge, and complexities of the immune response. We demonstrate the incorporation of one possible improvement using a model described in Williams et al., [11] where low frequency residues that are likely to result in unviable viruses are removed from the underlying alignment prior to processing with construct generation (Fig. 3A and B, orange). Incorporation of such improvements will permit the systematic choice of appropriate sequence-constructs for inclusion in a polyvalent vaccine.
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Figure 3: The coverage provided by sequence-constructs for (A) the HIV-1 group M and (B) subtype B alignments for: all data (dark blue), after removal of improbable amino acids as determined by Williams et al., (orange), removal of amino acids occurring at a frequency of less than 0.5% (green), 1% (purple) and 2% (light blue). The mean coverage for a random control is shown (black); the vertical error bars include 95% of the coverage scores. The dashed black line indicates coverage provided by the alignment’s consensus sequence. Note, these are the same distributions as in Fig. 2C and D with the inclusion of percentage cut-offs. |
The Software
The applet to generate artificial constructs can be launched here.
Paste in the alignment (in fasta format) that you wish to cover for and press the Input button. This will bring up a workarea tab from which constructs and random controls can be generated. Parameters can be set using the Parameter menu. Once the sequences have been generated use the Export menu option to export the data.
Test Data
An alignment of 1000 p17 sequences from HIV-1 group M (subtype B) is available here in fasta format. The sequences were downloaded from the Los Alamos HIV sequence database. Paste the sequences into the input tab and press the input data button.
References
1. Gao, F., Y. Chen, et al. (2004). "Unselected mutations in the human immunodeficiency virus type 1 genome are mostly nonsynonymous and often deleterious." J Virol 78(5): 2426-33.
2. Robertson, D. L., B. H. Hahn, et al. (1995). "Recombination in AIDS viruses." J Mol Evol 40(3): 249-59.
3. Jetzt, A. E., H. Yu, et al. (2000). "High rate of recombination throughout the human immunodeficiency virus type 1 genome." J Virol 74(3): 1234-40.
4. Ho, D. D., A. U. Neumann, et al. (1995). "Rapid turnover of plasma virions and CD4 lymphocytes in HIV-1 infection." Nature 373(6510): 123-6.
5. Wei, X., S. K. Ghosh, et al. (1995). "Viral dynamics in human immunodeficiency virus type 1 infection." Nature 373(6510): 117-22.
6. Cohen, J. (2007). "AIDS research. Promising AIDS vaccine's failure leaves field reeling." Science 318(5847): 28-9.
7. Fischer, W., S. Perkins, et al. (2007). "Polyvalent vaccines for optimal coverage of potential T-cell epitopes in global HIV-1 variants." Nat Med 13(1): 100-6.
8. Nickle, D. C., M. Rolland, et al. (2007). "Coping with viral diversity in HIV vaccine design." PLoS Comput Biol 3(4): e75.
9. Archer et al., In Preperation.
10. Larsen, M. V., C. Lundegaard, et al. (2007). "Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction." BMC Bioinformatics 8: 424.
11. Williams et al. In Preperation.
Citing
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