Covariability of V3 loop amino acids

We reanalyzed for covariability a set of 308 human immunodeficiency virus type 1 (HIV-1) V3 loop amino acid sequences from the B envelope sequence subtype previously analyzed by Korber et al.,1 as well as a new set of 440 sequences that also included substantial numbers of sequences from subtypes A, D, and E. We used the measure employed by Korber et al., essentially the likelihood ratio statistic for independence, plus two additional measures as well as clade information to examine the new set and both data sets simultaneously. We set forth the following conclusions and observations. The eight most highly connected sites identified through these statistical approaches included all of the six residues previously shown to have determining roles in structure, immunologic recognition, virus phenotype, and host range; each of the seven pairs of covariant sites found by Korber were signaled by our additional two measures in the set of 308 sequences, although 2 or 3 dropped out of the examination of the set of 440 when the requirement of stringent significance was applied for some or all of the three tests, respectively; using the same criteria, a total of 20 (including 5 Korber et al. pairs) or a total of 6 (including 4 Korber et al. pairs) were found when the set of 440 was added. Several limitations to statistical analysis of this type of HIV sequence data were also noted. For example, the data sets were, by historical necessity, collected haphazardly. For example, it was not possible to separate substantially sized groups out according to time of or since infection, disease status, antiviral treatment, geography, etc. There was also an enormous "wealth of significance" within the data. For example, for one measure the 440 data set showed 233 of the 465 pairs of sites with a likelihood ratio statistic of < 0.001. Last, most sites had consensus amino acids in 80% or more of the sequences; hence, there was an absence of data on many combinations of amino acids. Given the observed linkage between sites shown to be covariable and those known to have critical biological function, the statistical approaches we and Korber et al. have outlined may find use in predicting critical structural features of HIV proteins as targets for therapeutic intervention.