The ability of HIV type 1 to use CCR-3 as a coreceptor is controlled by envelope V1/V2 sequences acting in conjunction with a CCR-5 tropic V3 loop

Although infection by primary HIV type 1 (HIV-1) isolates normally requires the functional interaction of the viral envelope protein with both CD4 and the CCR-5 coreceptor, a subset of such isolates also are able to use the distinct CCR-3 receptor. By analyzing the ability of a series of wild-type and chimeric HIV-1 envelope proteins to mediate CCR-3-dependent infection, we have determined that CCR-3 tropism maps to the V1 and V2 variable region of envelope. Although substitution of the V1/V2 region of a CCR-3 tropic envelope into the context of a CCR-5 tropic envelope is both necessary and sufficient to confer CCR-3 tropism, this same substitution has no phenotypic effect when inserted into a CXCR-4 tropic HIV-1 envelope context. However, this latter chimera acquires both CCR-3 and CCR-5 tropism when a CCR-5 tropic V3 loop sequence also is introduced. These data demonstrate that the V1/2 region of envelope can, like the V3 loop region, encode a particular coreceptor requirement and suggest that a functional envelope:CCR-3 interaction may depend on the cooperative interaction of CCR-3 with both the V1/V2 and the V3 region of envelope.

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.