The ability to determine coreceptor usage of patient-derived human immunodeficiency virus type 1 (HIV-1) strains is clinically important, particularly for the administration of the CCR5 antagonist maraviroc. The envelope glycoprotein (Env) determinants of coreceptor specificity lie primarily within the gp120 V3 loop region, although other Env determinants have been shown to influence gp120-coreceptor interactions. Here, we determined whether conserved amino acid alterations outside the V3 loop that contribute to coreceptor usage exist, and whether these alterations improve the performance of V3 sequence-based coreceptor usage prediction algorithms. We demonstrate a significant covariant association between charged amino acids at position 322 in V3 and position 440 in the C4 Env region that contributes to the specificity of HIV-1 subtype B strains for CCR5 or CXCR4. Specifically, positively charged Lys/Arg at position 322 and negatively charged Asp/Glu at position 440 occurred more frequently in CXCR4-using viruses, whereas negatively charged Asp/Glu at position 322 and positively charged Arg at position 440 occurred more frequently in R5 strains. In the context of CD4-bound gp120, structural models suggest that covariation of amino acids at Env positions 322 and 440 has the potential to alter electrostatic interactions that are formed between gp120 and charged amino acids in the CCR5 N-terminus. We further demonstrate that inclusion of a 440 rule can improve the sensitivity of several V3 sequence-based genotypic algorithms for predicting coreceptor usage of subtype B HIV-1 strains, without compromising specificity, and significantly improves the AUROC of the geno2pheno algorithm when set to its recommended false positive rate of 5.75 . Together, our results provide further mechanistic insights into the intra-molecular interactions within Env that contribute to coreceptor specificity of subtype B HIV-1 strains, and demonstrate that incorporation of Env determinants outside V3 can improve the reliability of coreceptor usage prediction algorithms.