Computational analysis of Human Immunodeficiency Virus (HIV) Type-1 reverse transcriptase crystallographic models based on significant conserved residues found in Highly Active Antiretroviral Therapy (HAART)-treated patients.

Reverse transcription of the viral single-stranded (+) RNA genome into double-stranded DNA is an essential step in the human immunodeficiency virus' (HIV) life-cycle. Although several viral proteins are involved in the regulation and/or efficiency of reverse transcription, the process of retroviral DNA synthesis is entirely dependent on the enzymatic activities of the retroviral reverse transcriptase enzyme (RT). Due to its crucial role in the HIV life-cycle, RT is a primary target for anti-HIV drug development. Nonetheless, drug resistance is the major problem affecting the clinical efficacy of antiretroviral agents. Incomplete pharmacological pressure represents the logical cause and not the consequence of different mutation pathways in RT associated with approved inhibitors resistance. In this review we have analyzed RT Protein Data Bank (PDB) models using our innovative computational approach "GRID Based Pharmacophore Model" (GBPM). This method was applied to clinically relevant RT conserved residues found in a large cohort of HAART treated patients. The PDB entries have been selected among the unbound and the complexed models with DNA and/or inhibitors. Such an approach has revealed itself useful to highlight the mutation effects in the drug-RT recognition as well as in the heterodimer stabilization of the enzyme. Most of the clinical and biochemical evidences already reported in the literature have been rationalized at molecular level via the GBPM computational approach. A definite future application of this method will be the identification of conserved regions of critical macromolecules, such as the HIV-1 RT, to be targeted for the development of innovative therapeutic agents.