Introduction The Antiretroviral Therapy ART

Introduction The Antiretroviral Therapy (ART), in the last decade, has been providing a better treatment for the Human Immunodeficiency Virus 1 (HIV) infection, by reducing its viral load to undetectable levels and recovering the immune system. In fact, according to the last data from UNAIDS the mortality related to the consequences of HIV infection decreased (Gallo et al., 1983). Nowadays, ART is based on the combination of at least 3 class of antiretroviral drugs. One of these antiretroviral drugs could be Dolutegravir (DTG), an integrase inhibitor (IN), that binds to the catalytic domain of HIV integrase enzyme (INT) and abolishes its activity, therefore avoiding the assembling of HIV BOP reagent mg into the host (Tozzi, 2010). During the reverse transcription of HIV RNA, occasionally, new mutations occur with a rate of 5.9 × 10−4 to 5.3 × 10−5 mutations/bp/cycles (Abram et al., 2014), and somehow, increase the resistance to ART (including integrase inhibitors such as DTG) by affecting their efficacy (Krishnan et al., 2010). The appearance of novel drug-resistant HIV strains has been also related to the lack of adherence to the treatment (Michaud et al., 2012). So, in the context of integrase inhibitors, such as DTG, the analyses of HIV integrase mutations, allows the identification of possible drug resistance, which may lead to a change of the drug that will be administered to a naïve patient, increasing the likelihood of a successful treatment (Barré-Sinoussi et al., 2013). All this considered, we performed as in silico study aimed at predicting the influence HIV Integrase mutations on the binding process of Dolutegravir, with the objective of creating a rationale for the use or not of this drug in patients carrying different HIV integrase mutation, thus ameliorating the follow-up and patients' quality of life.
Methods
Results
Discussion The HIV literature is overflowed by several in vitro studies focusing on the discovery and evaluation of the presence of drug-resistant strains, mainly caused by changes in key proteins of viral life cycle. Nowadays, the main tool to categorize the drug-resistant mutation levels is the HIV Stanford database. However, this database should be fed by experimental findings, with delay occurring between the release of novel medications and the information concerning the resistance against the novel drugs. Here we propose a relatively faster, inexpensive and robust method to find and describe possible novel HIV drug-resistance mutations based on computational methods. Our in silico approach is not aimed at substituting the experimental findings contained in databases such the HIV Stanford, but it is useful to complement the database information. We tested our framework using the INT. As the original structure of INT (PDB id: 5U1C) presents several issues on the atoms positions, possibly interfering with the docking results, this was caused because the protein structure is out of their cryoEM map, we noted this error downloading the original map, in the EMDataBank. Along the DTG mutations reported on the HIV Stanford database, the R263K and N155H variants have been classified as mutations with low-level of drug-resistance (Anstett et al., 2015) (25 and 10 on Stanford mutation score, respectively), whereas the Y226K mutation has never been studied before. Based on our results, we hypothesize that this mutation appears to be a drug-resistance mutation as well, since the parameters that we analyzed were more alike the R263K and N155H variants than the WT. We believe that there could be more mutations associated with resistance to DTG, however since the use of this drug is relatively recent, only a couple of variants were found until now. In conclusion, this framework has been useful to identify possible novel mutations conferring resistance to DTG, and it could be extended to other drugs and proteins. Our in silico framework could be used together with the experimental HIV Stanford database to improve the rate of drugs treatment's success in HIV patients.