To understand the structural basis for
To understand the structural basis for the mode of action of indole-based compounds the crystal structures of bound to both the wild type and A128T IN CCDs have been solved. The X-ray crystallography studies have revealed that binds at the IN dimer interface in the principal LEDGF/p75 binding pocket (). Similar to its quinoline-based counterparts, is anchored to the protein through the hydrogen bonding interactions between the carboxylic ANA 12 Supplier moiety and the backbone amides of Glu170 and His171 of subunit 2 with the -butyl ether oxygen atom providing additional hydrogen bonding to Thr174 (A)., At the same time, comparison of the binding sites of indole-based and quinoline-based BI-1001 (B) reveals important differences between these compounds. The quinoline ring system of BI-1001 extends toward the Ala128 residue in subunit 1 of IN. Accordingly, the replacement of Ala 128 with bulkier and polar Thr creates steric and electrostatic repulsions to BI-1001, and results in repositioning of BI-1001 away from subunit 1 as demonstrated in previous X-ray crystallographic studies. In contrast, very similar binding orientations of have been observed in both the wild type and A128T CCD-CCD (C) crystal structures. As predicted by the computational docking model, the five-membered indole ring structure of mitigates the repulsion induced by the A128T mutation by tilting the aromatic ring away from the 128 residue of subunit 1, thus maintaining similar activities against both the WT and A128T proteins.
Introduction According to the World Health Organization (WHO), 36.7 million (30.8 million–42.9 million) people were living with HIV in 2016 and 1.8 million (1.6 million–2.1 million) of them were newly infected (Word Health Organization, 2016). HIV prevalence in Iran is classified as being concentrated, because the prevalence of infection among injecting drug users is 13.8% (National AIDS Committee Secretariat, Ministry of Health and Medical Education, 2015). On the other hand, sexual transmission of HIV is growing in recent years and its prevalence among Iranian female sex workers has reached 4.5%. However, this prevalence is less than other risk groups and is estimated to be 0.14% in general population for the age range 15–49, in 2014 (National AIDS Committee Secretariat, Ministry of Health and Medical Education, 2015). It has been shown that the common circulating genotype of HIV in Iran is CRF35_AD, which is an uncommon genotype around the world (Jahanbakhsh et al., 2013a; Jahanbakhsh et al., 2015). Despite the comprehensive prevention programs that are running throughout Iran, there is no reliable tool to evaluate the impact of the programs on HIV infection. The prevalence data is not useful for this purpose, because HIV prevalence is confounded directly by population changes (survival, migration, birth rate) (UNAIDS/WHO Working Group on Global HIV/AIDS and STI Surveillance, 2011). While HIV prevalence is an index of people living with virus, HIV incidence is the rate of new infection in a defined period of time, usually in 1 year. Changes in the HIV incidence reflect the rate of transmission in a defined population; so the data related to HIV incidence can be used for real time evaluation of the impacts of preventive measures. One of the simple and inexpensive ways for evaluating HIV incidence is by use of laboratory tests discriminating recent and non-recent HIV infected sera. In the past two decades researchers have focused on developing such assays and have applied different approaches worldwide for this purpose. The first approach was the application of detuned (less-sensitive) enzyme immuno assays (Janssen et al., 1998). This method is based on modification of commercial HIV antibody detection assays for discrimination of recent seroconversion. Using a high dilution of sera and shortening the incubation time allows the detection of high avidity and high titer antibodies in non-recent HIV infected individuals and, consequently, the discrimination of recent and non-recent samples. The main problem with the less sensitive method was different window periods in different subtypes which led to complication of interpreting data (Parekh et al., 2001a; Novitsky et al., 2009). BED-EIA (Parekh et al., 2001b) and LAg-avidity (Duong et al., 2012) are two commercially available kits, developed by Centers for Disease Control and Prevention (CDC), which are commonly used for estimation of HIV incidence in cross sectional samples (Xu et al., 2010; Hall et al., 2008; Simmons et al., 2016).