Structural analyses of 2015-updated drug-resistant mutations in HIV-1 protease: an implication of protease inhibitor cross-resistance

Abstract

BackgroundStrategies to control HIV for improving the quality of patient lives have been aided by the Highly Active Anti-Retroviral Therapy (HAART), which consists of a cocktail of inhibitors targeting key viral enzymes. Numerous new drugs have been developed over the past few decades but viral resistances to these drugs in the targeted viral enzymes are increasingly reported. Nonetheless the acquired mutations often reduce viral fitness and infectivity. Viral compensatory secondary-line mutations mitigate this loss of fitness, equipping the virus with a broad spectrum of resistance against these drugs. While structural understanding of the viral protease and its drug resistance mutations have been well established, the interconnectivity and development of structural cross-resistance remain unclear. This paper reports the structural analyses of recent clinical mutations on the drug cross-resistance effects from various protease and protease inhibitors (PIs) complexes.MethodsUsing the 2015 updated clinical HIV protease mutations, we constructed a structure-based correlation network and a minimum-spanning tree (MST) based on the following features: (i) topology of the PI-binding pocket, (ii) allosteric effects of the mutations, and (iii) protease structural stability.Results and conclusionAnalyis of the network and the MST of dominant mutations conferring resistance to the seven PIs (Atazanavir-ATV, Darunavir-DRV, Indinavir-IDV, Lopinavir-LPV, Nelfinavir-NFV, Saquinavir-SQV, and Tipranavir-TPV) showed that cross-resistance can develop easily across NFV, SQV, LPV, IDV, and DRV, but not for ATV or TPV. Through estimation of the changes in vibrational entropies caused by each reported mutation, some secondary mutations were found to destabilize protease structure. Our findings provide an insight into the mechanism of PI cross-resistance and may also be useful in guiding the selection of PI in clinical treatment to delay the onset of cross drug resistance.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1372-3) contains supplementary material, which is available to authorized users.

Evolution of Protease Inhibitor Resistance in Human Immunodeficiency Virus Type 1 Infected Patients Failing Protease Inhibitor Monotherapy as Second-line Therapy in Low-income Countries: An Observational Analysis Within the EARNEST Randomized Trial

“…We found that VL increased only modestly after failure, driven mostly by low adherence and development of PI mutations. Our finding of an initial relatively large increase in VL followed by a plateau in replication after I47A development is supported by in vitro evidence [33,35]; contrary to this literature, we did not find a difference in virus that also had the V32I mutation, although numbers were small. Other PI mutations had comparatively modest effects as observed elsewhere [36], although not all studies have observed this effect [10].…”

“…I47A was often followed by V32I, and developing I47A was associated with a lower odds of developing I54V and vice versa. This has important consequences for third-line ART, as I47A confers intermediate-level resistance to darunavir and V32I is suggested as a key mutation for high-level resistance [32,33], whereas I54V is not known to affect darunavir response [13]. We also frequently observed the V82A mutation¸ thought to improve susceptibility to darunavir [34].…”

The Determination of HIV-1 RT Mutation Rate, Its Possible Allosteric Effects, and Its Implications on Drug Resistance

“…Differing from RT, symmetrical allosteric communications between domains in HIV-1 integrase and protease are found [95,96], probably resulting from the homo-multimerization (Figure 2A). Mutations on the C-terminal domain (CTD, residues 220-221, 230-232, and 211-217) of integrase and at the "ears" regions of protease (residues 33-45 and 57-63, of which residues L33, E34, and M36 were reported to be resistant to several protease inhibitors [79]) have been found to affect the active sites of the two enzymes ( Figure 2B).…”

Bioinformatics and systems biology research update from the 15th International Conference on Bioinformatics (InCoB2016)

“…By analysing the standard Barton502 dataset using support vector regression on informative physicochemical properties, Liou et al [17] show that an aligned row (called spline) of solventexposed hydrophobic residues actually stabilise ɑ-helices in proteins. Su et al [18] have used structural bioinformatics approaches to correlate recent clinically identified mutation in HIV-1 protease with different protease inhibitors used as drugs. Their results provide an insight into the mechanism for HIV-1 drug resistance and suggest a protease inhibitor drug may selection strategy for clinical applications.…”