Binding of Clinical Inhibitors to a Model Precursor of a Rationally Selected Multidrug Resistant HIV-1 Protease Is Significantly Weaker Than That to the Released Mature Enzyme

Viral proteases as therapeutic targets

Some medically important viruses-including retroviruses, flaviviruses, coronaviruses, and herpesviruses-code for a protease, which is indispensable for viral maturation and pathogenesis. Viral protease inhibitors have become an important class of antiviral drugs. Development of the first-in-class viral protease inhibitor saquinavir, which targets HIV protease, started a new era in the treatment of chronic viral diseases. Combining several drugs that target different steps of the viral life cycle enables use of lower doses of individual drugs (and thereby reduction of potential side effects, which frequently occur during long term therapy) and reduces drug-resistance development. Currently, several HIV and HCV protease inhibitors are routinely used in clinical practice. In addition, a drug including an inhibitor of SARS-CoV-2 main protease, nirmatrelvir (co-administered with a pharmacokinetic booster ritonavir as Paxlovid®), was recently authorized for emergency use. This review summarizes the basic features of the proteases of human immunodeficiency virus (HIV), hepatitis C virus (HCV), and SARS-CoV-2 and discusses the properties of their inhibitors in clinical use, as well as development of compounds in the pipeline.

Precursors of Viral Proteases as Distinct Drug Targets

Viral proteases are indispensable for successful virion maturation, thus making them a prominent drug target. Their enzyme activity is tightly spatiotemporally regulated by expression in the precursor form with little or no activity, followed by activation via autoprocessing. These cleavage events are frequently triggered upon transportation to a specific compartment inside the host cell. Typically, precursor oligomerization or the presence of a co-factor is needed for activation. A detailed understanding of these mechanisms will allow ligands with non-canonical mechanisms of action to be designed, which would specifically modulate the initial irreversible steps of viral protease autoactivation. Binding sites exclusive to the precursor, including binding sites beyond the protease domain, can be exploited. Both inhibition and up-regulation of the proteolytic activity of viral proteases can be detrimental for the virus. All these possibilities are discussed using examples of medically relevant viruses including herpesviruses, adenoviruses, retroviruses, picornaviruses, caliciviruses, togaviruses, flaviviruses, and coronaviruses.

Revertant mutation V48G alters conformational dynamics of highly drug resistant HIV protease PRS17

Drug resistance is a serious problem for controlling the HIV/AIDS pandemic. Current antiviral drugs show several orders of magnitude worse inhibition of highly resistant clinical variant PRS17 of HIV-1 protease compared with wild-type protease. We have analyzed the effects of a common resistance mutation G48V in the flexible flaps of the protease by assessing the revertant PRS17_V48G for changes in enzyme kinetics, inhibition, structure, and dynamics. Both PRS17 and the revertant showed about 10-fold poorer catalytic efficiency than wild-type enzyme (0.55 and 0.39 μM^-1min^-1 compared to 6.3 μM^-1min^-1). Clinical inhibitors, amprenavir and darunavir, showed 2-fold and 8-fold better inhibition, respectively, of the revertant than of PRS17, although the inhibition constants for PRS17_V48G were still 25 to 1,200-fold worse than for wild-type protease. Crystal structures of inhibitor-free revertant and amprenavir complexes with revertant and PRS17 were solved at 1.3-1.5 Å resolution. The amprenavir complexes of PRS17_V48G and PRS17 showed no significant differences in the interactions with inhibitor, although changes were observed in the conformation of Phe53 and the interactions of the flaps. The inhibitor-free structure of the revertant showed flaps in an open conformation, however, the flap tips do not have the unusual curled conformation seen in inhibitor-free PRS17. Molecular dynamics simulations were run for 1 μs on the two inhibitor-free mutants and wild-type protease. PRS17 exhibited higher conformational fluctuations than the revertant, while the wild-type protease adopted the closed conformation and showed the least variation. The second half of the simulations captured the transition of the flaps of PRS17 from a closed to a semi-open state, whereas the flaps of PRS17_V48G tucked into the active site and the wild-type protease retained the closed conformation. These results suggest that mutation G48V contributes to drug resistance by altering the conformational dynamics of the flaps.

Novel HIV PR inhibitors with C4-substituted bis-THF and bis-fluoro-benzyl target the two active site mutations of highly drug resistant mutant PRS17

The emergence of multidrug resistant (MDR) HIV strains severely reduces the effectiveness of antiretroviral therapy. Clinical inhibitor darunavir (1) has picomolar binding affinity for HIV-1 protease (PR), however, drug resistant variants like PR^S17 show poor inhibition by 1, despite the presence of only two mutated residues in the inhibitor-binding site. Antiviral inhibitors that target MDR proteases like PR^S17 would be valuable as therapeutic agents. Inhibitors 2 and 3 derived from 1 through substitutions at P1, P2 and P2' positions exhibit 3.4- to 500-fold better inhibition than clinical inhibitors for PR^S17 with the exception of amprenavir. Crystal structures of PR^S17/2 and PR^S17/3 reveal how these inhibitors target the two active site mutations of PR^S17. The substituted methoxy P2 group of 2 forms new interactions with G48V mutation, while the modified bis-fluoro-benzyl P1 group of 3 forms a halogen interaction with V82S mutation, contributing to improved inhibition of PR^S17.

HIV Protease: Historical Perspective and Current Research

The retroviral protease of human immunodeficiency virus (HIV) is an excellent target for antiviral inhibitors for treating HIV/AIDS. Despite the efficacy of therapy, current efforts to control the disease are undermined by the growing threat posed by drug resistance. This review covers the historical background of studies on the structure and function of HIV protease, the subsequent development of antiviral inhibitors, and recent studies on drug-resistant protease variants. We highlight the important contributions of Dr. Stephen Oroszlan to fundamental knowledge about the function of the HIV protease and other retroviral proteases. These studies, along with those of his colleagues, laid the foundations for the design of clinical inhibitors of HIV protease. The drug-resistant protease variants also provide an excellent model for investigating the molecular mechanisms and evolution of resistance.

Acquired HIV-1 Protease Conformational Flexibility Associated with Lopinavir Failure May Shape the Outcome of Darunavir Therapy after Antiretroviral Therapy Switch

Understanding the underlying molecular interaction during a therapy switch from lopinavir (LPV) to darunavir (DRV) is essential to achieve long-term virological suppression. We investigated the kinetic and structural characteristics of multidrug-resistant South African HIV-1 subtype C protease (HIV-1 PR) during therapy switch from LPV to DRV using enzyme activity and inhibition assay, fluorescence spectroscopy, and molecular dynamic simulation. The HIV-1 protease variants were from clinical isolates with a combination of drug resistance mutations; MUT-1 (M46I, I54V, V82A, and L10F), MUT-2 (M46I, I54V, L76V, V82A, L10F, and L33F), and MUT-3 (M46I, I54V, L76V, V82A, L90M, and F53L). Enzyme kinetics analysis shows an association between increased relative resistance to LPV and DRV with the progressive decrease in the mutant HIV-1 PR variants' catalytic efficiency. A direct relationship between high-level resistance to LPV and intermediate resistance to DRV with intrinsic changes in the three-dimensional structure of the mutant HIV-1 PR as a function of the multidrug-resistance mutation was observed. In silico analysis attributed these structural adjustments to the multidrug-resistance mutations affecting the LPV and DRV binding landscape. Though DRV showed superiority to LPV, as a lower concentration was needed to inhibit the HIV-1 PR variants, the inherent structural changes resulting from mutations selected during LPV therapy may dynamically shape the DRV treatment outcome after the therapy switch.

Spontaneous Mutations in HIV-1 Gag, Protease, RT p66 in the First Replication Cycle and How They Appear: Insights from an In Vitro Assay on Mutation Rates and Types

While drug resistant mutations in HIV-1 are largely credited to its error prone HIV-1 RT, the time point in the infection cycle that these mutations can arise and if they appear spontaneously without selection pressures both remained enigmatic. Many HIV-1 RT mutational in vitro studies utilized reporter genes (LacZ) as a template to investigate these questions, thereby not accounting for the possible contribution of viral codon usage. To address this gap, we investigated HIV-1 RT mutation rates and biases on its own Gag, protease, and RT p66 genes in an in vitro selection pressure free system. We found rare clinical mutations with a general avoidance of crucial functional sites in the background mutations rates for Gag, protease, and RT p66 at 4.71 × 10-5, 6.03 × 10-5, and 7.09 × 10-5 mutations/bp, respectively. Gag and p66 genes showed a large number of 'A to G' mutations. Comparisons with silently mutated p66 sequences showed an increase in mutation rates (1.88 × 10-4 mutations/bp) and that 'A to G' mutations occurred in regions reminiscent of ADAR neighbor sequence preferences. Mutational free energies of the 'A to G' mutations revealed an avoidance of destabilizing effects, with the natural p66 gene codon usage providing barriers to disruptive amino acid changes. Our study demonstrates the importance of studying mutation emergence in HIV genes in a RT-PCR in vitro selection pressure free system to understand how fast drug resistance can emerge, providing transferable applications to how new viral diseases and drug resistances can emerge.

Highly drug-resistant HIV-1 protease reveals decreased intra-subunit interactions due to clusters of mutations

Drug-resistance is a serious problem for treatment of the HIV/AIDS pandemic. Potent clinical inhibitors of HIV-1 protease show several orders of magnitude worse inhibition of highly drug-resistant variants. Hence, the structure and enzyme activities were analyzed for HIV protease mutant HIV-1 protease (EC 3.4.23.16) (PR) with 22 mutations (PRS5B) from a clinical isolate that was selected by machine learning to represent high-level drug-resistance. PRS5B has 22 mutations including only one (I84V) in the inhibitor binding site; however, clinical inhibitors had poor inhibition of PRS5B activity with kinetic inhibition value (K_i ) values of 4-1000 nm or 18- to 8000-fold worse than for wild-type PR. High-resolution crystal structures of PRS5B complexes with the best inhibitors, amprenavir (APV) and darunavir (DRV) (K_i ~ 4 nm), revealed only minor changes in protease-inhibitor interactions. Instead, two distinct clusters of mutations in distal regions induce coordinated conformational changes that decrease favorable internal interactions across the entire protein subunit. The largest structural rearrangements are described and compared to other characterized resistant mutants. In the protease hinge region, the N83D mutation eliminates a hydrogen bond connecting the hinge and core of the protease and increases disorder compared to highly resistant mutants PR with 17 mutations and PR with 20 mutations with similar hinge mutations. In a distal β-sheet, mutations G73T and A71V coordinate with accessory mutations to bring about shifts that propagate throughout the subunit. Molecular dynamics simulations of ligand-free dimers show differences consistent with loss of interactions in mutant compared to wild-type PR. Clusters of mutations exhibit both coordinated and antagonistic effects, suggesting PRS5B may represent an intermediate stage in the evolution of more highly resistant variants. DATABASES: Structural data are available in Protein Data Bank under the accession codes 6P9A and 6P9B for PRS5B/DRV and PRS5B/APV, respectively.

Potent antiviral HIV-1 protease inhibitor combats highly drug resistant mutant PR20

Drug-resistance threatens effective treatment of HIV/AIDS. Clinical inhibitors, including darunavir (1), are ineffective for highly resistant protease mutant PR20, however, antiviral compound 2 derived from 1 with fused tricyclic group at P2, extended amino-benzothiazole P2' ligand and two fluorine atoms on P1 shows 16-fold better inhibition of PR20 enzyme activity. Crystal structures of PR20 and wild-type PR complexes reveal how the extra groups of 2 counteract the expanded ligand-binding pocket, dynamic flaps, and faster dimer dissociation of PR20.

Reviewing HIV-1 Gag Mutations in Protease Inhibitors Resistance: Insights for Possible Novel Gag Inhibitor Designs

HIV protease inhibitors against the viral protease are often hampered by drug resistance mutations in protease and in the viral substrate Gag. To overcome this drug resistance and inhibit viral maturation, targeting Gag alongside protease rather than targeting protease alone may be more efficient. In order to successfully inhibit Gag, understanding of its drug resistance mutations and the elicited structural changes on protease binding needs to be investigated. While mutations on Gag have already been mapped to protease inhibitor resistance, there remain many mutations, particularly the non-cleavage mutations, that are not characterized. Through structural studies to unravel how Gag mutations contributes to protease drug resistance synergistically, it is thus possible to glean insights to design novel Gag inhibitors. In this review, we discuss the structural role of both novel and previously reported Gag mutations in PI resistance, and how new Gag inhibitors can be designed.

Highly Drug-Resistant HIV-1 Protease Mutant PRS17 Shows Enhanced Binding to Substrate Analogues

We report the structural analysis of highly drug-resistant human immunodeficiency virus protease (PR) variant PR^S17, rationally selected by machine learning, in complex with substrate analogues. Crystal structures were solved of inhibitor-free inactive PR^S17-D25N, wild-type PR/CA-p2 complex, and PR^S17 in complex with substrate analogues, CA-p2 and p2-NC. Peptide analogues p2-NC and CA-p2 exhibit inhibition constants of 514 and 22 nM, respectively, for PR^S17 or approximately 3-fold better than for PR. CA-p2 is a better inhibitor of PR^S17 than are clinical inhibitors (K _i = 50-8390 nM) except for amprenavir (K _i = 11 nM). G48V resistance mutation induces curled flap tips in PR^S17-D25N structure. The inner P2-P2' residues of substrate analogues in PR^S17 complexes maintain similar conformations to those of wild-type complex, while significant conformational changes are observed in the peripheral residues P3, P4' of CA-p2 and P3, P4, and P3' of p2-NC. The loss of β-branched side chain by V82S mutation initiates a shift in 80's loop and reshapes the S3/S3' subsite, which enhances substrate binding with new hydrogen bonds and van der Waals interactions that are absent in the wild-type structures. The steric hindrance caused by G48V mutation in the flap of PR^S17 contributes to altered binding interactions of P3 Arg, P4' norleucine of CA-p2, and P4 and P3' of p2-NC with the addition of new hydrogen bonds and van der Waals contacts. The enhanced interaction of PR^S17 with substrate analogues agrees with their relative inhibition, suggesting that this mutant improves substrate binding while decreasing affinity for clinical inhibitors.

Exploiting the unique features of Zika and Dengue proteases for inhibitor design

Zika and Dengue viruses have attracted substantial attention from researchers in light of recent outbreaks of Dengue fever and increases in cases of congenital microcephaly in areas with Zika incidence. This review summarizes the current state of knowledge about Zika and Dengue proteases. These enzymes have several interesting features: 1) NS3 serine protease requires the activating co-factor NS2B, which is anchored in the membrane of the endoplasmic reticulum; 2) NS2B displays extensive conformational dynamics; 3) NS3 is a multidomain protein with proteolytic, NTPase, RNA 5' triphosphatase and helicase activity and has many protein-protein interaction partners; 4) NS3 is autoproteolytically released from its precursor. Attempts to design tight-binding and specific active-site inhibitors are complicated by the facts that the substrate pocket of the NS2B-NS3 protease is flat and the active-site ligands are charged. The ionic character of potential active-site inhibitors negatively influences their cell permeability. Possibilities to block cis-autoprocessing of the protease precursor have recently been considered. Additionally, potential allosteric sites on NS2B-NS3 proteases have been identified and allosteric compounds have been designed to impair substrate binding and/or block the NS2B-NS3 interaction. Such compounds could be specific to viral proteases, without off-target effects on host serine proteases, and could have favorable pharmacokinetic profiles. This review discusses various groups of inhibitors of these proteases according to their mechanisms of action and chemical structures.

Analysis of drug resistance in HIV protease

BACKGROUND

Drug resistance in HIV is the major problem limiting effective antiviral therapy. Computational techniques for predicting drug resistance profiles from genomic data can accelerate the appropriate choice of therapy. These techniques can also be used to select protease mutants for experimental studies of resistance and thereby assist in the development of next-generation therapies.

RESULTS

The machine learning produced highly accurate and robust classification of HIV protease resistance. Genotype data were mapped to the enzyme structure and encoded using Delaunay triangulation. Generative machine learning models trained on one inhibitor could classify resistance from other inhibitors with varying levels of accuracy. Generally, the accuracy was best when the inhibitors were chemically similar.

CONCLUSIONS

Restricted Boltzmann Machines are an effective machine learning tool for classification of genomic and structural data. They can also be used to compare resistance profiles of different protease inhibitors.

Inhibition of the precursor and mature forms of HIV-1 protease as a tool for drug evaluation

HIV-1 protease (PR) is a homodimeric enzyme that is autocatalytically cleaved from the Gag-Pol precursor. Known PR inhibitors bind the mature enzyme several orders of magnitude more strongly than the PR precursor. Inhibition of PR at the precursor level, however, may stop the process at its rate-limiting step before the proteolytic cascade is initiated. Due to its structural heterogeneity, limited solubility and autoprocessing, the PR precursor is difficult to access by classical methods, and limited knowledge regarding precursor inhibition is available. Here, we describe a cell-based assay addressing precursor inhibition. We used a reporter molecule containing the transframe (TFP) and p6* peptides, PR, and N-terminal fragment of reverse transcriptase flanked by the fluorescent proteins mCherry and EGFP on its N- and C- termini, respectively. The level of FRET between EGFP and mCherry indicates the amount of unprocessed reporter, allowing specific monitoring of precursor inhibition. The inhibition can be quantified by flow cytometry. Additionally, two microscopy techniques confirmed that the reporter remains unprocessed within individual cells upon inhibition. We tested darunavir, atazanavir and nelfinavir and their combinations against wild-type PR. Shedding light on an inhibitor’s ability to act on non-mature forms of PR may aid novel strategies for next-generation drug design.

Context-dependent autoprocessing of human immunodeficiency virus type 1 protease precursors

HIV-1 protease autoprocessing is responsible for liberation of free mature protease (PR) from the Gag-Pol polyprotein precursor. A cell-based model system was previously developed to examine the autoprocessing mechanism of fusion precursors carrying the p6*-PR miniprecursor sandwiched between various proteins or epitopes. We here report that precursor autoprocessing is context-dependent as its activity and outcomes can be modulated by sequences upstream of p6*-PR. This was exemplified by the 26aa maltose binding protein (MBP) signal peptide (SigP) when placed at the N-terminus of a fusion precursor. The mature PRs released from SigP-carrying precursors are resistant to self-degradation whereas those released from SigP-lacking fusion precursors are prone to self-degradation. A H69D mutation in PR abolished autoprocessing of SigP-containing fusion precursors whereas it only partially suppressed autoprocessing of fusion precursors lacking SigP. An autoprocessing deficient GFP fusion precursor with SigP exhibited a subcellular distribution pattern distinct from the one without it in transfected HeLa cells. Furthermore, a SigP fusion precursor carrying a substitution at the P1 position released the mature PR and PR-containing fragments that were different from those released from the precursor carrying the same mutation but lacking SigP. We also examined autoprocessing outcomes in viral particles produced by a NL4-3 derived proviral construct and demonstrated the existence of several PR-containing fragments along with the mature PR. Some of these resembled the SigP precursor autoprocessing outcomes. This finding of context-dependent modulation reveals the complexity of precursor autoprocessing regulation that most likely accompanies sequence variation imposed by the evolution of the upstream Gag moiety.

I36T↑T mutation in South African subtype C (C-SA) HIV-1 protease significantly alters protease-drug interactions

The efficacy of HIV-1 protease (PR) inhibition therapies is often compromised by the emergence of mutations in the PR molecule that reduces the binding affinity of inhibitors while maintaining viable catalytic activity and affinity for natural substrates. In the present study, we used a recombinant HIV-1 C-SA PR and a recently reported variant for inhibition (Ki, IC50) and thermodynamic studies against nine clinically used inhibitors. This is the first time that binding free energies for C-SA PR and the mutant are reported. This variant PR harbours a mutation and insertion (I36T↑T) at position 36 of the C-SA HIV-1 PR, and did not show a significant difference in the catalytic effect of the HIV-1 PR. However, the nine clinically approved HIV PR drugs used in this study demonstrated weaker inhibition and lower binding affinities toward the variant when compared to the wild type HIV-1 PR. All the protease inhibitors (PIs), except Amprenavir and Ritonavir exhibited a significant decrease in binding affinity (p<0.0001). Darunavir and Nelfinavir exhibited the weakest binding affinity, 155- and 95-fold decreases respectively, toward the variant. Vitality values for the variant PR, against the seven selected PIs, confirm the impact of the mutation and insertion on the South African HIV-1 subtype C PR. This information has important clinical implications for thousands of patients in Sub-Saharan Africa.

Decoding HIV resistance: from genotype to therapy

Genetic variation in HIV poses a major challenge for prevention and treatment of the AIDS pandemic. Resistance occurs by mutations in the target proteins that lower affinity for the drug or alter the protein dynamics, thereby enabling viral replication in the presence of the drug. Due to the prevalence of drug-resistant strains, monitoring the genotype of the infecting virus is recommended. Computational approaches for predicting resistance from genotype data and guiding therapy are discussed. Many prediction methods rely on rules derived from known resistance-associated mutations, however, statistical or machine learning can improve the classification accuracy and assess unknown mutations. Adding classifiers such as information on the atomic structure of the protein can further enhance the predictions.

Combating mutations in genetic disease and drug resistance: understanding molecular mechanisms to guide drug design

INTRODUCTION

Mutations introduce diversity into genomes, leading to selective changes and driving evolution. These changes have contributed to the emergence of many of the current major health concerns of the 21st century, from the development of genetic diseases and cancers to the rise and spread of drug resistance. The experimental systematic testing of all mutations in a system of interest is impractical and not cost-effective, which has created interest in the development of computational tools to understand the molecular consequences of mutations to aid and guide rational experimentation. Areas covered: Here, the authors discuss the recent development of computational methods to understand the effects of coding mutations to protein function and interactions, particularly in the context of the 3D structure of the protein. Expert opinion: While significant progress has been made in terms of innovative tools to understand and quantify the different range of effects in which a mutation or a set of mutations can give rise to a phenotype, a great gap still exists when integrating these predictions and drawing causality conclusions linking variants. This often requires a detailed understanding of the system being perturbed. However, as part of the drug development process it can be used preemptively in a similar fashion to pharmacokinetics predictions, to guide development of therapeutics to help guide the design and analysis of clinical trials, patient treatment and public health policy strategies.

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

Background

Strategies 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.

Methods

Using 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 conclusion

Analyis 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 material

The online version of this article (doi:10.1186/s12859-016-1372-3) contains supplementary material, which is available to authorized users.

Structural Studies of a Rationally Selected Multi-Drug Resistant HIV-1 Protease Reveal Synergistic Effect of Distal Mutations on Flap Dynamics

We report structural analysis of HIV protease variant PR^S17 which was rationally selected by machine learning to represent wide classes of highly drug-resistant variants. Crystal structures were solved of PR^S17 in the inhibitor-free form and in complex with antiviral inhibitor, darunavir. Despite its 17 mutations, PR^S17 has only one mutation (V82S) in the inhibitor/substrate binding cavity, yet exhibits high resistance to all clinical inhibitors. PR^S17 has none of the major mutations (I47V, I50V, I54ML, L76V and I84V) associated with darunavir resistance, but has 10,000-fold weaker binding affinity relative to the wild type PR. Comparable binding affinity of 8000-fold weaker than PR is seen for drug resistant mutant PR20, which bears 3 mutations associated with major resistance to darunavir (I47V, I54L and I84V). Inhibitor-free PR^S17 shows an open flap conformation with a curled tip correlating with G48V flap mutation. NMR studies on inactive PR^S17_D25N unambiguously confirm that the flaps adopt mainly an open conformation in solution very similar to that in the inhibitor-free crystal structure. In PR^S17, the hinge loop cluster of mutations, E35D, M36I and S37D, contributes to the altered flap dynamics by a mechanism similar to that of PR20. An additional K20R mutation anchors an altered conformation of the hinge loop. Flap mutations M46L and G48V in PR^S17/DRV complex alter the Phe53 conformation by steric hindrance between the side chains. Unlike the L10F mutation in PR20, L10I in PR^S17 does not break the inter-subunit ion pair or diminish the dimer stability, consistent with a very low dimer dissociation constant comparable to that of wild type PR. Distal mutations A71V, L90M and I93L propagate alterations to the catalytic site of PR^S17. PR^S17 exhibits a molecular mechanism whereby mutations act synergistically to alter the flap dynamics resulting in significantly weaker binding yet maintaining active site contacts with darunavir.