Retracing the evolutionary pathways of human immunodeficiency virus type 1 resistance to protease inhibitors: virus fitness in the absence and in the presence of drug

Human immunodeficiency virus type 1 (HIV-1) resistance to protease inhibitors (PI) is a major obstacle to the full success of combined antiretroviral therapy. High-level resistance to these compounds is the consequence of stepwise accumulation of amino acid substitutions in the HIV-1 protease (PR), following pathways that usually differ from one inhibitor to another. The selective advantage conferred by resistance mutations may depend upon several parameters: the impact of the mutation on virus infectivity in the presence or absence of drug, the nature of the drug, and its local concentration. Because drug concentrations in vivo are subject to extensive variation over time and display a markedly uneven tissue distribution, the parameters of selection for HIV-1 resistance to PI in treated patients are complex and poorly understood. In this study, we have reconstructed a large series of HIV-1 mutants that carry single or combined mutations in the PR, retracing the accumulation pathways observed in ritonavir-, indinavir-, and saquinavir-treated patients. We have then measured the phenotypic resistance and the drug-free infectivity of these mutant viruses. A deeper insight into the evolutionary value of HIV-1 PR mutants came from a novel assay system designed to measure the replicative advantage of mutant viruses as a function of drug concentration. By tracing the resultant fitness profiles, we determined the range of drug concentrations for which mutant viruses displayed a replicative advantage over the wild type and the extent of this advantage. Fitness profiles were fully consistent with the order of accumulation of resistance mutations observed in treated patients and further emphasise the key importance of local drug concentration in the patterns of selection of drug-resistant HIV-1 mutants.     

Mutational fitness landscape and drug resistance

Robust technology has been developed to systematically quantify fitness landscapes that provide valuable opportunities to improve our understanding of drug resistance and define new avenues to develop drugs with reduced resistance susceptibility. We outline the critical importance of drug resistance studies and the potential for fitness landscape approaches to contribute to this effort. We describe the major technical advancements in mutational scanning, which is the primary approach used to quantify protein fitness landscapes. There are many complex steps to consider in planning and executing mutational scanning projects including developing a selection scheme, generating mutant libraries, tracking the frequency of variants using next-generation sequencing, and processing and interpreting the data. Key experimental parameters impacting each of these steps are discussed to aid in planning fitness landscape studies. There is a strong need for improved understanding of drug resistance, and fitness landscapes provide a promising new approach.     

Comparing mutational pathways to lopinavir resistance in HIV-1 subtypes B versus C

Although combination antiretroviral therapies seem to be effective at controlling HIV-1 infections regardless of the viral subtype, there is increasing evidence for subtype-specific drug resistance mutations. The order and rates at which resistance mutations accumulate in different subtypes also remain poorly understood. Most of this knowledge is derived from studies of subtype B genotypes, despite not being the most abundant subtype worldwide. Here, we present a methodology for the comparison of mutational networks in different HIV-1 subtypes, based on Hidden Conjunctive Bayesian Networks (H-CBN), a probabilistic model for inferring mutational networks from cross-sectional genotype data. We introduce a Monte Carlo sampling scheme for learning H-CBN models for a larger number of resistance mutations and develop a statistical test to assess differences in the inferred mutational networks between two groups. We apply this method to infer the temporal progression of mutations conferring resistance to the protease inhibitor lopinavir in a large cross-sectional cohort of HIV-1 subtype C genotypes from South Africa, as well as to a data set of subtype B genotypes obtained from the Stanford HIV Drug Resistance Database and the Swiss HIV Cohort Study. We find strong support for different initial mutational events in the protease, namely at residue 46 in subtype B and at residue 82 in subtype C. The inferred mutational networks for subtype B versus C are significantly different sharing only five constraints on the order of accumulating mutations with mutation at residue 54 as the parental event. The results also suggest that mutations can accumulate along various alternative paths within subtypes, as opposed to a unique total temporal ordering. Beyond HIV drug resistance, the statistical methodology is applicable more generally for the comparison of inferred mutational networks between any two groups.     

Mutation K42R in Ribosomal Protein S12 Does Not Affect Susceptibility of Mycobacterium smegmatis 16S rRNA A-Site Mutants to 2-Deoxystreptamines

Recent studies have suggested that ribosomal protein S12 modulates 16S rRNA function and susceptibility to 2-deoxystreptamine aminoglycosides. To study whether the non-restrictive K42R mutation in RpsL affects 2-deoxystreptamine susceptibility in Mycobacterium smegmatis, we studied the drug susceptibility pattern of various mutants with genetic alterations in the 16S rRNA decoding A-site in the context of wild-type and mutant protein S12. RpsL K42R substitution was found not to affect the drug resistance pattern associated with mutational alterations in 16S rRNA H44.     

Altered gag polyprotein cleavage specificity of feline immunodeficiency virus/human immunodeficiency virus mutant proteases as demonstrated in a cell-based expression system

We have used feline immunodeficiency virus (FIV) protease (PR) as a mutational system to study the molecular basis of substrate-inhibitor specificity for lentivirus PRs, with a focus on human immunodeficiency virus type 1 (HIV-1) PR. Our previous mutagenesis studies demonstrated that discrete substitutions in the active site of FIV PR with structurally equivalent residues of HIV-1 PR dramatically altered the specificity of the mutant PRs in in vitro analyses. Here, we have expanded these studies to analyze the specificity changes in each mutant FIV PR expressed in the context of the natural Gag-Pol polyprotein ex vivo. Expression mutants were prepared in which 4 to 12 HIV-1-equivalent substitutions were made in FIV PR, and cleavage of each Gag-Pol polyprotein was then assessed in pseudovirions from transduced cells. The findings demonstrated that, as with in vitro analyses, inhibitor specificities of the mutants showed increased HIV-1 PR character when analyzed against the natural substrate. In addition, all of the mutant PRs still processed the FIV polyprotein but the apparent order of processing was altered relative to that observed with wild-type FIV PR. Given the importance of the order in which Gag-Pol is processed, these findings likely explain the failure to produce infectious FIVs bearing these mutations.     

Dolutegravir Interactions with HIV-1 Integrase-DNA: Structural Rationale for Drug Resistance and Dissociation Kinetics

Signature HIV-1 integrase mutations associated with clinical raltegravir resistance involve 1 of 3 primary genetic pathways, Y143C/R, Q148H/K/R and N155H, the latter 2 of which confer cross-resistance to elvitegravir. In accord with clinical findings, in vitro drug resistance profiling studies with wild-type and site-directed integrase mutant viruses have shown significant fold increases in raltegravir and elvitegravir resistance for the specified viral mutants relative to wild-type HIV-1. Dolutegravir, in contrast, has demonstrated clinical efficacy in subjects failing raltegravir therapy due to integrase mutations at Y143, Q148 or N155, which is consistent with its distinct in vitro resistance profile as dolutegravir’s antiviral activity against these viral mutants is equivalent to its activity against wild-type HIV-1. Kinetic studies of inhibitor dissociation from wild-type and mutant integrase-viral DNA complexes have shown that dolutegravir also has a distinct off-rate profile with dissociative half-lives substantially longer than those of raltegravir and elvitegravir, suggesting that dolutegravir’s prolonged binding may be an important contributing factor to its distinct resistance profile. To provide a structural rationale for these observations, we constructed several molecular models of wild-type and clinically relevant mutant HIV-1 integrase enzymes in complex with viral DNA and dolutegravir, raltegravir or elvitegravir. Here, we discuss our structural models and the posited effects that the integrase mutations and the structural and electronic properties of the integrase inhibitors may have on the catalytic pocket and inhibitor binding and, consequently, on antiviral potency in vitro and in the clinic.     

Predicting drug resistance of the HIV-1 protease using molecular interaction energy components

Drug resistance significantly impairs the efficacy of AIDS therapy. Therefore, precise prediction of resistant viral mutants is particularly useful for developing effective drugs and designing therapeutic regimen. In this study, we applied a structure-based computational approach to predict mutants of the HIV-1 protease resistant to the seven FDA approved drugs. We analyzed the energetic pattern of the protease-drug interaction by calculating the molecular interaction energy components (MIECs) between the drug and the protease residues. Support vector machines (SVMs) were trained on MIECs to classify protease mutants into resistant and nonresistant categories. The high prediction accuracies for the test sets of cross-validations suggested that the MIECs successfully characterized the interaction interface between drugs and the HIV-1 protease. We conducted a proof-of-concept study on a newly approved drug, darunavir (TMC114), on which no drug resistance data were available in the public domain. Compared with amprenavir, our analysis suggested that darunavir might be more potent to combat drug resistance. To quantitatively estimate binding affinities of drugs and study the contributions of protease residues to causing resistance, linear regression models were trained on MIECs using partial least squares (PLS). The MIEC-PLS models also achieved satisfactory prediction accuracy. Analysis of the fitting coefficients of MIECs in the regression model revealed the important resistance mutations and shed light into understanding the mechanisms of these mutations to cause resistance. Our study demonstrated the advantages of characterizing the protease-drug interaction using MIECs. We believe that MIEC-SVM and MIEC-PLS can help design new agents or combination of therapeutic regimens to counter HIV-1 protease resistant strains.     

Human immunodeficiency virus type 1 resistance to the small molecule maturation inhibitor 3-O-(3',3'-dimethylsuccinyl)-betulinic acid is conferred by a variety of single amino acid substitutions at the CA-SP1 cleavage site in Gag

The compound 3-O-(3',3'-dimethylsuccinyl)-betulinic acid (DSB) potently and specifically inhibits human immunodeficiency virus type 1 (HIV-1) replication by delaying the cleavage of the CA-SP1 junction in Gag, leading to impaired maturation of the viral core. In this study, we investigated HIV-1 resistance to DSB by analyzing HIV-1 mutants encoding a variety of individual amino acid substitutions in the CA-SP1 cleavage site. Three of the substitutions were lethal to HIV-1 replication owing to a deleterious effect on particle assembly. The remaining mutants exhibited a range of replication efficiencies; however, each mutant was capable of replicating in the presence of concentrations of DSB that effectively inhibited wild-type HIV-1. Mutations conferring resistance to DSB also led to impaired binding of the compound to immature HIV-1 virions and loss of DSB-mediated inhibition of cleavage of Gag. Surprisingly, two of the DSB-resistant mutants retained an intermediate ability to bind the compound, suggesting that binding of DSB to immature HIV-1 particles may not be sufficient for antiviral activity. Overall, our results indicate that Gag amino acids L363 and A364 are critical for inhibition of HIV-1 replication by DSB and suggest that these residues form key contacts with the drug in the context of the assembling HIV-1 particle. These results have implications for the design of and screening for novel inhibitors of HIV-1 maturation.     

用分子模拟方法研究HIV-1整合酶突变体的耐药性机理

二酮酸类化合物(DKAs)是目前最有前景的HIV-1整合酶(integrase, IN)抑制剂．为了解DKAs引起的多种耐药株共有的耐药性机理,选择3种S-1360引起的IN耐药突变体,用分子对接和分子动力学模拟,研究了野生型和突变型IN与S-1360的结合模式,基于该结合模式探讨了3种耐药突变体所共有的耐药性机理．结果表明：在突变体中,S-1360结合到耐药突变IN核心区中的位置靠近功能loop 3区却远离与 DNA结合的关键残基,结合位置不同导致S-1360的抑制作用部分丧失;残基138到166区域的柔性对IN发挥生物学功能很重要,S-1360能与DNA结合的关键残基N155及K159形成氢键,这2个氢键作用降低了该区域的柔性,突变体中无类似氢键,因而该区域柔性增高;在突变体中,S-1360的苯环远离病毒DNA结合区,不能阻止病毒DNA末端暴露给宿主DNA;T66I突变导致残基Ⅰ的长侧链占据IN的活性口袋,阻止抑制剂以与野生型中相同的方式结合到活性中心,这均是产生抗药性的重要原因．这些模拟结果与实验结果吻合,可为抗IN的抑制剂设计和改造提供帮助．     

Evolutionary Analysis of Human Immunodeficiency Virus Type 1 Therapies Based on Conditionally Replicating Vectors

Efforts to reduce the viral load of human immunodeficiency virus type 1 (HIV-1) during long-term treatment are challenged by the evolution of anti-viral resistance mutants. Recent studies have shown that gene therapy approaches based on conditionally replicating vectors (CRVs) could have many advantages over anti-viral drugs and other approaches to therapy, potentially including the ability to circumvent the problem of evolved resistance. However, research to date has not explored the evolutionary consequences of long-term treatment of HIV-1 infections with conditionally replicating vectors. In this study, we analyze a computational model of the within-host co-evolutionary dynamics of HIV-1 and conditionally replicating vectors, using the recently proposed ‘therapeutic interfering particle’ as an example. The model keeps track of the stochastic process of viral mutation, and the deterministic population dynamics of T cells as well as different strains of CRV and HIV-1 particles. We show that early in the co-infection, mutant HIV-1 genotypes that escape suppression by CRV therapy appear; this is similar to the dynamics observed in drug treatments and other gene therapies. In contrast to other treatments, however, the CRV population is able to evolve and catch up with the dominant HIV-1 escape mutant and persist long-term in most cases. On evolutionary grounds, gene therapies based on CRVs appear to be a promising tool for long-term treatment of HIV-1. Our model allows us to propose design principles to optimize the efficacy of this class of gene therapies. In addition, because of the analogy between CRVs and naturally-occurring defective interfering particles, our results also shed light on the co-evolutionary dynamics of wild-type viruses and their defective interfering particles during natural infections.     

Structure-based phenotyping predicts HIV-1 protease inhibitor resistance

Mutations in HIV-1 drug targets lead to resistance and consequent therapeutic failure of antiretroviral drugs. Phenotypic resistance assays are time-consuming and costly, and genotypic rules-based interpretations may fail to predict the effects of multiple mutations. We have developed a computational procedure that rapidly evaluates changes in the binding energy of inhibitors to mutant HIV-1 PR variants. Models of WT complexes were produced from crystal structures. Mutant complexes were built by amino acid substitutions in the WT complexes with subsequent energy minimization of the ligand and PR binding site residues. Accuracy of the models was confirmed by comparison with available crystal structures and by prediction of known resistance-related mutations. PR variants from clinical isolates were modeled in complex with six FDA-approved PIs, and changes in the binding energy (DeltaE(bind)) of mutant versus WT complexes were correlated with the ratios of phenotypic 50% inhibitory concentration (IC(50)) values. The calculated DeltaE(bind) of five PIs showed significant correlations (R(2) = 0.7-0.8) with IC(50) ratios from the Virco Antivirogram assay, and the DeltaE(bind) of six PIs showed good correlation (R(2) = 0.76-0.85) with IC(50) ratios from the Virologic PhenoSense assay. DeltaE(bind) cutoffs corresponding to a four-fold increase in IC(50) were used to define the structure-based phenotype as susceptible, resistant, or equivocal. Blind predictions for 78 PR variants gave overall agreement of 92% (kappa = 0.756) and 86% (kappa = 0.666) with PhenoSense and Antivirogram phenotypes, respectively. The structural phenotyping predicted drug resistance of clinical HIV-1 PR variants with an accuracy approaching that of frequently used cell-based phenotypic assays.     

Studies on flexibility and binding affinity of Asp25 of HIV-1 protease mutants

We have investigated and highlighted the behavior of binding residue, Asp25 by computational analysis, which play an important role in understanding docking process with drug molecule, Ritonavir (Norvir^®) and the flexibility nature of the Human Immunodeficiency Virus-1 (HIV-1) protease enzyme. It is well known that Ritonavir is a potent and a selective HIV-1 protease inhibitor. Molecular dockings were performed in order to gain insights regarding the binding mode of this inhibitor. In our analysis, we observed Ritonavir had different rank orders of scores against different mutant of this enzyme. Asp25 of the enzyme was found to be the active site for all the mutants. The results clearly suggest that Ritonavir is not able to appropriately bind at the active site of each HIV-1 protease mutant due to RMSD difference of the amino acid (Asp) at the position 25 of all mutants. These findings support the concept that 3D space of active site is a qualitative assessment for binding affinity of inhibitor with an enzyme. The investigation on the flexibility nature of Asp25 by normal mode analysis, show that binding residue posses less flexibility due to its solvation potential. The overall analysis of our study brings clarity to the binding behavior with respect to the different mutants with Ritonavir on the basis RMSD and also on the flexible nature of HIV-1 protease enzyme with respect to Asp25 position.     

Accurate prediction of enzyme mutant activity based on a multibody statistical potential

MOTIVATION: An important area of research in biochemistry and molecular biology focuses on characterization of enzyme mutants. However, synthesis and analysis of experimental mutants is time consuming and expensive. We describe a machine-learning approach for inferring the activity levels of all unexplored single point mutants of an enzyme, based on a training set of such mutants with experimentally measured activity. RESULTS: Based on a Delaunay tessellation-derived four-body statistical potential function, a perturbation vector measuring environmental changes relative to wild type (wt) at every residue position uniquely characterizes each enzyme mutant for model development and prediction. First, a measure of model performance utilizing area (AUC) under the receiver operating characteristic (ROC) curve surpasses 0.83 and 0.77 for data sets of experimental HIV-1 protease and T4 lysozyme mutants, respectively. Additionally, a novel method is introduced for evaluating statistical significance associated with the number of correct test set predictions obtained from a trained model. Third, 100 stratified random splits of the protease and T4 lysozyme mutant data sets into training and test sets achieve 77.0% and 80.8% mean accuracy, respectively. Next, protease and T4 lysozyme models trained with experimental mutants are used to predict activity levels for all remaining mutants; a subsequent search for publications reporting on dozens of these test mutants reveals that experimental results are matched by 79% and 86% of predictions, respectively. Finally, learning curves for each mutant enzyme system indicate the influence of training set size on model performance. AVAILABILITY: Prediction databases at http://proteins.gmu.edu/automute/