Despite the high degree of HIV-1 protease and reverse transcriptase (RT) mutation in the setting of antiretroviral therapy, the spectrum of possible virus variants appears to be limited by patterns of amino acid covariation. We analyzed patterns of amino acid covariation in protease and RT sequences from more than 7,000 persons infected with HIV-1 subtype B viruses obtained from the Stanford HIV Drug Resistance Database (http://hivdb.stanford.edu). In addition, we examined the relationship between conditional probabilities associated with a pair of mutations and the order in which those mutations developed in viruses for which longitudinal sequence data were available. Patterns of RT covariation were dominated by the distinct clustering of Type I and Type II thymidine analog mutations and the Q151M-associated mutations. Patterns of protease covariation were dominated by the clustering of nelfinavir-associated mutations (D30N and N88D), two main groups of protease inhibitor (PI)–resistance mutations associated either with V82A or L90M, and a tight cluster of mutations associated with decreased susceptibility to amprenavir and the most recently approved PI darunavir. Different patterns of covariation were frequently observed for different mutations at the same position including the RT mutations T69D versus T69N, L74V versus L74I, V75I versus V75M, T215F versus T215Y, and K219Q/E versus K219N/R, and the protease mutations M46I versus M46L, I54V versus I54M/L, and N88D versus N88S. Sequence data from persons with correlated mutations in whom earlier sequences were available confirmed that the conditional probabilities associated with correlated mutation pairs could be used to predict the order in which the mutations were likely to have developed. Whereas accessory nucleoside RT inhibitor–resistance mutations nearly always follow primary nucleoside RT inhibitor–resistance mutations, accessory PI-resistance mutations often preceded primary PI-resistance mutations.
The identification of which mutations in a protein covary has played a major role in both structural and evolutionary biology. Covariation analysis has been used to help predict unsolved protein structures and to better understand the functions of proteins with known structures. The large number of published genetic sequences of the targets of HIV-1 therapy has provided an unprecedented opportunity to identify dependencies among mutations in these proteins that can be exploited to design inhibitors that have high genetic barriers to resistance. In our analysis, we identified many pairs of covarying drug-resistance mutations in HIV-1 protease and reverse transcriptase and organized them into clusters of mutations that often develop in a predictable order. Inhibitors that are active against early drug-resistant mutants are likely to be less prone to the development of resistance, whereas inhibitors that are active against fully evolved clusters of mutations may be useful drugs for salvage therapy.
Our objective was to analyze the evolution of resistance mutations (RM) and viral tropism of multi-drug-resistant (MDR) strains detected at primary HIV-1 infection (PHI). MDR HIV strain was defined as the presence of genotypic resistance to at least 1 antiretroviral of the 3 classes. Tropism determinations (CCR5 or CXCR4) were performed on baseline plasma HIV-RNA and/or PBMC-HIV-DNA samples, then during follow-up using population-based sequencing of V3 loop and phenotypic tests. Clonal analysis was performed at baseline for env, RT and protease genes, and for HIV-DNA env gene during follow-up. Five patients were eligible. At baseline, RT, protease and env clones from HIV-RNA and HIV-DNA were highly homogenous for each patient; genotypic tropism was R5 in 3 (A,B,C) and X4 in 2 patients (D,E). MDR strains persisted in HIV-DNA throughout follow-up in all patients. For patient A, tropism remained R5 with concordance between phenotypic and genotypic tests. Clonal analysis on Month (M) 78 HIV-DNA evidenced exclusively R5 (21/21) variants. In patient B, clonal analysis at M36 showed exclusively R5 variants (19/19) using both genotypic and phenotypic tests. In patient C, baseline tropism was R5 by genotypic test and R5/X4 by phenotypic test. An expansion of these X4 clones was evidenced by clonal analysis on M72 HIV-DNA (12/14 X4 and 2/14 R5 variants). In patient D, baseline tropism was X4 with concordance between both techniques and HIV-RNA and HIV-DNA remained X4-tropic up to M72, confirmed by the clonal analysis. Patient E harboured highly homogenous X4-using population at baseline; tropism was unchanged at M1 and M18. In all patients, the initial MDR population was highly homogenous initially, supporting the early expansion of a monoclonal population and its long-term persistence. X4-tropic variants present at baseline were still exclusive (patients D and E) or dominant (at least one time point, patient C) far from PHI.
A major obstacle in treatment of HIV is the ability of the virus to mutate rapidly into drug-resistant variants. A method for predicting the susceptibility of mutated HIV strains to antiviral agents would provide substantial clinical benefit as well as facilitate the development of new candidate drugs. Therefore, we used proteochemometrics to model the susceptibility of HIV to protease inhibitors in current use, utilizing descriptions of the physico-chemical properties of mutated HIV proteases and 3D structural property descriptions for the protease inhibitors. The descriptions were correlated to the susceptibility data of 828 unique HIV protease variants for seven protease inhibitors in current use; the data set comprised 4792 protease-inhibitor combinations.
The model provided excellent predictability (R2 = 0.92, Q2 = 0.87) and identified general and specific features of drug resistance. The model's predictive ability was verified by external prediction in which the susceptibilities to each one of the seven inhibitors were omitted from the data set, one inhibitor at a time, and the data for the six remaining compounds were used to create new models. This analysis showed that the over all predictive ability for the omitted inhibitors was Q2 inhibitors = 0.72.
Our results show that a proteochemometric approach can provide generalized susceptibility predictions for new inhibitors. Our proteochemometric model can directly analyze inhibitor-protease interactions and facilitate treatment selection based on viral genotype. The model is available for public use, and is located at HIV Drug Research Centre.
Reverse transcriptase is a major drug target in highly active antiretroviral therapy (HAART) against HIV, which typically comprises two nucleoside/nucleotide analog reverse transcriptase (RT) inhibitors (NRTIs) in combination with a non-nucleoside RT inhibitor or a protease inhibitor. Unfortunately, HIV is capable of escaping the therapy by mutating into drug-resistant variants. Computational models that correlate HIV drug susceptibilities to the virus genotype and to drug molecular properties might facilitate selection of improved combination treatment regimens.
We applied our earlier developed proteochemometric modeling technology to analyze HIV mutant susceptibility to the eight clinically approved NRTIs. The data set used covered 728 virus variants genotyped for 240 sequence residues of the DNA polymerase domain of the RT; 165 of these residues contained mutations; totally the data-set covered susceptibility data for 4,495 inhibitor-RT combinations. Inhibitors and RT sequences were represented numerically by 3D-structural and physicochemical property descriptors, respectively. The two sets of descriptors and their derived cross-terms were correlated to the susceptibility data by partial least-squares projections to latent structures. The model identified more than ten frequently occurring mutations, each conferring more than two-fold loss of susceptibility for one or several NRTIs. The most deleterious mutations were K65R, Q151M, M184V/I, and T215Y/F, each of them decreasing susceptibility to most of the NRTIs. The predictive ability of the model was estimated by cross-validation and by external predictions for new HIV variants; both procedures showed very high correlation between the predicted and actual susceptibility values (Q2 = 0.89 and Q2ext = 0.86). The model is available at www.hivdrc.org as a free web service for the prediction of the susceptibility to any of the clinically used NRTIs for any HIV-1 mutant variant.
Our results give directions how to develop approaches for selection of genome-based optimum combination therapy for patients harboring mutated HIV variants.
Drug-resistant mutations (DRMs) in HIV-1 protease are a major challenge to antiretroviral therapy. Protease-substrate interactions that are determined to be critical for native selectivity could serve as robust targets for drug design that are immune to DRMs. In order to identify the structural mechanisms of selectivity, we developed a peptide docking algorithm to predict the atomic structure of protease-substrate complexes and applied it to a large and diverse set of cleavable and non-cleavable peptides. Cleavable peptides showed significantly lower energies of interaction than non-cleavable peptides with six protease active-site residues playing the most significant role in discrimination. Surprisingly, all six residues correspond to sequence positions associated with drug resistance mutations, demonstrating that the very residues that are responsible for native substrate specificity in HIV-1 protease are altered during its evolution to drug resistance, suggesting that drug resistance and substrate selectivity may share common mechanisms.
Protein-peptide docking; substrate specificity; peptide docking; drug resistance
The requirement for multiple mutations for protease inhibitor (PI) resistance necessitates a better understanding of the molecular basis of resistance development. The novel bioinformatics resistance determination approach presented here elaborates on genetic profiles observed in clinical human immunodeficiency virus type 1 (HIV-1) isolates. Synthetic protease sequences were cloned in a wild-type HIV-1 background to generate a large number of close variants, covering 69 mutation clusters between multi-PI-resistant viruses and their corresponding genetically closely related, but PI-susceptible, counterparts. The vast number of mutants generated facilitates a profound and broad analysis of the influence of the background on the effect of individual PI resistance-associated mutations (PI-RAMs) on PI susceptibility. Within a set of viruses, all PI-RAMs that differed between susceptible and resistant viruses were varied while maintaining the background sequence from the resistant virus. The PI darunavir was used to evaluate PI susceptibility. Single sets allowed delineation of the impact of individual mutations on PI susceptibility, as well as the influence of PI-RAMs on one another. Comparing across sets, it could be inferred how the background influenced the interaction between two mutations, in some cases even changing antagonistic relationships into synergistic ones or vice versa. The approach elaborates on patient data and demonstrates how the specific mutational background greatly influences the impact of individual mutations on PI susceptibility in clinical patterns.
Correlated amino acid mutation analysis has been widely used to infer functional interactions between different sites in a protein. However, this analysis can be confounded by important phylogenetic effects broadly classifiable as background linkage disequilibrium (BLD). We have systematically separated the covariation induced by selective interactions between amino acids from background LD, using synonymous (S) vs. amino acid (A) mutations. Covariation between two amino acid mutations, (A,A), can be affected by selective interactions between amino acids, whereas covariation within (A,S) pairs or (S,S) pairs cannot. Our analysis of the pol gene — including the protease and the reverse transcriptase genes — in HIV reveals that (A,A) covariation levels are enormously higher than for either (A,S) or (S,S), and thus cannot be attributed to phylogenetic effects. The magnitude of these effects suggests that a large portion of (A,A) covariation in the HIV pol gene results from selective interactions. Inspection of the most prominent (A,A) interactions in the HIV pol gene showed that they are known sites of independently identified drug resistance mutations, and physically cluster around the drug binding site. Moreover, the specific set of (A,A) interaction pairs was reproducible in different drug treatment studies, and vanished in untreated HIV samples. The (S,S) covariation curves measured a low but detectable level of background LD in HIV.
We compared HIV-1 subtype B reverse transcriptase (RT) and protease mutation patterns in isolates from heavily treated persons in Northern California with those from persons described in the published literature predominantly from other parts of the United States and Europe. There were few differences in the prevalence of single, double, and triple mutations between the two sets of sequences. More complex patterns of mutations could be characterized by clustering the sequences into eight groups of RT sequences and nine groups of protease sequences according to the presence of known drug-resistance mutations. This clustering accounted for 63% of the variation at RT inhibitor-resistance positions and 68% of the variation at protease inhibitor-resistance positions. The majority of clusters contained Northern California and literature sequences in similar proportions.
Great strides have been made in the effective treatment of HIV-1 with the development of second-generation protease inhibitors (PIs) that are effective against historically multi-PI-resistant HIV-1 variants. Nevertheless, mutation patterns that confer decreasing susceptibility to available PIs continue to arise within the population. Understanding the phenotypic and genotypic patterns responsible for multi-PI resistance is necessary for developing PIs that are active against clinically-relevant PI-resistant HIV-1 variants.
In this work, we use globally optimal integer programming-based clustering techniques to elucidate multi-PI phenotypic resistance patterns using a data set of 398 HIV-1 protease sequences that have each been phenotyped for susceptibility toward the nine clinically-approved HIV-1 PIs. We validate the information content of the clusters by evaluating their ability to predict the level of decreased susceptibility to each of the available PIs using a cross validation procedure. We demonstrate the finding that as a result of phenotypic cross resistance, the considered clinical HIV-1 protease isolates are confined to ~6% or less of the clinically-relevant phenotypic space. Clustering and feature selection methods are used to find representative sequences and mutations for major resistance phenotypes to elucidate their genotypic signatures. We show that phenotypic similarity does not imply genotypic similarity, that different PI-resistance mutation patterns can give rise to HIV-1 isolates with similar phenotypic profiles.
Rather than characterizing HIV-1 susceptibility toward each PI individually, our study offers a unique perspective on the phenomenon of PI class resistance by uncovering major multidrug-resistant phenotypic patterns and their often diverse genotypic determinants, providing a methodology that can be applied to understand clinically-relevant phenotypic patterns to aid in the design of novel inhibitors that target other rapidly evolving molecular targets as well.
Darunavir and tipranavir are two inhibitors that are active against multi-drug resistant (MDR) HIV-1 protease variants. In this study, the in vitro inhibitory efficacy was tested against a MDR HIV-1 protease variant, MDR 769 82T, containing the drug resistance mutations of 46L/54V/82T/84V/90M. Crystallographic and enzymatic studies were performed to examine the mechanism of resistance and the relative maintenance of potency. The key findings are as follows: (i) The MDR protease exhibits decreased susceptibility to all nine HIV-1 protease inhibitors approved by the U.S. Food and Drug Administration (FDA), among which darunavir and tipranavir are the most potent; (ii) the threonine 82 mutation on the protease greatly enhances drug resistance by altering the hydrophobicity of the binding pocket; (iii) darunavir or tipranavir binding facilitates closure of the wide-open flaps of the MDR protease; and (iv) the remaining potency of tipranavir may be preserved by stabilizing the flaps in the inhibitor-protease complex while darunavir maintains its potency by preserving protein main chain hydrogen bonds with the flexible P2 group. These results could provide new insights into drug design strategies to overcome multi-drug resistance of HIV-1 protease variants.
darunavir; tipranavir; multi-drug resistant HIV-1 protease; x-ray crystallography
Plasma-derived sequences of human immunodeficiency virus type 1 (HIV-1) protease from 1,162 patients (457 drug-naïve patients and 705 patients receiving protease inhibitor [PI]-containing antiretroviral regimens) led to the identification and characterization of 17 novel protease mutations potentially associated with resistance to PIs. Fourteen mutations were positively associated with PIs and significantly correlated in pairs and/or clusters with known PI resistance mutations, suggesting their contribution to PI resistance. In particular, E34Q, K43T, and K55R, which were associated with lopinavir treatment, correlated with mutations associated with lopinavir resistance (E34Q with either L33F or F53L, or K43T with I54A) or clustered with multi-PI resistance mutations (K43T with V82A and I54V or V82A, V32I, and I47V, or K55R with V82A, I54V, and M46I). On the other hand, C95F, which was associated with treatment with saquinavir and indinavir, was highly expressed in clusters with either L90M and I93L or V82A and G48V. K45R and K20T, which were associated with nelfinavir treatment, were specifically associated with D30N and N88D and with L90M, respectively. Structural analysis showed that several correlated positions were within 8 Å of each other, confirming the role of the local environment for interactions among mutations. We also identified three protease mutations (T12A, L63Q, and H69N) whose frequencies significantly decreased in PI-treated patients compared with that in drug-naïve patients. They never showed positive correlations with PI resistance mutations; if anything, H69N showed a negative correlation with the compensatory mutations M36I and L10I. These mutations may prevent the appearance of PI resistance mutations, thus increasing the genetic barrier to PI resistance. Overall, our study contributes to a better definition of protease mutational patterns that regulate PI resistance and strongly suggests that other (novel) mutations beyond those currently known to confer resistance should be taken into account to better predict resistance to antiretroviral drugs.
Drug resistance is a major problem in the treatment of AIDS, due to the very high mutation rate of human immunodeficiency virus (HIV) and subsequent rapid development of resistance to new drugs. Identification of mutations associated with drug resistance is critical for both individualized treatment selection and new drug design. We have performed an automated mutation analysis of HIV Type 1 (HIV-1) protease and reverse transcriptase (RT) from approximately 40,000 AIDS patient plasma samples sequenced by Specialty Laboratories Inc. from 1999 to mid-2002. This data set provides a nearly complete mutagenesis of HIV protease and enables the calculation of statistically significant Ka/Ks values for each individual amino acid mutation in protease and RT. Positive selection (i.e., a Ka/Ks ratio of >1, indicating increased reproductive fitness) detected 19 of 23 known drug-resistant mutation positions in protease and 20 of 34 such positions in RT. We also discovered 163 new amino acid mutations in HIV protease and RT that are strong candidates for drug resistance or fitness. Our results match available independent data on protease mutations associated with specific drug treatments and mutations with positive reproductive fitness, with high statistical significance (the P values for the observed matches to occur by random chance are 10−5.2 and 10−16.6, respectively). Our mutation analysis provides a valuable resource for AIDS research and will be available to academic researchers upon publication at http://www.bioinformatics.ucla.edu/HIV. Our data indicate that positive selection mapping is an analysis that can yield powerful insights from high-throughput sequencing of rapidly mutating pathogens.
Development of viral resistance to the aminodiol human immunodeficiency virus (HIV) protease inhibitor BMS 186,318 was studied by serial passage of HIV type 1 RF in MT-2 cells in the presence of increasing concentrations of compound. After 11 passages, an HIV variant that showed a 15-fold increase in 50% effective dose emerged. This HIV variant displays low-level cross-resistance to the C2 symmetric inhibitor A-77003 but remains sensitive to the protease inhibitors Ro 31-8959 and SC52151. Genetic analysis of the protease gene from a drug-resistant variant revealed an Ala-to-Thr change at amino acid residue 71 (A71T) and a Val-to-Ala change at residue 82 (V82A). To determine the effects of these mutations on protease and virus drug susceptibility, recombinant protease and proviral HIV type 1 clones containing the single mutations A71T and V82A or double mutation A71T/V82A were constructed. Subsequent drug sensitivity assays on the mutant proteases and viruses indicated that the V82A substitution was responsible for most of the resistance observed. Further genotypic analysis of the protease genes from earlier passages of virus indicated that the A71T mutation emerged prior to the V82A change. Finally, the level of resistance did not increase following continued passage in increasing concentrations of drug, and the resistant virus retained its drug susceptibility phenotype 34 days after drug withdrawal.
From 2005 through 2007, Seattle health care providers identified cases of primary multiclass drug-resistant (MDR) HIV-1 with common patterns of resistance to antiretrovirals (ARVs). Through surveillance activities and genetic analysis, the local Health Department and the University of Washington identified phylogenetically linked cases among ARV treatment–naive and -experienced individuals.
HIV-1 pol nucleotide consensus sequences submitted to the University of Washington Clinical Virology Laboratory were assessed for phylogenetically related MDR HIV. Demographic and clinical data collected included HIV diagnosis date, ARV history, and laboratory results.
Seven ARV-naive men had phylogenetically linked MDR strains with resistance to most ARVs; these were linked to 2 ARV-experienced men. All 9 men reported methamphetamine use and multiple anonymous male partners. Primary transmissions were diagnosed for more than a 2-year period, 2005−2007. Three, including the 2 ARV-experienced men, were prescribed ARVs.
This cluster of 9 men with phylogenetically related highly drug-resistant MDR HIV strains and common risk factors but without reported direct epidemiologic links may have important implications to public health. This cluster demonstrates the importance of primary resistance testing and of collaboration between the public and private medical community in identifying MDR outbreaks. Public health interventions and surveillance are needed to reduce transmission of MDR HIV-1.
HIV; HIV-1; multiple drug resistance; disease clustering; highly active antiretroviral therapy
Protease inhibitors designed to bind to protease have become major anti-AIDS drugs. Unfortunately, the emergence of viral mutations severely limits the long-term efficiency of the inhibitors. The resistance mechanism of these diversely located mutations remains unclear.
Here I use an elastic network model to probe the connection between the global dynamics of HIV-1 protease and the structural distribution of drug-resistance mutations. The models for study are the crystal structures of unbounded and bound (with the substrate and nine FDA approved inhibitors) forms of HIV-1 protease. Coarse-grained modeling uncovers two groups that couple either with the active site or the flap. These two groups constitute a majority of the drug-resistance residues. In addition, the significance of residues is found to be correlated with their dynamical changes in binding and the results agree well with the complete mutagenesis experiment of HIV-1 protease.
The dynamic study of HIV-1 protease elucidates the functional importance of common drug-resistance mutations and suggests a unifying mechanism for drug-resistance residues based on their dynamical properties. The results support the robustness of the elastic network model as a potential predictive tool for drug resistance.
Hydrophobic residues outside the active site of HIV-1 protease frequently mutate in patients undergoing protease inhibitor therapy, however, the mechanism by which these mutations confer drug resistance is not understood. From analysis of molecular dynamics simulations, 19 core hydrophobic residues appear to facilitate the conformational changes that occur in HIV-1 protease. The hydrophobic core residues slide by each other, exchanging one hydrophobic van der Waal contact for another, with little energy penalty, while maintaining many structurally important hydrogen bonds. Such hydrophobic sliding may represent a general mechanism by which proteins undergo conformational changes. Mutation of these residues in HIV-1 protease would alter the packing of the hydrophobic core, affecting the conformational flexibility of the protease, thereby impacting the dynamic balance between processing substrates and binding inhibitors thus contributing to drug resistance.
HIV protease; molecular dynamics; conformational changes; drug resistance
HIV protease, an aspartyl protease crucial to the life cycle of HIV, is the target of many drug development programs. Though many protease inhibitors are on the market, protease eventually evades these drugs by mutating at a rapid pace and building drug resistance. The drug resistance mutations, called primary mutations, are often destabilizing to the enzyme and this loss of stability has to be compensated for. Using a coarse-grained biophysical energy model together with statistical inference methods, we observe that accessory mutations of charged residues increase protein stability, playing a key role in compensating for destabilizing primary drug resistance mutations. Increased stability is intimately related to correlations between electrostatic mutations – uncorrelated mutations would strongly destabilize the enzyme. Additionally, statistical modeling indicates that the network of correlated electrostatic mutations has a simple topology and has evolved to minimize frustrated interactions. The model's statistical coupling parameters reflect this lack of frustration and strongly distinguish like-charge electrostatic interactions from unlike-charge interactions for of the most significantly correlated double mutants. Finally, we demonstrate that our model has considerable predictive power and can be used to predict complex mutation patterns, that have not yet been observed due to finite sample size effects, and which are likely to exist within the larger patient population whose virus has not yet been sequenced.
HIV is incurable because its enzymes evolve rapidly by developing resistance mutations to retroviral inhibitors. Most of these mutations work synergistically, but the biophysical basis behind their cooperation is not well understood. Our work addresses these important issues by bridging the gap between the statistical modeling of HIV protease subtype B sequences with the energetics of mutations involving charged amino acids by showing that electrostatic stability is intimately related to correlations. Moreover, we demonstrate that our statistical model has considerable predictive power and can be used to predict complex mutation patterns that have not yet been observed due to the finite sizes of the current sequence databases. In other words, as the database size increases, our model has the ability to predict the identities of the high probability mutations patterns, which are more likely to be observed. Knowing which currently unobserved mutations are more likely to be observed can be very advantageous in combating the disease.
Maturation of human immunodeficiency virus (HIV) depends on the processing of Gag and Pol polyproteins by the viral protease, making this enzyme a prime target for anti-HIV therapy. Among the protease substrates, the nucleocapsid-p1 (NC-p1) sequence is the least homologous, and its cleavage is the rate-determining step in viral maturation. In the other substrates of HIV-1 protease, P1 is usually either a hydrophobic or an aromatic residue, and P2 is usually a branched residue. NC-p1, however, contains Asn at P1 and Ala at P2. In response to the V82A drug-resistant protease mutation, the P2 alanine of NC-p1 mutates to valine (AP2V). To provide a structural rationale for HIV-1 protease binding to the NC-p1 cleavage site, we solved the crystal structures of inactive (D25N) WT and V82A HIV-1 proteases in complex with their respective WT and AP2V mutant NC-p1 substrates. Overall, the WT NC-p1 peptide binds HIV-1 protease less optimally than the AP2V mutant, as indicated by the presence of fewer hydrogen bonds and fewer van der Waals contacts. AlaP2 does not fill the P2 pocket completely; PheP1′ makes van der Waals interactions with Val82 that are lost with the V82A protease mutation. This loss is compensated by the AP2V mutation, which reorients the peptide to a conformation more similar to that observed in other substrate-protease complexes. Thus, the mutant substrate not only binds the mutant protease more optimally but also reveals the interdependency between the P1′ and P2 substrate sites. This structural interdependency results from coevolution of the substrate with the viral protease.
The reaction of HIV protease to inhibitor therapy is characterized by the emergence of complex mutational patterns which confer drug resistance. The response of HIV protease to drugs often involves both primary mutations that directly inhibit the action of the drug, and a host of accessory resistance mutations that may occur far from the active site but may contribute to restoring the fitness or stability of the enzyme. Here we develop a probabilistic approach based on connected information that allows us to study residue, pair level and higher-order correlations within the same framework.
We apply our methodology to a database of approximately 13,000 sequences which have been annotated by the treatment history of the patients from which the samples were obtained. We show that including pair interactions is essential for agreement with the mutational data, since neglect of these interactions results in order-of-magnitude errors in the probabilities of the simultaneous occurence of many mutations. The magnitude of these pair correlations changes dramatically between sequences obtained from patients that were or were not exposed to drugs. Higher-order effects make a contribution of as much as 10% for residues taken three at a time, but increase to more than twice that for 10 to 15-residue groups. The sequence data is insufficient to determine the higher-order effects for larger groups. We find that higher-order interactions have a significant effect on the predicted frequencies of sequences with large numbers of mutations. While relatively rare, such sequences are more prevalent after multi-drug therapy. The relative importance of these higher-order interactions increases with the number of drugs the patient had been exposed to.
Correlations are critical for the understanding of mutation patterns in HIV protease. Pair interactions have substantial qualitative effects, while higher-order interactions are individually smaller but may have a collective effect. Together they lead to correlations which could have an important impact on the dynamics of the evolution of cross-resistance, by allowing the virus to pass through otherwise unlikely mutational states. These findings also indicate that pairwise and possibly higher-order effects should be included in the models of protein evolution, instead of assuming that all residues mutate independently of one another.
Structural studies revealed a proline switch as a novel mechanism for the multidrug-resistant nature of multidrug-resistant clinical isolate 769 HIV-1 protease variants.
The flexible flaps and the 80s loops (Pro79–Ile84) of HIV-1 protease are crucial in inhibitor binding. Previously, it was reported that the crystal structure of multidrug-resistant 769 (MDR769) HIV-1 protease shows a wide-open conformation of the flaps owing to conformational rigidity acquired by the accumulation of mutations. In the current study, the effect of mutations on the conformation of the 80s loop of MDR769 HIV-1 protease variants is reported. Alternate conformations of Pro81 (proline switch) with a root-mean-square deviation of 3–4.8 Å in the Cα atoms of the I10V mutant and a side chain with a ‘flipped-out’ conformation in the A82F mutant cause distortion in the S1/S1′ binding pockets that affects inhibitor binding. The A82S and A82T mutants show local changes in the electrostatics of inhibitor binding owing to the mutation from nonpolar to polar residues. In summary, the crystallographic studies of four variants of MDR769 HIV-1 protease presented in this article provide new insights towards understanding the drug-resistance mechanism as well as a basis for design of future protease inhibitors with enhanced potency.
multidrug-resistance; HIV-1 protease; proline switch; 80s loop; expanded active-site cavity; wide-open flaps; docking
Unique viral variants and resistance mutations may occur in the genital tract of HIV-2 ARV-naive infected women. We sequenced and phylogenetically analyzed protease (PR), reverse transcriptase (RT), and envelope (ENV) from PBMC and genital tract samples from four ARV-naive women in Senegal. HIV-2 protease polymorphisms that predict HIV-1 protease inhibitor (PI) resistance were common. Two subjects had protease mutations (T77I and I64V) in genital tract samples that were not found in PBMCs. One subject had the HIV-2 reverse transcriptase M184I mutation in CVL DNA (but not PBMCs) that is known to confer 3TC/FTC resistance in HIV-2. In another subject, the reverse transcriptase A62V mutation was also found in CVL-RNA but not PBMCs. We found no significant difference in ENV variants between PBMCs and the genital tract. HIV-2 RT and PR mutations in the genital tract of ARV-naive females may have implications for transmitted HIV-2 resistance and ARV therapy.
The goal of this study was to use X-ray crystallography to investigate the structural basis of resistance to human immunodeficiency virus type 1 (HIV-1) protease inhibitors. We overexpressed, purified, and crystallized a multidrug-resistant (MDR) HIV-1 protease enzyme derived from a patient failing on several protease inhibitor-containing regimens. This HIV-1 variant contained codon mutations at positions 10, 36, 46, 54, 63, 71, 82, 84, and 90 that confer drug resistance to protease inhibitors. The 1.8-angstrom (Å) crystal structure of this MDR patient isolate reveals an expanded active-site cavity. The active-site expansion includes position 82 and 84 mutations due to the alterations in the amino acid side chains from longer to shorter (e.g., V82A and I84V). The MDR isolate 769 protease “flaps” stay open wider, and the difference in the flap tip distances in the MDR 769 variant is 12 Å. The MDR 769 protease crystal complexes with lopinavir and DMP450 reveal completely different binding modes. The network of interactions between the ligands and the MDR 769 protease is completely different from that seen with the wild-type protease-ligand complexes. The water molecule-forming hydrogen bonds bridging between the two flaps and either the substrate or the peptide-based inhibitor are lacking in the MDR 769 clinical isolate. The S1, S1′, S3, and S3′ pockets show expansion and conformational change. Surface plasmon resonance measurements with the MDR 769 protease indicate higher koff rates, resulting in a change of binding affinity. Surface plasmon resonance measurements provide kon and koff data (Kd = koff/kon) to measure binding of the multidrug-resistant protease to various ligands. This MDR 769 protease represents a new antiviral target, presenting the possibility of designing novel inhibitors with activity against the open and expanded protease forms.
Human immunodeficiency virus type 1 (HIV-1) integrase inhibitors are in clinical trials, and raltegravir and elvitegravir are likely to be the first licensed drugs of this novel class of HIV antivirals. Understanding resistance to these inhibitors is important to maximize their efficacy. It has been shown that natural variation and covariation provide valuable insights into the development of resistance for established HIV inhibitors. Therefore, we have undertaken a study to fully characterize natural polymorphisms and amino acid covariation within an inhibitor-naïve sequence set spanning all defined HIV-1 subtypes. Inter- and intrasubtype variation was greatest in a 50-amino-acid segment of HIV-1 integrase incorporating the catalytic aspartic acid codon 116, suggesting that polymorphisms affect inhibitor binding and pathways to resistance. The critical mutations that determine the resistance pathways to raltegravir and elvitegravir (N155H, Q148K/R/H, and E92Q) were either rare or absent from the 1,165-sequence data set. However, 25 out of 41 mutations associated with integrase inhibitor resistance were present. These mutations were not subtype associated and were more prevalent in the subtypes that had been sampled frequently within the database. A novel modification of the Jaccard index was used to analyze amino acid covariation within HIV-1 integrase. A network of 10 covarying resistance-associated mutations was elucidated, along with a further 15 previously undescribed mutations that covaried with at least two of the resistance positions. The validation of covariation as a predictive tool will be dependent on monitoring the evolution of HIV-1 integrase under drug selection pressure.
Highly-lethal outbreaks of multi drug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis are increasing. Whole-genome sequencing of KwaZulu-Natal MDR and XDR outbreak strains prevalent in HIV patients by the Broad Institute identified 22 novel mutations which were unique to the XDR genome or shared only by the MDR and XDR genomes and not already known to be associated with drug-resistance. We studied the 12 novel mutations which were not located in highly-repetitive genes to identify mutations that were truly associated with drug-resistance or likely to confer a specific fitness advantage. None of these mutations could be found in a phylogenetically and geographically diverse set of drug–resistant and susceptible M. tuberculosis isolates, suggesting that these mutations are unique to the KZN clone. Examination of the 600 bp region flanking each mutation revealed 26 new mutations. We searched for a convergent evolutionary signal in the new mutations for evidence that they emerged under selective pressure, consistent with increased fitness. However, all but one rare mutation were monophyletic, indicating that the mutations were markers of strain-phylogeny rather than fitness or drug-resistance. Our results suggest that virulent XDR tuberculosis in immunocompromised HIV patients can evolve without generalizable fitness changes or other XDR-specific mutations.
XDR tuberculosis evolution
Motivation: Model-based clustering has been widely used, e.g. in microarray data analysis. Since for high-dimensional data variable selection is necessary, several penalized model-based clustering methods have been proposed tørealize simultaneous variable selection and clustering. However, the existing methods all assume that the variables are independent with the use of diagonal covariance matrices.
Results: To model non-independence of variables (e.g. correlated gene expressions) while alleviating the problem with the large number of unknown parameters associated with a general non-diagonal covariance matrix, we generalize the mixture of factor analyzers to that with penalization, which, among others, can effectively realize variable selection. We use simulated data and real microarray data to illustrate the utility and advantages of the proposed method over several existing ones.
Supplementary information: Supplementary data are available at Bioinformatics online.