Bioinformatics Techniques for Studying Drug Resistance In HIV and Staphylococcus Aureus

The worldwide HIV/AIDS pandemic has been partly controlled and treated by antivirals targeting HIV protease, integrase and reverse transcriptase, however, drug resistance has become a serious problem. HIV-1 drug resistance to protease inhibitors evolves by mutations in the PR gene. The resistance mutations can alter protease catalytic activity, inhibitor binding, and stability. Different machine learning algorithms (restricted boltzmann machines, clustering, etc.) have been shown to be effective machine learning tools for classification of genomic and resistance data. Application of restricted boltzmann machine produced highly accurate and robust classification of HIV protease resistance. They can also be used to compare resistance profiles of different protease inhibitors. HIV drug resistance has also been studied by enzyme kinetics and X-ray crystallography. Triple mutant HIV-1 protease with resistance mutations V32I, I47V and V82I has been used as a model for the active site of HIV-2 protease. The effects of four investigational antiviral inhibitors was measured for Triple mutant. The tested compounds had significantly worse inhibition of triple mutant with Ki values of 17-40 nM compared to 2-10 pM for wild type protease. The crystal structure of triple mutant in complex with GRL01111 was solved and showed few changes in protease interactions with inhibitor. These new inhibitors are not expected to be effective for HIV-2 protease or HIV-1 protease with changes V32I, I47V and V82I. Methicillin-resistant Staphylococcus aureus (MRSA) is an opportunistic pathogen that causes hospital and community-acquired infections. Antibiotic resistance occurs because of newly acquired low-affinity penicillin-binding protein (PBP2a). Transcriptome analysis was performed to determine how MuM (mutated PBP2 gene) responds to spermine and how Mu50 (wild type) responds to spermine and spermine–β-lactam synergy. Exogenous spermine and oxacillin were found to alter some significant gene expression patterns with major biochemical pathways (iron, sigB regulon) in MRSA with mutant PBP2 protein

HIV-1 protease-substrate coevolution in nelfinavir resistance

Resistance to various human immunodeficiency virus type 1 (HIV-1) protease inhibitors (PIs) challenges the effectiveness of therapies in treating HIV-1-infected individuals and AIDS patients. The virus accumulates mutations within the protease (PR) that render the PIs less potent. Occasionally, Gag sequences also coevolve with mutations at PR cleavage sites contributing to drug resistance. In this study, we investigated the structural basis of coevolution of the p1-p6 cleavage site with the nelfinavir (NFV) resistance D30N/N88D protease mutations by determining crystal structures of wild-type and NFV-resistant HIV-1 protease in complex with p1-p6 substrate peptide variants with L449F and/or S451N. Alterations of residue 30\u27s interaction with the substrate are compensated by the coevolving L449F and S451N cleavage site mutations. This interdependency in the PR-p1-p6 interactions enhances intermolecular contacts and reinforces the overall fit of the substrate within the substrate envelope, likely enabling coevolution to sustain substrate recognition and cleavage in the presence of PR resistance mutations. IMPORTANCE: Resistance to human immunodeficiency virus type 1 (HIV-1) protease inhibitors challenges the effectiveness of therapies in treating HIV-1-infected individuals and AIDS patients. Mutations in HIV-1 protease selected under the pressure of protease inhibitors render the inhibitors less potent. Occasionally, Gag sequences also mutate and coevolve with protease, contributing to maintenance of viral fitness and to drug resistance. In this study, we investigated the structural basis of coevolution at the Gag p1-p6 cleavage site with the nelfinavir (NFV) resistance D30N/N88D protease mutations. Our structural analysis reveals the interdependency of protease-substrate interactions and how coevolution may restore substrate recognition and cleavage in the presence of protease drug resistance mutations

X-Ray Crystal Structures And Enzyme Kinetic Investigations Of Drug-Resistant Mutants Of Hiv-1 Protease

Globally, 62% of 38 million HIV-infected people are receiving antiretroviral therapy. Inhibitors targeting the viral protease have been clinically successful as 9 protease inhibitors (PIs) have been approved by the FDA since 1995. However, drug resistance arising by mutations in the protease undermines effective treatment. Analysis of protease and its mutants by structural biology methods paired with enzymology has given insight into the molecular mechanisms for drug resistance and guided new strategies for inhibitor design. Recently, highly resistant variants of HIV-1 protease from clinical isolates have been identified with ~20 mutations and several orders of magnitude worse binding affinity for clinical PIs such as darunavir. Three such mutants, PR20, PRS17, and PRS5B, are the focus of this body of work and show 800-10,000-fold less susceptibility to darunavir than wild-type protease. Understanding the molecular mechanisms driving the extreme drug-resistance of these three protease mutants aid rational drug design efforts to fight the HIV/AIDS pandemic. Structure-guided strategies for drug design have resulted in an investigational inhibitor, GRL-142, with modified ligands derived from the darunavir scaffold that shows 16-fold better inhibition than darunavir of resistant mutant PR20. The crystal structure of PR20 in complex with GRL-142 reveals how the expanded binding pocket, dynamic flaps, and faster dimer dissociation of PR20 are counteracted by the larger moieties of GRL-142. Resistant variant PRS17, which was rationally selected from the HIVdb genotype-phenotype database by machine learning, shows ~3-fold better inhibition by peptide substrate analogs compared to wild-type protease. Crystal structures of PRS17 with substrate analogs show a major effect of drug-resistance mutations V82S and G48V improving interactions with substrates consistent with better inhibition, suggesting a novel mechanism for resistance. Finally, structural studies of another mutant selected by machine learning, PRS5B, reveal coordinated structural changes leading to decreased intra-subunit interactions and intermediate levels of resistance to PIs. The sum of knowledge on resistant variants PR20, PRS17, and PRS5B illuminates the evolution of HIV-1 protease in the era of accessible PI treatments. These results illustrate the power of combining structural analysis of proteins with enzyme kinetics for combatting drug resistant HIV

Virtual Screening of Drugs against HIV-1 Protease

The life-threatening infections and pandemic spread of Human Immunodeficiency virus-1 (HIV-1), the etiologic agent of AIDS, has promoted an unending scientific effort to understand and control the disease. The resultant understanding of HIV-1 life cycle has defined many different targets for potential drug intervention. HIV protease enzyme responsible for cleaving large polyprotein precursors into biologically active protein products is an important target for the treatment of AIDS. However drug resistance is a persistent problem and new protease inhibitors are needed. Tipranavir, one of the protease inhibitors most recently approved for clinical use has been shown to be potent against viruses harbouring multidrug resistance mutations such as V82A and L90M, but even this drug is shown to lose potency due to certain mutations or mutation patterns. Thus 10 derivatives of the drug Tipranavir, chemically diverse from the initial hit were generated and screened to determine their ability to interact with protease. Further analysis revealed one unique compound with high binding ability from the initial hit and its possibility for new class of protease inhibitors is discussed in this report

Simulating HIV-1 Protease Mutations for Conferred Drug Resistance

A major challenge in the long-term management of HIV is drug resistance caused from high rate and error prone viral replication. To examine mechanisms of drug resistance within HIV-1 protease complexed with Darunavir, specific point mutations were placed in the protease amino acid sequence and molecular dynamic simulations were run. MATLAB and python scripts were developed to efficiently and consistently analyze simulation data. The team hypothesized that there would be a difference in inhibitor interactions and protein dynamic behavior in mutant variants compared to wild type. Although some aspects of increased resistance were seen with compounded mutations, overall this trend was not observed across every facet of our analysis

Retroviral proteases: correlating substrate recognition with both selected and native inhibitor resistance

A diverse group of retroviral proteases were analyzed to correlate mechanisms of substrate recognition with resistance to HIV-1 protease active-site inhibitors. Here it was shown that HIV-1 protease utilized a pathway common to many retroviral proteases, for recognition of mutated Gag/Pol cleavage sites, in order to become resistant to active-site inhibitors. While HIV-1 and HIV-2 resulted from independent cross-species transmissions of simian immunodeficiency virus into humans, HIV-2 has native primary resistance to many HIV-1 protease inhibitors as do many other retroviral proteases. The native multi-drug resistance of those proteases contributed to the lack of treatments for the respective life-long infections. Analysis of interactions between retroviral proteases and Gag/Pol substrates revealed that protease interactions weighted towards cleavage site residues P4-P4' resulted in inhibitor sensitivity, while interactions weighted towards residues P12-P5/P5'-P12' gave inhibitor resistance. In addition, a mechanism was identified for human T-cell leukemia virus type-1 protease that allowed re-weighting of the protease interactions with substrate residues P4-P4' and P12-P5/P5'-P12' using anti-parallel beta-sheets that connected the protease flaps to the substrate-grooves. Those anti-parallel beta-sheets are common to all studied retroviral proteases. The critical role of the retroviral protease substrate-grooves in substrate recognition and inhibitor resistance makes them a potential target

Minor mutations in HIV protease at baseline and appearance of primary mutation 90M in patients for whom their first protease-inhibitor ntiretroviral regimens failed

The association between minor mutations in human immunodeficiency virus (HIV) protease at baseline and development of common primary mutation 90M at virological failure (conferring some resistance to all protease inhibitors [PIs]) was evaluated in 93 previously drug-naive patients experiencing failure of their first PI-based antiretroviral regimens. In logistic regression analysis, the probability of accumulating a new 90M mutation at virological failure was associated with the presence at baseline of minor mutation 36I (naturally occurring in ∼25% of HIV clade B and in >80% of HIV non-clade-B viruses) (adjusted odds ratio, 13.5 [95% confidence interval, 1.89–95.6]; P=.009) and, possibly, of 10I/V. This suggests a potential role for the presence of 36I at baseline in predicting the appearance of 90M at virological failure

Distinguishing HIV-1 drug resistance, accessory, and viral fitness mutations using conditional selection pressure analysis of treated versus untreated patient samples

BACKGROUND: HIV can evolve drug resistance rapidly in response to new drug treatments, often through a combination of multiple mutations [1-3]. It would be useful to develop automated analyses of HIV sequence polymorphism that are able to predict drug resistance mutations, and to distinguish different types of functional roles among such mutations, for example, those that directly cause drug resistance, versus those that play an accessory role. Detecting functional interactions between mutations is essential for this classification. We have adapted a well-known measure of evolutionary selection pressure (K(a)/K(s)) and developed a conditional K(a)/K(s )approach to detect important interactions. RESULTS: We have applied this analysis to four independent HIV protease sequencing datasets: 50,000 clinical samples sequenced by Specialty Laboratories, Inc.; 1800 samples from patients treated with protease inhibitors; 2600 samples from untreated patients; 400 samples from untreated African patients. We have identified 428 mutation interactions in Specialty dataset with statistical significance and we were able to distinguish primary vs. accessory mutations for many well-studied examples. Amino acid interactions identified by conditional K(a)/K(s )matched 80 of 92 pair wise interactions found by a completely independent study of HIV protease (p-value for this match is significant: 10(-70)). Furthermore, K(a)/K(s )selection pressure results were highly reproducible among these independent datasets, both qualitatively and quantitatively, suggesting that they are detecting real drug-resistance and viral fitness mutations in the wild HIV-1 population. CONCLUSION: Conditional K(a)/K(s )analysis can detect mutation interactions and distinguish primary vs. accessory mutations in HIV-1. K(a)/K(s )analysis of treated vs. untreated patient data can distinguish drug-resistance vs. viral fitness mutations. Verification of these results would require longitudinal studies. The result provides a valuable resource for AIDS research and will be available for open access upon publication at REVIEWERS: This article was reviewed by Wen-Hsiung Li (nominated by Eugene V. Koonin), Robert Shafer (nominated by Eugene V. Koonin), and Shamil Sunyaev

Molecular dynamics simulation studies of drug resistance in HIV-1 protease

Overcoming the emergence of drug resistance in HIV is a major challenge to the scientific community. We use the established computational method of classical molecular dynamics to investigate the molecular basis of resistance in HIV-1 protease to the inhibitor saquinavir, using the wildtype and the G48V, L90M and G48V7L90M mutant HIV-1 proteases throughout this thesis. Firstly we reveal insights into a G48V mutation-assisted lateral drug escape mechanism from the protease active site. Such a mechanism allows drug escape without the full opening of the flaps of the protease. Furthermore, the mechanism is facilitated by differential drug-protease interactions, induced by mutations that take advantage of the conformational flexibility of the inhibitor. Secondly, we investigate the thermodynamic basis of binding of this set of mutants, using established 'approximate' free energy methods. The absolute and relative free energies of saquinavir binding to this set of proteases are successfully determined using our simulation and free energy analysis protocol and exhibit excellent correlation with experiment. This study is thus a template for an extended study on a larger range of HIV-1 protease-drug combinations. We describe a tool, the 'Binding Affinity Calculator', which has been designed to automate this protocol and which can be routinely applied, using high performance computing and grid technology, to meet the intensive computational demands of such an investigation. The free energy of binding of the NC-pl natural substrate cleaved by the protease is also deter mined. The enhanced flexibility of the substrate over the drug precludes the guarantee of a converged free energy result, even from the 10 ns duration of each simulation. However, qualitative insight into the thermodynamic basis of binding is gleaned as well as the effect of these mutations on the catalytic efficiency of the protease. Furthermore, we combine drug and substrate binding free energies to develop a metric for evaluating the approximate enzymatic fitness of a given mutant protease, computable directly from molecular simulation