Design of substituted tetrahydrofuran derivatives for HIV-1 protease inhibitors: synthesis, biological evaluation, and X-ray structural studies.

Substituted tetrahydrofuran derivatives were designed and synthesized to serve as the P2 ligand for a series of potent HIV-1 protease inhibitors. Both enantiomers of the tetrahydrofuran derivatives were synthesized stereoselectivity in optically active forms using lipase-PS catalyzed enzymatic resolution as the key step. These tetrahydrofuran derivatives are designed to promote hydrogen bonding and van der Waals interactions with the backbone atoms in the S2 subsite of the HIV-1 protease active site. Several inhibitors displayed very potent HIV-1 protease inhibitory activity. A high-resolution X-ray crystal structure of an inhibitor-bound HIV-1 protease provided important insight into the ligand binding site interactions in the active site.

Evaluation of darunavir-derived HIV-1 protease inhibitors incorporating P2' amide-derivatives: Synthesis, biological evaluation and structural studies.

We report here the synthesis and biological evaluation of darunavir derived HIV-1 protease inhibitors and their functional effect on enzyme inhibition and antiviral activity in MT-2 cell lines. The P2' 4-amino functionality was modified to make a number of amide derivatives to interact with residues in the S2' subsite of the HIV-1 protease active site. Several compounds exhibited picomolar enzyme inhibitory and low nanomolar antiviral activity. The X-ray crystal structure of the chloroacetate derivative bound to HIV-1 protease was determined. Interestingly, the active chloroacetate group converted to the acetate functionality during X-ray exposure. The structure revealed that the P2' carboxamide functionality makes enhanced hydrogen bonding interactions with the backbone atoms in the S2'-subsite.

Discovery of a new flavin N5-adduct in a tyrosine to phenylalanine variant of d-Arginine dehydrogenase.

d-Arginine dehydrogenase from Pseudomonas aeruginosa (PaDADH) catalyzes the flavin-dependent oxidation of d-arginine and other d-amino acids. Here, we report the crystal structure at 1.29 Å resolution for PaDADH-Y249F expressed and co-crystallized with d-arginine. The overall structure of PaDADH-Y249F resembled PaDADH-WT, but the electron density for the flavin cofactor was ambiguous, suggesting the presence of modified flavins. Electron density maps and mass spectrometric analysis confirmed the presence of both N5-(4-guanidino-oxobutyl)-FAD and 6-OH-FAD in a single crystal of PaDADH-Y249F and helped with the further refinement of the X-ray crystal structure. The versatility of the reduced flavin is apparent in the PaDADH-Y249F structure and is evidenced by the multiple functions it can perform in the same active site.

A Single-Point Mutation in d-Arginine Dehydrogenase Unlocks a Transient Conformational State Resulting in Altered Cofactor Reactivity.

Proteins are inherently dynamic, and proper enzyme function relies on conformational flexibility. In this study, we demonstrated how an active site residue changes an enzyme's reactivity by modulating fluctuations between conformational states. Replacement of tyrosine 249 (Y249) with phenylalanine in the active site of the flavin-dependent d-arginine dehydrogenase yielded an enzyme with both an active yellow FAD (Y249F-y) and an inactive chemically modified green FAD, identified as 6-OH-FAD (Y249F-g) through various spectroscopic techniques. Structural investigation of Y249F-g and Y249F-y variants by comparison to the wild-type enzyme showed no differences in the overall protein structure and fold. A closer observation of the active site of the Y249F-y enzyme revealed an alternative conformation for some active site residues and the flavin cofactor. Molecular dynamics simulations probed the alternate conformations observed in the Y249F-y enzyme structure and showed that the enzyme variant with FAD samples a metastable conformational state, not available to the wild-type enzyme. Hybrid quantum/molecular mechanical calculations identified differences in flavin electronics between the wild type and the alternate conformation of the Y249F-y enzyme. The computational studies further indicated that the alternate conformation in the Y249F-y enzyme is responsible for the higher spin density at the C6 atom of flavin, which is consistent with the formation of 6-OH-FAD in the variant enzyme. The observations in this study are consistent with an alternate conformational space that results in fine-tuning the microenvironment around a versatile cofactor playing a critical role in enzyme function.

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 PRS17 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 PRS17 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 PRS17 with the exception of amprenavir. Crystal structures of PRS17/2 and PRS17/3 reveal how these inhibitors target the two active site mutations of PRS17. 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 PRS17.

Evolution of drug resistance in HIV protease.

BACKGROUND: Drug resistance is a critical problem limiting effective antiviral therapy for HIV/AIDS. Computational techniques for predicting drug resistance profiles from genomic data can accelerate the appropriate choice of therapy. These techniques can also be used to identify protease mutants for experimental studies of resistance and thereby assist in the development of next-generation therapies. Few studies, however, have assessed the evolution of resistance from genotype-phenotype data. RESULTS: The machine learning produced highly accurate and robust classification of resistance to HIV protease inhibitors. Genotype data were mapped to the enzyme structure and encoded using Delaunay triangulation. Estimates of evolutionary relationships, based on this encoding, and using Minimum Spanning Trees, showed clusters of mutations that closely resemble the wild type. These clusters appear to evolve uniquely to more resistant phenotypes. CONCLUSIONS: Using the triangulation metric and spanning trees results in paths that are consistent with evolutionary theory. The majority of the paths show bifurcation, namely they switch once from non-resistant to resistant or from resistant to non-resistant. Paths that lose resistance almost uniformly have far lower levels of resistance than those which either gain resistance or are stable. This strongly suggests that selection for stability in the face of a rapid rate of mutation is as important as selection for resistance in retroviral systems.

Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences alone.

Coronaviruses infect many animals, including humans, due to interspecies transmission. Three of the known human coronaviruses: MERS, SARS-CoV-1, and SARS-CoV-2, the pathogen for the COVID-19 pandemic, cause severe disease. Improved methods to predict host specificity of coronaviruses will be valuable for identifying and controlling future outbreaks. The coronavirus S protein plays a key role in host specificity by attaching the virus to receptors on the cell membrane. We analyzed 1238 spike sequences for their host specificity. Spike sequences readily segregate in t-SNE embeddings into clusters of similar hosts and/or virus species. Machine learning with SVM, Logistic Regression, Decision Tree, Random Forest gave high average accuracies, F1 scores, sensitivities and specificities of 0.95-0.99. Importantly, sites identified by Decision Tree correspond to protein regions with known biological importance. These results demonstrate that spike sequences alone can be used to predict host specificity.

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.

Structural studies of antiviral inhibitor with HIV-1 protease bearing drug resistant substitutions of V32I, I47V and V82I.

HIV-1 protease inhibitors are effective in HIV/AIDS therapy, although drug resistance is a severe problem. This study examines the effects of four investigational inhibitors against HIV-1 protease with drug resistant mutations of V32I, I47V and V82I (PRTri) that model the inhibitor-binding site of HIV-2 protease. These inhibitors contain diverse chemical modifications on the darunavir scaffold and form new interactions with wild type protease, however, the measured inhibition constants for PRTri mutant range from 17 to 40 nM or significantly worse than picomolar values reported for wild type enzyme. The X-ray crystal structure of PRTri mutant in complex with inhibitor 1 at 1.5 Šresolution shows minor changes in interactions with inhibitor compared with the corresponding wild type PR complex. Instead, the basic amine at P2 of inhibitor together with mutation V82I induces two alternate conformations for the side chain of Arg8 with new interactions with inhibitor and Leu10. Hence, inhibition is influenced by small coordinated changes in hydrophobic interactions.

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.

Design, synthesis, X-ray studies, and biological evaluation of novel macrocyclic HIV-1 protease inhibitors involving the P1'-P2' ligands.

Design, synthesis, and evaluation of a new class of HIV-1 protease inhibitors containing diverse flexible macrocyclic P1'-P2' tethers are reported. Inhibitor 5a with a pyrrolidinone-derived macrocycle exhibited favorable enzyme inhibitory and antiviral activity (Ki=13.2nM, IC50=22nM). Further incorporation of heteroatoms in the macrocyclic skeleton provided macrocyclic inhibitors 5m and 5o. These compounds showed excellent HIV-1 protease inhibitory (Ki=62pM and 14pM, respectively) and antiviral activity (IC50=5.3nM and 2.0nM, respectively). Inhibitor 5o also remained highly potent against a DRV-resistant HIV-1 variant.

Design of novel HIV-1 protease inhibitors incorporating isophthalamide-derived P2-P3 ligands: Synthesis, biological evaluation and X-ray structural studies of inhibitor-HIV-1 protease complex.

Based upon molecular insights from the X-ray structures of inhibitor-bound HIV-1 protease complexes, we have designed a series of isophthalamide-derived inhibitors incorporating substituted pyrrolidines, piperidines and thiazolidines as P2-P3 ligands for specific interactions in the S2-S3 extended site. Compound 4b has shown an enzyme Ki of 0.025nM and antiviral IC50 of 69nM. An X-ray crystal structure of inhibitor 4b-HIV-1 protease complex was determined at 1.33Å resolution. We have also determined X-ray structure of 3b-bound HIV-1 protease at 1.27Å resolution. These structures revealed important molecular insight into the inhibitor-HIV-1 protease interactions in the active site.

Automated prediction of HIV drug resistance from genotype data.

BACKGROUND: HIV/AIDS is a serious threat to public health. The emergence of drug resistance mutations diminishes the effectiveness of drug therapy for HIV/AIDS. Developing a computational prediction of drug resistance phenotype will enable efficient and timely selection of the best treatment regimens. RESULTS: A unified encoding of protein sequence and structure was used as the feature vector for predicting phenotypic resistance from genotype data. Two machine learning algorithms, Random Forest and K-nearest neighbor, were used. The prediction accuracies were examined by five-fold cross-validation on the genotype-phenotype datasets. A supervised machine learning approach for automatic prediction of drug resistance was developed to handle genotype-phenotype datasets of HIV protease (PR) and reverse transcriptase (RT). It predicts the drug resistance phenotype and its relative severity from a query sequence. The accuracy of the classification was higher than 0.973 for eight PR inhibitors and 0.986 for ten RT inhibitors, respectively. The overall cross-validated regression R(2)-values for the severity of drug resistance were 0.772-0.953 for 8 PR inhibitors and 0.773-0.995 for 10 RT inhibitors. CONCLUSIONS: Machine learning using a unified encoding of sequence and protein structure as a feature vector provides an accurate prediction of drug resistance from genotype data. A practical webserver for clinicians has been implemented.

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.

We have systematically validated the activity and inhibition of a HIV-1 protease (PR) variant bearing 17 mutations (PR(S17)), selected to represent high resistance by machine learning on genotype-phenotype data. Three of five mutations in PR(S17) correlating with major drug resistance, M46L, G48V, and V82S, and five of 11 natural variations differ from the mutations in two clinically derived extreme mutants, PR20 and PR22 bearing 19 and 22 mutations, respectively. PR(S17), which forms a stable dimer (<10 nM), is ∼10- and 2-fold less efficient in processing the Gag polyprotein than the wild type and PR20, respectively, but maintains the same cleavage order. Isolation of a model precursor of PR(S17) flanked by the 56-amino acid transframe region (TFP-p6pol) at its N-terminus, which is impossible upon expression of an analogous PR20 precursor, allowed systematic comparison of inhibition of TFP-p6pol-PR(S17) and mature PR(S17). Resistance of PR(S17) to eight protease inhibitors (PIs) relative to PR (Ki) increases by 1.5-5 orders of magnitude from 0.01 to 8.4 µM. Amprenavir, darunavir, atazanavir, and lopinavir, the most effective of the eight PIs, inhibit precursor autoprocessing at the p6pol/PR site with IC50 values ranging from ∼7.5 to 60 µM. Thus, this process, crucial for stable dimer formation, shows inhibition ∼200-800-fold weaker than that of the mature PR(S17). TFP/p6pol cleavage, which occurs faster, is inhibited even more weakly by all PIs except darunavir (IC50 = 15 µM); amprenavir shows a 2-fold increase in IC50 (∼15 µM), and atazanavir and lopinavir show increased IC50 values of >42 and >70 µM, respectively.

In vitro heme biotransformation by the HupZ enzyme from Group A streptococcus.

In Group A streptococcus (GAS), the metallorepressor MtsR regulates iron homeostasis. Here we describe a new MtsR-repressed gene, which we named hupZ (heme utilization protein). A recombinant HupZ protein was purified bound to heme from Escherichia coli grown in the presence of 5-aminolevulinic acid and iron. HupZ specifically binds heme with stoichiometry of 1:1. The addition of NADPH to heme-bound HupZ (in the presence of cytochrome P450 reductase, NADPH-regeneration system and catalase) triggered progressive decrease of the HupZ Soret band and the appearance of an absorption peak at 660 nm that was resistance to hydrolytic conditions. No spectral changes were observed when ferredoxin and ferredoxin reductase were used as redox partners. Differential spectroscopy with myoglobin or with the ferrous chelator, ferrozine, confirmed that carbon monoxide and free iron are produced during the reaction. ApoHupZ was crystallized as a homodimer with a split ß-barrel conformation in each monomer comprising six ß strands and three α helices. This structure resembles the split ß-barrel domain shared by the members of a recently described family of heme degrading enzymes. However, HupZ is smaller and lacks key residues found in the proteins of the latter group. Phylogenetic analysis places HupZ on a clade separated from those for previously described heme oxygenases. In summary, we have identified a new GAS enzyme-containing split ß-barrel and capable of heme biotransformation in vitro; to the best of our knowledge, this is the first enzyme among Streptococcus species with such activity.

Tackling the problem of HIV drug resistance.

The virally-encoded HIV-1 protease is an effective target for antiviral drugs, however, treatment for HIV infections is limited by the prevalence of drug resistant viral mutants. In this review, we describe our three-pronged approach to analyze and combat drug resistance. Understanding the molecular basis for resistance due to protease inhibitors is a key initial step in this approach. This knowledge is being employed for the design of new, improved inhibitors with high affinity for resistant mutants as well as wild type enzyme. In parallel with experimental studies of diverse mutants and inhibitory compounds, we are developing efficient algorithms to predict drug resistance phenotype from genotype data. This approach has important practical applications in the clinic where genotyping is recommended for individuals with new infections.

Long-Range Electrostatics-Induced Two-Proton Transfer Captured by Neutron Crystallography in an Enzyme Catalytic Site.

Neutron crystallography was used to directly locate two protons before and after a pH-induced two-proton transfer between catalytic aspartic acid residues and the hydroxy group of the bound clinical drug darunavir, located in the catalytic site of enzyme HIV-1 protease. The two-proton transfer is triggered by electrostatic effects arising from protonation state changes of surface residues far from the active site. The mechanism and pH effect are supported by quantum mechanics/molecular mechanics (QM/MM) calculations. The low-pH proton configuration in the catalytic site is deemed critical for the catalytic action of this enzyme and may apply more generally to other aspartic proteases. Neutrons therefore represent a superb probe to obtain structural details for proton transfer reactions in biological systems at a truly atomic level.

Identifying representative drug resistant mutants of HIV.

BACKGROUND: Drug resistance is one of the most important causes for failure of anti-AIDS treatment. During therapy, multiple mutations accumulate in the HIV genome, eventually rendering the drugs ineffective in blocking replication of the mutant virus. The huge number of possible mutants precludes experimental analysis to explore the molecular mechanisms of resistance and develop improved antiviral drugs. RESULTS: In order to solve this problem, we have developed a new algorithm to reveal the most representative mutants from the whole drug resistant mutant database based on our newly proposed unified protein sequence and 3D structure encoding method. Mean shift clustering and multiple regression analysis were applied on genotype-resistance data for mutants of HIV protease and reverse transcriptase. This approach successfully chooses less than 100 mutants with the highest resistance to each drug out of about 10K in the whole database. When considering high level resistance to multiple drugs, the numbers reduce to one or two representative mutants. CONCLUSION: This approach for predicting the most representative mutants for each drug has major importance for experimental verification since the results provide a small number of representative sequences, which will be amenable for in vitro testing and characterization of the expressed mutant proteins.

The combined structural and kinetic characterization of a bacterial nitronate monooxygenase from Pseudomonas aeruginosa PAO1 establishes NMO class I and II.

Nitronate monooxygenase (NMO) oxidizes the mitochondrial toxin propionate 3-nitronate (P3N) to malonate semialdehyde. The enzyme has been previously characterized biochemically in fungi, but no structural information is available. Based on amino acid similarity 4,985 genes are annotated in the GenBank(TM) as NMO. Of these, 4,424 (i.e. 89%) are bacterial genes, including several Pseudomonads that have been shown to use P3N as growth substrate. Here, we have cloned and expressed the gene pa4202 of Pseudomonas aeruginosa PAO1, purified the resulting protein, and characterized it. The enzyme is active on P3N and other alkyl nitronates, but cannot oxidize nitroalkanes. P3N is the best substrate at pH 7.5 and atmospheric oxygen with k(cat)(app)/K(m)(app) of 12 × 10(6) M(-1) s(-1), k(cat)(app) of 1300 s(-1), and K(m)(app) of 110 µm. Anerobic reduction of the enzyme with P3N yields a flavosemiquinone, which is formed within 7.5 ms, consistent with this species being a catalytic intermediate. Absorption spectroscopy, mass spectrometry, and x-ray crystallography demonstrate a tightly, non-covalently bound FMN in the active site of the enzyme. Thus, PA4202 is the first NMO identified and characterized in bacteria. The x-ray crystal structure of the enzyme was solved at 1.44 Å, showing a TIM barrel-fold. Four motifs in common with the biochemically characterized NMO from Cyberlindnera saturnus are identified in the structure of bacterial NMO, defining Class I NMO, which includes bacterial, fungal, and two animal NMOs. Notably, the only other NMO from Neurospora crassa for which biochemical evidence is available lacks the four motifs, defining Class II NMO.

Structure-based design, synthesis, X-ray studies, and biological evaluation of novel HIV-1 protease inhibitors containing isophthalamide-derived P2-ligands.

We describe the design, synthesis and biological evaluation of a series of novel HIV-1 protease inhibitors bearing isophthalamide derivatives as the P2-P3 ligands. We have investigated a range of acyclic and heterocyclic amides as the extended P2-P3 ligands. These inhibitors displayed good to excellent HIV-1 protease inhibitory activity. Also, a number of inhibitors showed very good antiviral activity in MT cells. Compound 5n has shown an enzyme Ki of 0.17 nM and antiviral IC50 of 14 nM. An X-ray crystal structure of inhibitor 5o-bound to HIV-1 protease was determined at 1.11Å resolution. This structure revealed important molecular insight into the inhibitor-HIV-1 protease interactions in the active site.