Alexander L. Perryman

Non-classical transpeptidases yield insight into new antibacterials

Bacterial survival requires an intact peptidoglycan layer, a 3-dimensional exoskeleton that encapsulates the cytoplasmic membrane. Historically, the final steps of peptidoglycan synthesis are known to be carried out by d,d-transpeptidases, enzymes that are inhibited by the β-lactams which constitute >50% of all antibacterials in clinical use. Here, we show that the carbapenem subclass of β-lactams is distinctly effective not only because they inhibit d,d-transpeptidases and are poor substrates for β-lactamases, but primarily because they also inhibit non-classical transpeptidases, namely the l,d-transpeptidases, that generate the majority of linkages in the peptidoglycan of mycobacteria. We have characterized the molecular mechanisms responsible for inhibition of l,d-transpeptidases of M. tuberculosis and a range of bacteria, including ESKAPE pathogens, and utilized this information to design, synthesize and test simplified carbapenems with potent antibacterial activity.

A Virtual Screen Discovers Novel, Fragment-Sized Inhibitors of Mycobacterium tuberculosis InhA

Isoniazid (INH) is usually administered to treat latent Mycobacterium tuberculosis (Mtb) infections and is used in combination therapy to treat active tuberculosis (TB). Unfortunately, resistance to this drug is hampering its clinical effectiveness. INH is a prodrug that must be activated by Mtb catalase-peroxidase (KatG) before it can inhibit InhA (Mtb enoyl-acyl-carrier-protein reductase). Isoniazid-resistant cases of TB found in clinical settings usually involve mutations in or deletion of katG, which abrogate INH activation. Compounds that inhibit InhA without requiring prior activation by KatG would not be affected by this resistance mechanism and hence would display continued potency against these drug-resistant isolates of Mtb. Virtual screening experiments versus InhA in the GO Fight Against Malaria (GO FAM) project were designed to discover new scaffolds that display base-stacking interactions with the NAD cofactor. GO FAM experiments included targets from other pathogens, including Mtb, when they had structural similarity to a malaria target. Eight of the 16 soluble compounds identified by docking against InhA plus visual inspection were modest inhibitors and did not require prior activation by KatG. The best two inhibitors discovered are both fragment-sized compounds and displayed Ki values of 54 and 59 μM, respectively. Importantly, the novel inhibitors discovered have low structural similarity to known InhA inhibitors and thus help expand the number of chemotypes on which future medicinal chemistry efforts can be focused. These new fragment hits could eventually help advance the fight against INH-resistant Mtb strains, which pose a significant global health threat.

A Novel Small-Molecule Inhibitor of the Mycobacterium tuberculosis Demethylmenaquinone Methyltransferase MenG Is Bactericidal to Both Growing and Nutritionally Deprived Persister Cells

ABSTRACT Active tuberculosis (TB) and latent Mycobacterium tuberculosis infection both require lengthy treatments to achieve durable cures. This problem has partly been attributable to the existence of nonreplicating M. tuberculosis “persisters” that are difficult to kill using conventional anti-TB treatments. Compounds that target the respiratory pathway have the potential to kill both replicating and persistent M. tuberculosis and shorten TB treatment, as this pathway is essential in both metabolic states. We developed a novel respiratory pathway-specific whole-cell screen to identify new respiration inhibitors. This screen identified the biphenyl amide GSK1733953A (DG70) as a likely respiration inhibitor. DG70 inhibited both clinical drug-susceptible and drug-resistant M. tuberculosis strains. Whole-genome sequencing of DG70-resistant colonies identified mutations in menG (rv0558), which is responsible for the final step in menaquinone biosynthesis and required for respiration. Overexpression of menG from wild-type and DG70-resistant isolates increased the DG70 MIC by 4× and 8× to 30×, respectively. Radiolabeling and high-resolution mass spectrometry studies confirmed that DG70 inhibited the final step in menaquinone biosynthesis. DG70 also inhibited oxygen utilization and ATP biosynthesis, which was reversed by external menaquinone supplementation. DG70 was bactericidal in actively replicating cultures and in a nutritionally deprived persistence model. DG70 was synergistic with the first-line TB drugs isoniazid, rifampin, and the respiratory inhibitor bedaquiline. The combination of DG70 and isoniazid completely sterilized cultures in the persistence model by day 10. These results suggest that MenG is a good therapeutic target and that compounds targeting MenG along with standard TB therapy have the potential to shorten TB treatment duration. IMPORTANCE This study shows that MenG, which is responsible for the last enzymatic step in menaquinone biosynthesis, may be a good drug target for improving TB treatments. We describe the first small-molecule inhibitor (DG70) of Mycobacterium tuberculosis MenG and show that DG70 has characteristics that are highly desirable for a new antitubercular agent, including bactericidality against both actively growing and nonreplicating mycobacteria and synergy with several first-line drugs that are currently used to treat TB. IMPORTANCE This study shows that MenG, which is responsible for the last enzymatic step in menaquinone biosynthesis, may be a good drug target for improving TB treatments. We describe the first small-molecule inhibitor (DG70) of Mycobacterium tuberculosis MenG and show that DG70 has characteristics that are highly desirable for a new antitubercular agent, including bactericidality against both actively growing and nonreplicating mycobacteria and synergy with several first-line drugs that are currently used to treat TB.

Biological Evaluation of Potent Triclosan‐Derived Inhibitors of the Enoyl–Acyl Carrier Protein Reductase InhA in Drug‐Sensitive and Drug‐Resistant Strains of Mycobacterium tuberculosis

New triclosan (TRC) analogues were evaluated for their activity against the enoyl–acyl carrier protein reductase InhA in Mycobacterium tuberculosis (Mtb). TRC is a well‐known inhibitor of InhA, and specific modifications to its positions 5 and 4′ afforded 27 derivatives; of these compounds, seven derivatives showed improved potency over that of TRC. These analogues were active against both drug‐susceptible and drug‐resistant Mtb strains. The most active compound in this series, 4‐(n‐butyl)‐1,2,3‐triazolyl TRC derivative 3, had an MIC value of 0.6 μg mL−1 (1.5 μM) against wild‐type Mtb. At a concentration equal to its MIC, this compound inhibited purified InhA by 98 %, and showed an IC50 value of 90 nM. Compound 3 and the 5‐methylisoxazole‐modified TRC 14 were able to inhibit the biosynthesis of mycolic acids. Furthermore, mc24914, an Mtb strain overexpressing inhA, was found to be less susceptible to compounds 3 and 14, supporting the notion that InhA is the likely molecular target of the TRC derivatives presented herein.

OpenZika: An IBM World Community Grid Project to Accelerate Zika Virus Drug Discovery

The Zika virus outbreak in the Americas has caused global concern. To help accelerate this fight against Zika, we launched the OpenZika project. OpenZika is an IBM World Community Grid Project that uses distributed computing on millions of computers and Android devices to run docking experiments, in order to dock tens of millions of drug-like compounds against crystal structures and homology models of Zika proteins (and other related flavivirus targets). This will enable the identification of new candidates that can then be tested in vitro, to advance the discovery and development of new antiviral drugs against the Zika virus. The docking data is being made openly accessible so that all members of the global research community can use it to further advance drug discovery studies against Zika and other related flaviviruses.

Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014–2015)

The renewed urgency to develop new treatments for Mycobacterium tuberculosis (Mtb) infection has resulted in large-scale phenotypic screening and thousands of new active compounds in vitro. The next challenge is to identify candidates to pursue in a mouse in vivo efficacy model as a step to predicting clinical efficacy. We previously analyzed over 70 years of this mouse in vivo efficacy data, which we used to generate and validate machine learning models. Curation of 60 additional small molecules with in vivo data published in 2014 and 2015 was undertaken to further test these models. This represents a much larger test set than for the previous models. Several computational approaches have now been applied to analyze these molecules and compare their molecular properties beyond those attempted previously. Our previous machine learning models have been updated, and a novel aspect has been added in the form of mouse liver microsomal half-life (MLM t1/2) and in vitro-based Mtb models incorporating cytotoxicity data that were used to predict in vivo activity for comparison. Our best Mtbin vivo models possess fivefold ROC values > 0.7, sensitivity > 80%, and concordance > 60%, while the best specificity value is >40%. Use of an MLM t1/2 Bayesian model affords comparable results for scoring the 60 compounds tested. Combining MLM stability and in vitroMtb models in a novel consensus workflow in the best cases has a positive predicted value (hit rate) > 77%. Our results indicate that Bayesian models constructed with literature in vivoMtb data generated by different laboratories in various mouse models can have predictive value and may be used alongside MLM t1/2 and in vitro-based Mtb models to assist in selecting antitubercular compounds with desirable in vivo efficacy. We demonstrate for the first time that consensus models of any kind can be used to predict in vivo activity for Mtb. In addition, we describe a new clustering method for data visualization and apply this to the in vivo training and test data, ultimately making the method accessible in a mobile app.

Identification of a novel drug lead that inhibits HCV infection and cell-to-cell transmission by targeting the HCV E2 glycoprotein.

Hepatitis C Virus (HCV) infects 200 million individuals worldwide. Although several FDA approved drugs targeting the HCV serine protease and polymerase have shown promising results, there is a need for better drugs that are effective in treating a broader range of HCV genotypes and subtypes without being used in combination with interferon and/or ribavirin. Recently, two crystal structures of the core of the HCV E2 protein (E2c) have been determined, providing structural information that can now be used to target the E2 protein and develop drugs that disrupt the early stages of HCV infection by blocking E2’s interaction with different host factors. Using the E2c structure as a template, we have created a structural model of the E2 protein core (residues 421–645) that contains the three amino acid segments that are not present in either structure. Computational docking of a diverse library of 1,715 small molecules to this model led to the identification of a set of 34 ligands predicted to bind near conserved amino acid residues involved in the HCV E2: CD81 interaction. Surface plasmon resonance detection was used to screen the ligand set for binding to recombinant E2 protein, and the best binders were subsequently tested to identify compounds that inhibit the infection of Huh-7 cells by HCV. One compound, 281816, blocked E2 binding to CD81 and inhibited HCV infection in a genotype-independent manner with IC50’s ranging from 2.2 µM to 4.6 µM. 281816 blocked the early and late steps of cell-free HCV entry and also abrogated the cell-to-cell transmission of HCV. Collectively the results obtained with this new structural model of E2c suggest the development of small molecule inhibitors such as 281816 that target E2 and disrupt its interaction with CD81 may provide a new paradigm for HCV treatment.

Molecular dynamics simulations of Zika virus NS3 helicase: Insights into RNA binding site activity

America is still suffering with the outbreak of Zika virus (ZIKV) infection. Congenital ZIKV syndrome has already caused a public health emergency of international concern. However, there are still no vaccines to prevent or drugs to treat the infection caused by ZIKV. The ZIKV NS3 helicase (NS3h) protein is a promising target for drug discovery due to its essential role in viral genome replication. NS3h unwinds the viral RNA to enable the replication of the viral genome by the NS5 protein. NS3h contains two important binding sites: the NTPase binding site and the RNA binding site. Here, we used molecular dynamics (MD) simulations to study the molecular behavior of ZIKV NS3h in the presence and absence of ssRNA and the potential implications for NS3h activity and inhibition. Although there is conformational variability and poor electron densities of the RNA binding loop in various apo flaviviruses NS3h crystallographic structures, the MD trajectories of NS3h-ssRNA demonstrated that the RNA binding loop becomes more stable when NS3h is occupied by RNA. Our results suggest that the presence of RNA generates important interactions with the RNA binding loop, and these interactions stabilize the loop sufficiently that it remains in a closed conformation. This closed conformation likely keeps the ssRNA bound to the protein for a sufficient duration to enable the unwinding/replication activities of NS3h to occur. In addition, conformational changes of this RNA binding loop can change the nature and location of the optimal ligand binding site, according to ligand binding site prediction results. These are important findings to help guide the design and discovery of new inhibitors of NS3h as promising compounds to treat the ZIKV infection.

Addressing the Metabolic Stability of Antituberculars through Machine Learning

We present the first prospective application of our mouse liver microsomal (MLM) stability Bayesian model. CD117, an antitubercular thienopyrimidine tool compound that suffers from metabolic instability (MLM t1/2 < 1 min), was utilized to assess the predictive power of our new MLM stability model. The S-substituent was removed, a set of commercial reagents was utilized to construct a virtual library of 411 analogues, and our MLM stability model was applied to prioritize 13 analogues for synthesis and biological profiling. In MLM stability assays, all 13 analogues had superior metabolic stability to the parent compound, and six new analogues had acceptable MLM t1/2 values greater than or equal to 60 min. It is noteworthy that whole-cell efficacy and lack of relative mammalian cell cytotoxicity could not be predicted simultaneously. These results support the utility of our new MLM stability model in chemical tool and drug discovery optimization efforts.

Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery

Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences and 1.7 million deaths. The need to develop new treatments for those infected with Mycobacterium tuberculosis ( Mtb) has led to many large-scale phenotypic screens and many thousands of new active compounds identified in vitro. However, with limited funding, efforts to discover new active molecules against Mtb needs to be more efficient. Several computational machine learning approaches have been shown to have good enrichment and hit rates. We have curated small molecule Mtb data and developed new models with a total of 18,886 molecules with activity cutoffs of 10 μM, 1 μM, and 100 nM. These data sets were used to evaluate different machine learning methods (including deep learning) and metrics and to generate predictions for additional molecules published in 2017. One Mtb model, a combined in vitro and in vivo data Bayesian model at a 100 nM activity yielded the following metrics for 5-fold cross validation: accuracy = 0.88, precision = 0.22, recall = 0.91, specificity = 0.88, kappa = 0.31, and MCC = 0.41. We have also curated an evaluation set ( n = 153 compounds) published in 2017, and when used to test our model, it showed the comparable statistics (accuracy = 0.83, precision = 0.27, recall = 1.00, specificity = 0.81, kappa = 0.36, and MCC = 0.47). We have also compared these models with additional machine learning algorithms showing Bayesian machine learning models constructed with literature Mtb data generated by different laboratories generally were equivalent to or outperformed deep neural networks with external test sets. Finally, we have also compared our training and test sets to show they were suitably diverse and different in order to represent useful evaluation sets. Such Mtb machine learning models could help prioritize compounds for testing in vitro and in vivo.