Trace gas oxidation sustains energy needs of a thermophilic archaeon at suboptimal temperatures.

Diverse aerobic bacteria use atmospheric hydrogen (H2) and carbon monoxide (CO) as energy sources to support growth and survival. Such trace gas oxidation is recognised as a globally significant process that serves as the main sink in the biogeochemical H2 cycle and sustains microbial biodiversity in oligotrophic ecosystems. However, it is unclear whether archaea can also use atmospheric H2. Here we show that a thermoacidophilic archaeon, Acidianus brierleyi (Thermoproteota), constitutively consumes H2 and CO to sub-atmospheric levels. Oxidation occurs across a wide range of temperatures (10 to 70 °C) and enhances ATP production during starvation-induced persistence under temperate conditions. The genome of A. brierleyi encodes a canonical CO dehydrogenase and four distinct [NiFe]-hydrogenases, which are differentially produced in response to electron donor and acceptor availability. Another archaeon, Metallosphaera sedula, can also oxidize atmospheric H2. Our results suggest that trace gas oxidation is a common trait of Sulfolobales archaea and may play a role in their survival and niche expansion, including during dispersal through temperate environments.

A Phenotypic Screen Identifies Potent DPP9 Inhibitors Capable of Killing HIV-1 Infected Cells.

Although current antiretroviral therapy can control HIV-1 replication and prevent disease progression, it is not curative. Identifying mechanisms that can lead to eradication of persistent viral reservoirs in people living with HIV-1 (PLWH) remains an outstanding challenge to achieving cure. Utilizing a phenotypic screen, we identified a novel chemical class capable of killing HIV-1 infected peripheral blood mononuclear cells. Tool compounds ICeD-1 and ICeD-2 ("inducer of cell death-1 and 2"), optimized for potency and selectivity from screening hits, were used to deconvolute the mechanism of action using a combination of chemoproteomic, biochemical, pharmacological, and genetic approaches. We determined that these compounds function by modulating dipeptidyl peptidase 9 (DPP9) and activating the caspase recruitment domain family member 8 (CARD8) inflammasome. Efficacy of ICeD-1 and ICeD-2 was dependent on HIV-1 protease activity and synergistic with efavirenz, which promotes premature activation of HIV-1 protease at high concentrations in infected cells. This in vitro synergy lowers the efficacious cell kill concentration of efavirenz to a clinically relevant dose at concentrations of ICeD-1 or ICeD-2 that do not result in complete DPP9 inhibition. These results suggest engagement of the pyroptotic pathway as a potential approach to eliminate HIV-1 infected cells.

Prediction Accuracy of Production ADMET Models as a Function of Version: Activity Cliffs Rule.

As with many other institutions, our company maintains many quantitative structure-activity relationship (QSAR) models of absorption, distribution, metabolism, excretion, and toxicity (ADMET) end points and updates the models regularly. We recently examined version-to-version predictivity for these models over a period of 10 years. In this approach we monitor the goodness of prediction of new molecules relative to the training set of model version V before they are incorporated in the updated model V+1. Using a cell-based permeability assay (Papp) as an example, we illustrate how the QSAR models made from this data are generally predictive and can be utilized to enrich chemical designs and synthesis. Despite the obvious utility of these models, we turned up unexpected behavior in Papp and other ADMET activities for which the explanation is not obvious. One such behavior is that the apparent predictivity of the models as measured by root-mean-square-error can vary greatly from version to version and is sometimes very poor. One intuitively appealing explanation is that the observed activities of the new molecules fall outside the bulk of activities in the training set. Alternatively, one may think that the new molecules are exploring different regions of chemical space than the training set. However, the real explanation has to do with activity cliffs. If the observed activities of the new molecules are different than expected based on similar molecules in the training set, the predictions will be less accurate. This is true for all our ADMET end points.

Development of ProTx-II Analogues as Highly Selective Peptide Blockers of Nav1.7 for the Treatment of Pain.

Inhibitor cystine knot peptides, derived from venom, have evolved to block ion channel function but are often toxic when dosed at pharmacologically relevant levels in vivo. The article describes the design of analogues of ProTx-II that safely display systemic in vivo blocking of Nav1.7, resulting in a latency of response to thermal stimuli in rodents. The new designs achieve a better in vivo profile by improving ion channel selectivity and limiting the ability of the peptides to cause mast cell degranulation. The design rationale, structural modeling, in vitro profiles, and rat tail flick outcomes are disclosed and discussed.

Discovery of an Anion-Dependent Farnesyltransferase Inhibitor from a Phenotypic Screen.

By employing a phenotypic screen, a set of compounds, exemplified by 1, were identified which potentiate the ability of histone deacetylase inhibitor vorinostat to reverse HIV latency. Proteome enrichment followed by quantitative mass spectrometric analysis employing a modified analogue of 1 as affinity bait identified farnesyl transferase (FTase) as the primary interacting protein in cell lysates. This ligand-FTase binding interaction was confirmed via X-ray crystallography and temperature dependent fluorescence studies, despite 1 lacking structural and binding similarity to known FTase inhibitors. Although multiple lines of evidence established the binding interaction, these ligands exhibited minimal inhibitory activity in a cell-free biochemical FTase inhibition assay. Subsequent modification of the biochemical assay by increasing anion concentration demonstrated FTase inhibitory activity in this novel class. We propose 1 binds together with the anion in the active site to inhibit farnesyl transferase. Implications for phenotypic screening deconvolution and HIV reactivation are discussed.

Nearest Neighbor Gaussian Process for Quantitative Structure-Activity Relationships.

While Gaussian process models are typically restricted to smaller data sets, we propose a variation which extends its applicability to the larger data sets common in the industrial drug discovery space, making it relatively novel in the quantitative structure-activity relationship (QSAR) field. By incorporating locality-sensitive hashing for fast nearest neighbor searches, the nearest neighbor Gaussian process model makes predictions with time complexity that is sub-linear with the sample size. The model can be efficiently built, permitting rapid updates to prevent degradation as new data is collected. Given its small number of hyperparameters, it is robust against overfitting and generalizes about as well as other common QSAR models. Like the usual Gaussian process model, it natively produces principled and well-calibrated uncertainty estimates on its predictions. We compare this new model with implementations of random forest, light gradient boosting, and k-nearest neighbors to highlight these promising advantages. The code for the nearest neighbor Gaussian process is available at https://github.com/Merck/nngp.

Augmenting Hit Identification by Virtual Screening Techniques in Small Molecule Drug Discovery.

Two orthogonal approaches for hit identification in drug discovery are large-scale in vitro and in silico screening. In recent years, due to the emergence of new targets and a rapid increase in the size of the readily synthesizable chemical space, there is a growing emphasis on the integration of the two techniques to improve the hit finding efficiency. Here, we highlight three examples of drug discovery projects at Merck & Co., Inc., Kenilworth, NJ, USA in which different virtual screening (VS) techniques, each specifically tailored to leverage knowledge available for the target, were utilized to augment the selection of high-quality chemical matter for in vitro assays and to enhance the diversity and tractability of hits. Central to success is a fully integrated workflow combining in silico and experimental expertise at every stage of the hit identification process. We advocate that workflows encompassing VS as part of an integrated hit finding plan should be widely adopted to accelerate hit identification and foster cross-functional collaborations in modern drug discovery.

Experimental Error, Kurtosis, Activity Cliffs, and Methodology: What Limits the Predictivity of Quantitative Structure-Activity Relationship Models?

Given a particular descriptor/method combination, some quantitative structure-activity relationship (QSAR) datasets are very predictive by random-split cross-validation while others are not. Recent literature in modelability suggests that the limiting issue for predictivity is in the data, not the QSAR methodology, and the limits are due to activity cliffs. Here, we investigate, on in-house data, the relative usefulness of experimental error, distribution of the activities, and activity cliff metrics in determining how predictive a dataset is likely to be. We include unmodified in-house datasets, datasets that should be perfectly predictive based only on the chemical structure, datasets where the distribution of activities is manipulated, and datasets that include a known amount of added noise. We find that activity cliff metrics determine predictivity better than the other metrics we investigated, whatever the type of dataset, consistent with the modelability literature. However, such metrics cannot distinguish real activity cliffs due to large uncertainties in the activities. We also show that a number of modern QSAR methods, and some alternative descriptors, are equally bad at predicting the activities of compounds on activity cliffs, consistent with the assumptions behind "modelability." Finally, we relate time-split predictivity with random-split predictivity and show that different coverages of chemical space are at least as important as uncertainty in activity and/or activity cliffs in limiting predictivity.

Diversity & tractability revisited in collaborative small molecule phenotypic screening library design.

Identification of purposeful chemical matter on a broad range of drug targets is of high importance to the pharmaceutical industry. However, disease-relevant but more complex hit-finding plans require flexibility regarding the subset of the compounds that we screen. Herein we describe a strategy to design high-quality small molecule screening subsets of two different sizes to cope with a rapidly changing early discovery portfolio. The approach taken balances chemical tractability, chemical diversity and biological target coverage. Furthermore, using surveys, we actively involved chemists within our company in the selection process of the diversity decks to ensure current medicinal chemistry principles were incorporated. The chemist surveys revealed that not all published PAINS substructure alerts are considered productive by the medicinal chemistry community and in agreement with previously published results from other institutions, QED scores tracked quite well with chemists' notions of chemical attractiveness.

Inflammation-Induced, STING-Dependent Autophagy Restricts Zika Virus Infection in the Drosophila Brain.

The emerging arthropod-borne flavivirus Zika virus (ZIKV) is associated with neurological complications. Innate immunity is essential for the control of virus infection, but the innate immune mechanisms that impact viral infection of neurons remain poorly defined. Using the genetically tractable Drosophila system, we show that ZIKV infection of the adult fly brain leads to NF-kB-dependent inflammatory signaling, which serves to limit infection. ZIKV-dependent NF-kB activation induces the expression of Drosophila stimulator of interferon genes (dSTING) in the brain. dSTING protects against ZIKV by inducing autophagy in the brain. Loss of autophagy leads to increased ZIKV infection of the brain and death of the infected fly, while pharmacological activation of autophagy is protective. These data suggest an essential role for an inflammation-dependent STING pathway in the control of neuronal infection and a conserved role for STING in antimicrobial autophagy, which may represent an ancestral function for this essential innate immune sensor.

Endogenous Locus Reporter Assays.

Reporter gene assays are widely used in high-throughput screening (HTS) to identify compounds that modulate gene expression. Traditionally a reporter gene assay is built by cloning an endogenous promoter sequence or synthetic response elements in the regulatory region of a reporter gene to monitor transcriptional activity of a specific biological process (exogenous reporter assay). In contrast, an endogenous locus reporter has a reporter gene inserted in the endogenous gene locus that allows the reporter gene to be expressed under the control of the same regulatory elements as the endogenous gene, thus more accurately reflecting the changes seen in the regulation of the actual gene. In this chapter, we introduce some of the considerations behind building a reporter gene assay for high-throughput compound screening and describe the methods we have utilized to establish 1536-well format endogenous locus reporter and exogenous reporter assays for the screening of compounds that modulate Myc pathway activity.

High-Content Reporter Assays.

While luminescent reporter gene assays allow for a rapid and relatively interference free assessment of the activation state of a luminescent reporter, fluorescent reporters do not. They suffer from artifacts such as compound fluorescence and cellular debris which makes the assessment of whole well fluorescence signals difficult. However, the use of high-content screening allows for the isolation of individual cells, segmentation and thus enables the screener to utilize fluorescent reporters to assess the activation state of such a high-content reporter on a cell by cell level, thus minimizing artifacts. Here we discuss the use of such a high-content reporter that enables screening for compounds useful for HIV reactivation on Jurkat cells with high-content screening.

Is Multitask Deep Learning Practical for Pharma?

Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery.

Deorphanization strategies for dark chemical matter.

The term dark chemical matter (DCM) was recently introduced for those molecules in a screening collection that have never shown any substantial biological activity despite having been tested in hundreds of high-throughput assays. It was suggested that, if hits emerge from this compound pool in future screening campaigns, they should be prioritized due to their exquisite selectivity profile. In this article we define DCM at our company and describe on-going efforts to shed light on the bioactivity of these apparently silent compounds, with an emphasis on multi-parametric profiling methods. It is also demonstrated that compounds that are dark within one institution might be found active in another, but typically show the foretold selectivity.

Representing high throughput expression profiles via perturbation barcodes reveals compound targets.

High throughput mRNA expression profiling can be used to characterize the response of cell culture models to perturbations such as pharmacologic modulators and genetic perturbations. As profiling campaigns expand in scope, it is important to homogenize, summarize, and analyze the resulting data in a manner that captures significant biological signals in spite of various noise sources such as batch effects and stochastic variation. We used the L1000 platform for large-scale profiling of 978 representative genes across thousands of compound treatments. Here, a method is described that uses deep learning techniques to convert the expression changes of the landmark genes into a perturbation barcode that reveals important features of the underlying data, performing better than the raw data in revealing important biological insights. The barcode captures compound structure and target information, and predicts a compound's high throughput screening promiscuity, to a higher degree than the original data measurements, indicating that the approach uncovers underlying factors of the expression data that are otherwise entangled or masked by noise. Furthermore, we demonstrate that visualizations derived from the perturbation barcode can be used to more sensitively assign functions to unknown compounds through a guilt-by-association approach, which we use to predict and experimentally validate the activity of compounds on the MAPK pathway. The demonstrated application of deep metric learning to large-scale chemical genetics projects highlights the utility of this and related approaches to the extraction of insights and testable hypotheses from big, sometimes noisy data.

Nup98 promotes antiviral gene expression to restrict RNA viral infection in Drosophila.

In response to infection, the innate immune system rapidly activates an elaborate and tightly orchestrated gene expression program to induce critical antimicrobial genes. While many key players in this program have been identified in disparate biological systems, it is clear that there are additional uncharacterized mechanisms at play. Our previous studies revealed that a rapidly-induced antiviral gene expression program is active against disparate human arthropod-borne viruses in Drosophila. Moreover, one-half of this program is regulated at the level of transcriptional pausing. Here we found that Nup98, a virus-induced gene, was antiviral against a panel of viruses both in cells and adult flies since its depletion significantly enhanced viral infection. Mechanistically, we found that Nup98 promotes antiviral gene expression in Drosophila at the level of transcription. Expression profiling revealed that the virus-induced activation of 36 genes was abrogated upon loss of Nup98; and we found that a subset of these Nup98-dependent genes were antiviral. These Nup98-dependent virus-induced genes are Cdk9-dependent and translation-independent suggesting that these are rapidly induced primary response genes. Biochemically, we demonstrate that Nup98 is directly bound to the promoters of virus-induced genes, and that it promotes occupancy of the initiating form of RNA polymerase II at these promoters, which are rapidly induced on viral infection to restrict human arboviruses in insects.

Dicer-2 processes diverse viral RNA species.

RNA silencing pathways play critical roles in gene regulation, virus infection, and transposon control. RNA interference (RNAi) is mediated by small interfering RNAs (siRNAs), which are liberated from double-stranded (ds)RNA precursors by Dicer and guide the RNA-induced silencing complex (RISC) to targets. Although principles governing small RNA sorting into RISC have been uncovered, the spectrum of RNA species that can be targeted by Dicer proteins, particularly the viral RNAs present during an infection, are poorly understood. Dicer-2 potently restricts viral infection in insects by generating virus-derived siRNAs from viral RNA. To better characterize the substrates of Dicer-2, we examined the virus-derived siRNAs produced during the Drosophila antiviral RNAi response to four different viruses using high-throughput sequencing. We found that each virus was uniquely targeted by the RNAi pathway; dicing substrates included dsRNA replication intermediates and intramolecular RNA stem loops. For instance, a putative intergenic RNA hairpin encoded by Rift Valley Fever virus generates abundant small RNAs in both Drosophila and mosquito cells, while repetitive sequences within the genomic termini of Vaccinia virus, which give rise to abundant small RNAs in Drosophila, were found to be transcribed in both insect and mammalian cells. Moreover, we provide evidence that the RNA species targeted by Dicer-2 can be modulated by the presence of a viral suppressor of RNAi. This study uncovered several novel, heavily targeted features within viral genomes, offering insight into viral replication, viral immune evasion strategies, and the mechanism of antiviral RNAi.

Parsimonious discovery of synergistic drug combinations.

Combination therapies that enhance efficacy or permit reduced dosages to be administered have seen great success in a variety of therapeutic applications. More fundamentally, the discovery of epistatic pathway interactions not only informs pharmacologic intervention but can be used to better understand the underlying biological system. There is, however, no systematic and efficient method to identify interacting activities as candidates for combination therapy and, in particular, to identify those with synergistic activities. We devised a pooled, self-deconvoluting screening paradigm for the efficient comprehensive interrogation of all pairs of compounds in 1000-compound libraries. We demonstrate the power of the method to recover established synergistic interactions between compounds. We then applied this approach to a cell-based screen for anti-inflammatory activities using an assay for lipopolysaccharide/interferon-induced acute phase response of a monocytic cell line. The described method, which is >20 times as efficient as a naïve approach, was used to test all pairs of 1027 bioactive compounds for interleukin-6 suppression, yielding 11 pairs of compounds that show synergy. These 11 pairs all represent the same two activities: ß-adrenergic receptor agonists and phosphodiesterase-4 inhibitors. These activities both act through cyclic AMP elevation and are known to be anti-inflammatory alone and to synergize in combination. Thus we show proof of concept for a robust, efficient technique for the identification of synergistic combinations. Such a tool can enable qualitatively new scales of pharmacological research and chemical genetics.