Comprehensive mechanistic analysis of hits from high-throughput and docking screens against beta-lactamase

Identifying Artifacts from Large Library Docking

While large library docking has discovered potent ligands for multiple targets, as the libraries have grown the hit lists can become dominated by rare artifacts that cheat our scoring functions. Here, we investigate rescoring top-ranked docked molecules with orthogonal methods to identify these artifacts, exploring implicit solvent models and absolute binding free energy perturbation as cross-filters. In retrospective studies, this approach deprioritized high-ranking nonbinders for nine targets while leaving true ligands relatively unaffected. We tested the method prospectively against hits from docking against AmpC β-lactamase. We prioritized 128 high-ranking molecules for synthesis and testing, a mixture of 39 molecules flagged as likely cheaters and 89 that were plausible inhibitors. None of the predicted cheating compounds inhibited AmpC detectably, while 57% of the 89 plausible compounds did so. As our libraries continue to grow, deprioritizing docking artifacts by rescoring with orthogonal methods may find wide use.

Computationally accelerated identification of P-glycoprotein inhibitors

Overexpression of the polyspecific efflux transporter, P-glycoprotein (P-gp, MDR1, ABCB1 ), is a major mechanism by which cancer cells acquire multidrug resistance (MDR), the resistance to diverse chemotherapeutic drugs. Inhibiting drug transport by P-gp can resensitize cancer cells to chemotherapy, but there are no P-gp inhibitors available to patients. Clinically unsuccessful P-gp inhibitors tend to bind at the pump's transmembrane drug binding domains and are often P-gp transport substrates, resulting in lowered intracellular concentration of the drug and altered pharmacokinetics. In prior work, we used computationally accelerated drug discovery to identify novel P-gp inhibitors that target the pump's cytoplasmic nucleotide binding domains. Our first-draft study provided conclusive evidence that the nucleotide binding domains of P-gp are viable targets for drug discovery. Here we develop an enhanced, computationally accelerated drug discovery pipeline that expands upon our prior work by iteratively screening compounds against multiple conformations of P-gp with molecular docking. Targeted molecular dynamics simulations with our homology model of human P-gp were used to generate docking receptors in conformations mimicking a putative drug transport cycle. We offset the increased computational complexity using custom Tanimoto chemical datasets, which maximize the chemical diversity of ligands screened by docking. Using our expanded, virtual-assisted pipeline, we identified nine novel P-gp inhibitors that reverse MDR in two types of P-gp overexpressing human cancer cell lines, reflecting a 13.4% hit rate. Of these inhibitors, all were non-toxic to non-cancerous human cells, and six were not likely to be transport substrates of P-gp. Our novel P-gp inhibitors are chemically diverse and are good candidates for lead optimization. Our results demonstrate that the nucleotide binding domains of P-gp are an underappreciated target in the effort to reverse P-gp-mediated multidrug resistance in cancer.

DeltaGzip: Computing Biopolymer-Ligand Binding Affinity via Kolmogorov Complexity and Lossless Compression

The design of biosequences for biosensing and therapeutics is a challenging multistep search and optimization task. In principle, computational modeling may speed up the design process by virtual screening of sequences based on their binding affinities to target molecules. However, in practice, existing machine-learned models trained to predict binding affinities lack the flexibility with respect to reaction conditions, and molecular dynamics simulations that can incorporate reaction conditions suffer from high computational costs. Here, we describe a computational approach called DeltaGzip that evaluates the free energy of binding in biopolymer-ligand complexes from ultrashort equilibrium molecular dynamics simulations. The entropy of binding is evaluated using the Kolmogorov complexity definition of entropy and approximated using a lossless compression algorithm, Gzip. We benchmark the method on a well-studied data set of protein-ligand complexes comparing the predictions of DeltaGzip to the free energies of binding obtained using Jarzynski equality and experimental measurements.

Identifying Artifacts from Large Library Docking

While large library docking has discovered potent ligands for multiple targets, as the libraries have grown, the very top of the hit-lists can become populated with artifacts that cheat our scoring functions. Though these cheating molecules are rare, they become ever-more dominant with library growth. Here, we investigate rescoring top-ranked molecules from docking screens with orthogonal methods to identify these artifacts, exploring implicit solvent models and absolute binding free energy perturbation (AB-FEP) as cross-filters. In retrospective studies, this approach deprioritized high-ranking non-binders for nine targets while leaving true ligands relatively unaffected. We tested the method prospectively against results from large library docking AmpC β-lactamase. From the very top of the docking hit lists, we prioritized 128 molecules for synthesis and experimental testing, a mixture of 39 molecules that rescoring flagged as likely cheaters and another 89 that were plausible true actives. None of the 39 predicted cheating compounds inhibited AmpC up to 200 μ M in enzyme assays, while 57% of the 89 plausible true actives did do so, with 19 of them inhibiting the enzyme with apparentK i values better than 50 μ M . As our libraries continue to grow, a strategy of catching docking artifacts by rescoring with orthogonal methods may find wide use in the field.

The impact of Library Size and Scale of Testing on Virtual Screening

Virtual libraries for ligand discovery have recently increased 10,000-fold, and this is thought to have improved hit rates and potencies from library docking. This idea has not, however, been experimentally tested in direct comparisons of larger-vs-smaller libraries. Meanwhile, though libraries have exploded, the scale of experimental testing has little changed, with often only dozens of high-ranked molecules investigated, making interpretation of hit rates and affinities uncertain. Accordingly, we docked a 1.7 billion molecule virtual library against the model enzyme AmpC β-lactamase, testing 1,521 new molecules and comparing the results to the same screen with a library of 99 million molecules, where only 44 molecules were tested. Encouragingly, the larger screen outperformed the smaller one: hit rates improved by two-fold, more new scaffolds were discovered, and potency improved. Overall, 50-fold more inhibitors were found, supporting the idea that there are many more compounds to be discovered than are being tested. With so many compounds evaluated, we could ask how the results vary with number tested, sampling smaller sets at random from the 1521. Hit rates and affinities were highly variable when we only sampled dozens of molecules, and it was only when we included several hundred molecules that results converged. As docking scores improved, so too did the likelihood of a molecule binding; hit rates improved steadily with docking score, as did affinities. This also appeared true on reanalysis of large-scale results against the σ2 and dopamine D4 receptors. It may be that as the scale of both the virtual libraries and their testing grows, not only are better ligands found but so too does our ability to rank them.

ChemFH: an integrated tool for screening frequent false positives in chemical biology and drug discovery

High-throughput screening rapidly tests an extensive array of chemical compounds to identify hit compounds for specific biological targets in drug discovery. However, false-positive results disrupt hit compound screening, leading to wastage of time and resources. To address this, we propose ChemFH, an integrated online platform facilitating rapid virtual evaluation of potential false positives, including colloidal aggregators, spectroscopic interference compounds, firefly luciferase inhibitors, chemical reactive compounds, promiscuous compounds, and other assay interferences. By leveraging a dataset containing 823 391 compounds, we constructed high-quality prediction models using multi-task directed message-passing network (DMPNN) architectures combining uncertainty estimation, yielding an average AUC value of 0.91. Furthermore, ChemFH incorporated 1441 representative alert substructures derived from the collected data and ten commonly used frequent hitter screening rules. ChemFH was validated with an external set of 75 compounds. Subsequently, the virtual screening capability of ChemFH was successfully confirmed through its application to five virtual screening libraries. Furthermore, ChemFH underwent additional validation on two natural products and FDA-approved drugs, yielding reliable and accurate results. ChemFH is a comprehensive, reliable, and computationally efficient screening pipeline that facilitates the identification of true positive results in assays, contributing to enhanced efficiency and success rates in drug discovery. ChemFH is freely available via https://chemfh.scbdd.com/.

Development of non-β-Lactam covalent allosteric inhibitors targeting PBP2a in Methicillin-Resistant Staphylococcus aureus

Methicillin-resistant Staphylococcus aureus (MRSA), a Gram-positive bacterial pathogen, continues to pose a serious threat to the current public health system in our society. The high level of resistance to β-lactam antibiotics in MRSA is attributed to the expression of penicillin-binding protein 2a (PBP2a), which catalyzes cell wall cross-linking. According to numerous research reports, the activity of the PBP2a protein is known to be regulated by an allosteric site distinct from the active site where cell wall cross-linking occurs. Here, we conducted a screening of 113 compounds containing a 1,3,4-oxadiazole core to design new covalent inhibitors targeting the allosteric site of PBP2a and establish their structural-activity relationship. The stereochemically selective synthesis of sulfonyl oxadiazole compounds identified in the initial screening resulted in a maximum eightfold enhancement in cell inhibition activity. The sulfonyl oxadiazole-based compounds formulated as PEG-based ointments, with low toxicity test results on human cells (CC 50 : >78μM), demonstrated potent antimicrobial effects not only in a mouse skin wound infection model but also against oxacillin-resistant clinical isolate MRSA (IC 50 ≈ 1μM), as evidenced by the results. Furthermore, additional studies utilizing LC-MS/MS and in-silico approaches clearly support the allosteric site covalent binding mechanism through the nucleophilic aromatic substitution (S N Ar) reaction, as well as its association with the closure of the major active site of PBP2a.

The European Lead Factory: Results from a decade of collaborative, public-private, drug discovery programs

The European Lead Factory (ELF) is a consortium of universities and small and medium-sized enterprises (SMEs) dedicated to drug discovery, and the pharmaceutical industry. This unprecedented consortium provides high-throughput screening, triage, and hit validation, including to non-consortium members. The ELF library was created through a novel compound-sharing model between nine pharmaceutical companies and expanded through library synthesis by chemistry-specialized SMEs. The library has been screened against ∼270 different targets and 15 phenotypic assays, and hits have been developed to form the basis of patents and spin-off companies. Here, we review the outcome of screening campaigns of the ELF, including the performance and physicochemical properties of the library, identification of possible frequent hitter compounds, and the effectiveness of the compound-sharing model.

Evaluating Known Zika Virus NS2B-NS3 Protease Inhibitor Scaffolds via In Silico Screening and Biochemical Assays

The NS2B-NS3 protease (NS2B-NS3pro) is regarded as an interesting molecular target for drug design, discovery, and development because of its essential role in the Zika virus (ZIKV) cycle. Although no NS2B-NS3pro inhibitors have reached clinical trials, the employment of drug-like scaffolds can facilitate the screening process for new compounds. In this study, we performed a combination of ligand-based and structure-based in silico methods targeting two known non-peptide small-molecule scaffolds with micromolar inhibitory activity against ZIKV NS2B-NS3pro by a virtual screening (VS) of promising compounds. Based on these two scaffolds, we selected 13 compounds from an initial library of 509 compounds from ZINC15's similarity search. These compounds exhibited structural modifications that are distinct from previously known compounds yet keep pertinent features for binding. Despite promising outcomes from molecular docking and initial enzymatic assays against NS2B-NS3pro, confirmatory assays with a counter-screening enzyme revealed an artifactual inhibition of the assessed compounds. However, we report two compounds, 9 and 11, that exhibited antiviral properties at a concentration of 50 μM in cellular-based assays. Overall, this study provides valuable insights into the ongoing research on anti-ZIKV compounds to facilitate and improve the development of new inhibitors.

Large library docking for novel SARS-CoV-2 main protease non-covalent and covalent inhibitors

Antiviral therapeutics to treat SARS-CoV-2 are needed to diminish the morbidity of the ongoing COVID-19 pandemic. A well-precedented drug target is the main viral protease (MPro ), which is targeted by an approved drug and by several investigational drugs. Emerging viral resistance has made new inhibitor chemotypes more pressing. Adopting a structure-based approach, we docked 1.2 billion non-covalent lead-like molecules and a new library of 6.5 million electrophiles against the enzyme structure. From these, 29 non-covalent and 11 covalent inhibitors were identified in 37 series, the most potent having an IC50 of 29 and 20 μM, respectively. Several series were optimized, resulting in low micromolar inhibitors. Subsequent crystallography confirmed the docking predicted binding modes and may template further optimization. While the new chemotypes may aid further optimization of MPro inhibitors for SARS-CoV-2, the modest success rate also reveals weaknesses in our approach for challenging targets like MPro versus other targets where it has been more successful, and versus other structure-based techniques against MPro itself.

High-Throughput Screening of Natural Product and Synthetic Molecule Libraries for Antibacterial Drug Discovery

Due to the continued emergence of resistance and a lack of new and promising antibiotics, bacterial infection has become a major public threat. High-throughput screening (HTS) allows rapid screening of a large collection of molecules for bioactivity testing and holds promise in antibacterial drug discovery. More than 50% of the antibiotics that are currently available on the market are derived from natural products. However, with the easily discoverable antibiotics being found, finding new antibiotics from natural sources has seen limited success. Finding new natural sources for antibacterial activity testing has also proven to be challenging. In addition to exploring new sources of natural products and synthetic biology, omics technology helped to study the biosynthetic machinery of existing natural sources enabling the construction of unnatural synthesizers of bioactive molecules and the identification of molecular targets of antibacterial agents. On the other hand, newer and smarter strategies have been continuously pursued to screen synthetic molecule libraries for new antibiotics and new druggable targets. Biomimetic conditions are explored to mimic the real infection model to better study the ligand-target interaction to enable the designing of more effective antibacterial drugs. This narrative review describes various traditional and contemporaneous approaches of high-throughput screening of natural products and synthetic molecule libraries for antibacterial drug discovery. It further discusses critical factors for HTS assay design, makes a general recommendation, and discusses possible alternatives to traditional HTS of natural products and synthetic molecule libraries for antibacterial drug discovery.

Enzyme Inhibitors from Gorgonians and Soft Corals

For decades, gorgonians and soft corals have been considered promising sources of bioactive compounds, attracting the interest of scientists from different fields. As the most abundant bioactive compounds within these organisms, terpenoids, steroids, and alkaloids have received the highest coverage in the scientific literature. However, enzyme inhibitors, a functional class of bioactive compounds with high potential for industry and biomedicine, have received much less notoriety. Thus, we revised scientific literature (1974-2022) on the field of marine natural products searching for enzyme inhibitors isolated from these taxonomic groups. In this review, we present representative enzyme inhibitors from an enzymological perspective, highlighting, when available, data on specific targets, structures, potencies, mechanisms of inhibition, and physiological roles for these molecules. As most of the characterization studies for the new inhibitors remain incomplete, we also included a methodological section presenting a general strategy to face this goal by accomplishing STRENDA (Standards for Reporting Enzymology Data) project guidelines.

Class C β-Lactamases: Molecular Characteristics

Class C β-lactamases or cephalosporinases can be classified into two functional groups (1, 1e) with considerable molecular variability (≤20% sequence identity). These enzymes are mostly encoded by chromosomal and inducible genes and are widespread among bacteria, including Proteobacteria in particular. Molecular identification is based principally on three catalytic motifs (⁶⁴SXSK, ¹⁵⁰YXN, ³¹⁵KTG), but more than 70 conserved amino-acid residues (≥90%) have been identified, many close to these catalytic motifs. Nevertheless, the identification of a tiny, phylogenetically distant cluster (including enzymes from the genera Legionella, Bradyrhizobium, and Parachlamydia) has raised questions about the possible existence of a C2 subclass of β-lactamases, previously identified as serine hydrolases. In a context of the clinical emergence of extended-spectrum AmpC β-lactamases (ESACs), the genetic modifications observed in vivo and in vitro (point mutations, insertions, or deletions) during the evolution of these enzymes have mostly involved the Ω- and H-10/R2-loops, which vary considerably between genera, and, in some cases, the conserved triplet ¹⁵⁰YXN. Furthermore, the conserved deletion of several amino-acid residues in opportunistic pathogenic species of Acinetobacter, such as A. baumannii, A. calcoaceticus, A. pittii and A. nosocomialis (deletion of residues 304-306), and in Hafnia alvei and H. paralvei (deletion of residues 289-290), provides support for the notion of natural ESACs. The emergence of higher levels of resistance to β-lactams, including carbapenems, and to inhibitors such as avibactam is a reality, as the enzymes responsible are subject to complex regulation encompassing several other genes (ampR, ampD, ampG, etc.). Combinations of resistance mechanisms may therefore be at work, including overproduction or change in permeability, with the loss of porins and/or activation of efflux systems.

Isometric Stratified Ensembles: A Partial and Incremental Adaptive Applicability Domain and Consensus-Based Classification Strategy for Highly Imbalanced Data Sets with Application to Colloidal Aggregation

Partial and incremental stratification analysis of a quantitative structure-interference relationship (QSIR) is a novel strategy intended to categorize classification provided by machine learning techniques. It is based on a 2D mapping of classification statistics onto two categorical axes: the degree of consensus and level of applicability domain. An internal cross-validation set allows to determine the statistical performance of the ensemble at every 2D map stratum and hence to define isometric local performance regions with the aim of better hit ranking and selection. During training, isometric stratified ensembles (ISE) applies a recursive decorrelated variable selection and considers the cardinal ratio of classes to balance training sets and thus avoid bias due to possible class imbalance. To exemplify the interest of this strategy, three different highly imbalanced PubChem pairs of AmpC β-lactamase and cruzain inhibition assay campaigns of colloidal aggregators and complementary aggregators data set available at the AGGREGATOR ADVISOR predictor web page were employed. Statistics obtained using this new strategy show outperforming results compared to former published tools, with and without a classical applicability domain. ISE performance on classifying colloidal aggregators shows from a global AUC of 0.82, when the whole test data set is considered, up to a maximum AUC of 0.88, when its highest confidence isometric stratum is retained.

Gains from no real PAINS: Where 'Fair Trial Strategy' stands in the development of multi-target ligands

Compounds that selectively modulate multiple targets can provide clinical benefits and are an alternative to traditional highly selective agents for unique targets. High-throughput screening (HTS) for multitarget-directed ligands (MTDLs) using approved drugs, and fragment-based drug design has become a regular strategy to achieve an ideal multitarget combination. However, the unexpected presence of pan-assay interference compounds (PAINS) suspects in the development of MTDLs frequently results in nonspecific interactions or other undesirable effects leading to artefacts or false-positive data of biological assays. Publicly available filters can help to identify PAINS suspects; however, these filters cannot comprehensively conclude whether these suspects are "bad" or innocent. Additionally, these in silico approaches may inappropriately label a ligand as PAINS. More than 80% of the initial hits can be identified as PAINS by the filters if appropriate biochemical tests are not used resulting in false positive data that are unacceptable for medicinal chemists in manuscript peer review and future studies. Therefore, extensive offline experiments should be used after online filtering to discriminate "bad" PAINS and avoid incorrect evaluation of good scaffolds. We suggest that the use of "Fair Trial Strategy" to identify interesting molecules in PAINS suspects to provide certain structure‒function insight in MTDL development.

A practical guide to large-scale docking

Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.

Structure-Based Optimization of Quinazolines as Cruzain and TbrCATL Inhibitors

The cysteine proteases, cruzain and TbrCATL (rhodesain), are therapeutic targets for Chagas disease and Human African Trypanosomiasis, respectively. Among the known inhibitors for these proteases, we have described N⁴-benzyl-N²-phenylquinazoline-2,4-diamine (compound 7 in the original publication, 1a in this study), as a competitive cruzain inhibitor (K_i = 1.4 μM). Here, we describe the synthesis and biological evaluation of 22 analogs of 1a, containing modifications in the quinazoline core, and in the substituents in positions 2 and 4 of this ring. The analogs demonstrate low micromolar inhibition of the target proteases and cidal activity against Trypanosoma cruzi with up to two log selectivity indices in counterscreens with myoblasts. Fourteen compounds were active against Trypanosoma brucei at low to mid micromolar concentrations. During the optimization of 1a, structure-based design and prediction of physicochemical properties were employed to maintain potency against the enzymes while removing colloidal aggregator characteristics observed for some molecules in this series.

Identifying SARS-CoV-2 antiviral compounds by screening for small molecule inhibitors of nsp13 helicase

The coronavirus disease 2019 (COVID-19) pandemic, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global public health challenge. While the efficacy of vaccines against emerging and future virus variants remains unclear, there is a need for therapeutics. Repurposing existing drugs represents a promising and potentially rapid opportunity to find novel antivirals against SARS-CoV-2. The virus encodes at least nine enzymatic activities that are potential drug targets. Here, we have expressed, purified and developed enzymatic assays for SARS-CoV-2 nsp13 helicase, a viral replication protein that is essential for the coronavirus life cycle. We screened a custom chemical library of over 5000 previously characterized pharmaceuticals for nsp13 inhibitors using a fluorescence resonance energy transfer-based high-throughput screening approach. From this, we have identified FPA-124 and several suramin-related compounds as novel inhibitors of nsp13 helicase activity in vitro. We describe the efficacy of these drugs using assays we developed to monitor SARS-CoV-2 growth in Vero E6 cells.

Detection of Small-Molecule Aggregation with High-Throughput Microplate Biophysical Methods

Small-molecule drug discovery can be hindered by the formation of aggregates that act as non-selective inhibitors of drug targets. Such aggregates appear as false positives in high-throughput screening campaigns and can bedevil structure-activity relationships during compound optimization. Protocols are described for resonant waveguide grating (RWG) and dynamic light scattering (DLS) as microplate-based high-throughput approaches to identify compound aggregation. Resonant waveguide grating and dynamic light scattering give equivalent results for the compound test set, as assessed with Bland-Altman analysis. © 2019 The Authors. Basic Protocol 1: Resonant waveguide grating (RWG) in 384-well or 1536-well plate format to detect compound aggregation Basic Protocol 2: Dynamic light scattering (DLS) in 384-well plate format to detect compound aggregation.

TREX1 as a Novel Immunotherapeutic Target

Mutations in the TREX1 3' → 5' exonuclease are associated with a spectrum of autoimmune disease phenotypes in humans and mice. Failure to degrade DNA activates the cGAS-STING DNA-sensing pathway signaling a type-I interferon (IFN) response that ultimately drives immune system activation. TREX1 and the cGAS-STING DNA-sensing pathway have also been implicated in the tumor microenvironment, where TREX1 is proposed to degrade tumor-derived DNA that would otherwise activate cGAS-STING. If tumor-derived DNA were not degraded, the cGAS-STING pathway would be activated to promote IFN-dependent antitumor immunity. Thus, we hypothesize TREX1 exonuclease inhibition as a novel immunotherapeutic strategy. We present data demonstrating antitumor immunity in the TREX1 D18N mouse model and discuss theory surrounding the best strategy for TREX1 inhibition. Potential complications of TREX1 inhibition as a therapeutic strategy are also discussed.

A Crowding Barrier to Protein Inhibition in Colloidal Aggregates

Small molecule colloidal aggregates adsorb and partially denature proteins, inhibiting them artifactually. Oddly, this inhibition is typically time-dependent. Two mechanisms might explain this: low concentrations of the colloid and enzyme might mean low encounter rates, or colloid-based protein denaturation might impose a kinetic barrier. These two mechanisms should have different concentration dependencies. Perplexingly, when enzyme concentration was increased, incubation times actually lengthened, inconsistent with both models and with classical chemical kinetics of solution species. We therefore considered molecular crowding, where colloids with lower protein surface density demand a shorter incubation time than more crowded colloids. To test this, we grew and shrank colloid surface area. As the surface area shrank, the incubation time lengthened, while as it increased, the converse was true. These observations support a crowding effect on protein binding to colloidal aggregates. Implications for drug delivery and for detecting aggregation-based inhibition will be discussed.

Predictive Global Models of Cruzain Inhibitors with Large Chemical Coverage

Chagas disease affects 8-11 million people worldwide, most of them living in Latin America. Moreover, migratory phenomena have spread the infection beyond endemic areas. Efforts for the development of new pharmacological therapies are paramount as the pharmacological profile of the two marketed drugs currently available, nifurtimox and benznidazole, needs to be improved. Cruzain, a parasitic cysteine protease, is one of the most attractive biological targets due to its roles in parasite survival and immune evasion. In this work, we compiled and curated a database of diverse cruzain inhibitors previously reported in the literature. From this data set, quantitative structure-activity relationship (QSAR) models for the prediction of their pIC50 values were generated using k-nearest neighbors and random forest algorithms. Local and global models were calculated and compared. The statistical parameters for internal and external validation indicate a significant predictability, with q loo 2 values around 0.66 and 0.61 and external R 2 coefficients of 0.725 and 0.766. The applicability domain is quantitatively defined, according to QSAR good practices, using the leverage and similarity methods. The models described in this work are readily available in a Python script for the discovery of novel cruzain inhibitors.

Novel drug discovery platform for spinocerebellar ataxia, using fluorescence technology targeting β-III-spectrin

Numerous diseases are linked to mutations in the actin-binding domains (ABDs) of conserved cytoskeletal proteins, including β-III-spectrin, α-actinin, filamin, and dystrophin. A β-III-spectrin ABD mutation (L253P) linked to spinocerebellar ataxia type 5 (SCA5) causes a dramatic increase in actin binding. Reducing actin binding of L253P is thus a potential therapeutic approach for SCA5 pathogenesis. Here, we validate a high-throughput screening (HTS) assay to discover potential disrupters of the interaction between the mutant β-III-spectrin ABD and actin in live cells. This assay monitors FRET between fluorescent proteins fused to the mutant ABD and the actin-binding peptide Lifeact, in HEK293-6E cells. Using a specific and high-affinity actin-binding tool compound, swinholide A, we demonstrate HTS compatibility with an excellent Z'-factor of 0.67 ± 0.03. Screening a library of 1280 pharmacologically active compounds in 1536-well plates to determine assay robustness, we demonstrate high reproducibility across plates and across days. We identified nine Hits that reduced FRET between Lifeact and ABD. Four of those Hits were found to reduce Lifeact cosedimentation with actin, thus establishing the potential of our assay for detection of actin-binding modulators. Concurrent to our primary FRET assay, we also developed a high-throughput compatible counter screen to remove undesirable FRET Hits. Using the FRET Hits, we show that our counter screen is sensitive to undesirable compounds that cause cell toxicity or ABD aggregation. Overall, our FRET-based HTS platform sets the stage to screen large compound libraries for modulators of β-III-spectrin, or disease-linked spectrin-related proteins, for therapeutic development.

Fragment Binding to the Nsp3 Macrodomain of SARS-CoV-2 Identified Through Crystallographic Screening and Computational Docking

The SARS-CoV-2 macrodomain (Mac1) within the non-structural protein 3 (Nsp3) counteracts host-mediated antiviral ADP-ribosylation signalling. This enzyme is a promising antiviral target because catalytic mutations render viruses non-pathogenic. Here, we report a massive crystallographic screening and computational docking effort, identifying new chemical matter primarily targeting the active site of the macrodomain. Crystallographic screening of diverse fragment libraries resulted in 214 unique macrodomain-binding fragments, out of 2,683 screened. An additional 60 molecules were selected from docking over 20 million fragments, of which 20 were crystallographically confirmed. X-ray data collection to ultra-high resolution and at physiological temperature enabled assessment of the conformational heterogeneity around the active site. Several crystallographic and docking fragment hits were validated for solution binding using three biophysical techniques (DSF, HTRF, ITC). Overall, the 234 fragment structures presented explore a wide range of chemotypes and provide starting points for development of potent SARS-CoV-2 macrodomain inhibitors.

Small-molecule active pharmaceutical ingredients of approved cancer therapeutics inhibit human aspartate/asparagine-β-hydroxylase

Human aspartate/asparagine-β-hydroxylase (AspH) is a 2-oxoglutarate (2OG) dependent oxygenase that catalyses the hydroxylation of Asp/Asn-residues of epidermal growth factor-like domains (EGFDs). AspH is reported to be upregulated on the cell surface of invasive cancer cells in a manner distinguishing healthy from cancer cells. We report studies on the effect of small-molecule active pharmaceutical ingredients (APIs) of human cancer therapeutics on the catalytic activity of AspH using a high-throughput mass spectrometry (MS)-based inhibition assay. Human B-cell lymphoma-2 (Bcl-2)-protein inhibitors, including the (R)-enantiomer of the natural product gossypol, were observed to efficiently inhibit AspH, as does the antitumor antibiotic bleomycin A2. The results may help in the design of AspH inhibitors with the potential of increased selectivity compared to the previously identified Fe(II)-chelating or 2OG-competitive inhibitors. With regard to the clinical use of bleomycin A2 and of the Bcl-2 inhibitor venetoclax, the results suggest that possible side-effects mediated through the inhibition of AspH and other 2OG oxygenases should be considered.

Fluorogenic Probes/Inhibitors of β-Lactamase and their Applications in Drug-Resistant Bacteria

β-Lactam antibiotics are generally perceived as one of the greatest inventions of the 20^th century, and these small molecular compounds have saved millions of lives. However, upon clinical application of antibiotics, the β-lactamase secreted by pathogenic bacteria can lead to the gradual development of drug resistance. β-Lactamase is a hydrolase that can efficiently hydrolyze and destroy β-lactam antibiotics. It develops and spreads rapidly in pathogens, and the drug-resistant bacteria pose a severe threat to human health and development. As a result, detecting and inhibiting the activities of β-lactamase are of great value for the rational use of antibiotics and the treatment of infectious diseases. At present, many specific detection methods and inhibitors of β-lactamase have been developed and applied in clinical practice. In this Minireview, we describe the resistance mechanism of bacteria producing β-lactamase and further summarize the fluorogenic probes, inhibitors of β-lactamase, and their applications in the treatment of infectious diseases. It may be valuable to design fluorogenic probes with improved selectivity, sensitivity, and effectiveness to further identify the inhibitors for β-lactamases and eventually overcome bacterial resistance.

Perspective on Antibacterial Lead Identification Challenges and the Role of Hypothesis-Driven Strategies

For the past three decades, the pharmaceutical industry has undertaken many diverse approaches to discover novel antibiotics, with limited success. We have witnessed and personally experienced many mistakes, hurdles, and dead ends that have derailed projects and discouraged scientists and business leaders. Of the many factors that affect the outcomes of screening campaigns, a lack of understanding of the properties that drive efflux and permeability requirements across species has been a major barrier for advancing hits to leads. Hits that possess bacterial spectrum have seldom also possessed druglike properties required for developability and safety. Persistence in solving these two key barriers is necessary for the reinvestment into discovering antibacterial agents. This perspective narrates our experience in antibacterial discovery-our lessons learned about antibacterial challenges as well as best practices for screening strategies. One of the tenets that guides us is that drug discovery is a hypothesis-driven science. Application of this principle, at all steps in the antibacterial discovery process, should improve decision making and possibly the odds of what has become, in recent decades, an increasingly challenging endeavor with dwindling success rates.

Synthesis of N-Substituted Benzamide Derivatives and their Evaluation as Antitumor Agents

BACKGROUND

Histone deacetylases inhibitors (HDACIs) with different chemical structures have been reported to play an important role in the treatment of cancer.

OBJECTIVE

The study aims to modify the structure of Entinostat (MS-275) to discover new compounds with improved anti-proliferative activities and perform SAR studies on this class of bioactive compounds.

METHODS

Fourteen N-substituted benzamide derivatives were synthesized and their antiproliferative activities were tested with four cancer cell lines (MCF-7, A549, K562 and MDA-MB- 231) by MTT assay.

RESULTS

Compared with MS-275, six compounds exhibited comparable or even better antiproliferative activities against specific/certain cancer cell lines.

CONCLUSION

The preliminary SARs showed that (ⅰ) the 2-substituent of the phenyl ring in the R group and heteroatoms of amide which can chelate with zinc ion are critical to the antiproliferative activity and (ⅱ) chlorine atom or nitro-group on the same benzene ring largely decreases their anti-proliferative activity. Molecular docking study illustrated the interaction (binding affinity) between the synthesized compounds and HDAC2 was observed to be similar to that of MS-275.

Quantitative high-throughput screening assays for the discovery and development of SIRPα-CD47 interaction inhibitors

CD47 is an immune checkpoint molecule that downregulates key aspects of both the innate and adaptive anti-tumor immune response via its counter receptor SIRPα, and it is expressed at high levels in a wide variety of tumor types. This has led to the development of biologics that inhibit SIRPα engagement including humanized CD47 antibodies and a soluble SIRPα decoy receptor that are currently undergoing clinical trials. Unfortunately, toxicological issues, including anemia related to on-target mechanisms, are barriers to their clinical advancement. Another potential issue with large biologics that bind CD47 is perturbation of CD47 signaling through its high-affinity interaction with the matricellular protein thrombospondin-1 (TSP1). One approach to avoid these shortcomings is to identify and develop small molecule molecular probes and pretherapeutic agents that would (1) selectively target SIRPα or TSP1 interactions with CD47, (2) provide a route to optimize pharmacokinetics, reduce on-target toxicity and maximize tissue penetration, and (3) allow more flexible routes of administration. As the first step toward this goal, we report the development of an automated quantitative high-throughput screening (qHTS) assay platform capable of screening large diverse drug-like chemical libraries to discover novel small molecules that inhibit CD47-SIRPα interaction. Using time-resolved Förster resonance energy transfer (TR-FRET) and bead-based luminescent oxygen channeling assay formats (AlphaScreen), we developed biochemical assays, optimized their performance, and individually tested them in small-molecule library screening. Based on performance and low false positive rate, the LANCE TR-FRET assay was employed in a ~90,000 compound library qHTS, while the AlphaScreen oxygen channeling assay served as a cross-validation orthogonal assay for follow-up characterization. With this multi-assay strategy, we successfully eliminated compounds that interfered with the assays and identified five compounds that inhibit the CD47-SIRPα interaction; these compounds will be further characterized and later disclosed. Importantly, our results validate the large library qHTS for antagonists of CD47-SIRPα interaction and suggest broad applicability of this approach to screen chemical libraries for other protein-protein interaction modulators.

Discovery and characterization of trypanocidal cysteine protease inhibitors from the 'malaria box'

Chagas disease, Human African Trypanosomiasis, and schistosomiasis are neglected parasitic diseases for which new treatments are urgently needed. To identify new chemical leads, we screened the 400 compounds of the Open Access Malaria Box against the cysteine proteases, cruzain (Trypanosoma cruzi), rhodesain (Trypanosoma brucei) and SmCB1 (Schistosoma mansoni), which are therapeutic targets for these diseases. Whereas just three hits were observed for SmCB1, 70 compounds inhibited cruzain or rhodesain by at least 50% at 5 μM. Among those, 15 commercially available compounds were selected for confirmatory assays, given their potency, time-dependent inhibition profile and reported activity against parasites. Additional assays led to the confirmation of four novel classes of cruzain and rhodesain inhibitors, with potency in the low-to mid-micromolar range against enzymes and T. cruzi. Assays against mammalian cathepsins S and B revealed inhibitor selectivity for parasitic proteases. For the two competitive inhibitors identified (compounds 7 and 12), their binding mode was predicted by docking, providing a basis for structure-based optimization efforts. Compound 12 also acted directly against the trypomastigote and the intracellular amastigote forms of T. cruzi at 3 μM. Therefore, through a combination of experimental and computational approaches, we report promising hits for optimization in the development of new trypanocidal drugs.

The compromise of virtual screening and its impact on drug discovery

Introduction: Docking and structure-based virtual screening (VS) have been standard approaches in structure-based design for over two decades. However, our understanding of the limitations, potential, and strength of these techniques has enhanced, raising expectations. Areas covered: Based on a survey of reports in the past five years, we assess whether VS: (1) predicts binding poses in agreement with crystallographic data (when available); (2) is a superior screening tool, as often claimed; (3) is successful in identifying chemical scaffolds that can be starting points for subsequent lead optimization cycles. Data shows that knowledge of the target and its chemotypes in postprocessing lead to viable hits in early drug discovery endeavors. Expert opinion: VS is capable of accurate placements in the pocket for the most part, but does not consistently score screening collections accurately. What matters is capitalization on available resources to get closer to a viable lead or optimizable series. Integration of approaches, subjective hit selection guided by knowledge of the receptor or endogenous ligand, libraries driven by experimental guides, validation studies to identify the best docking/scoring that reproduces experimental findings, constraints regarding receptor-ligand interactions, thoroughly designed methodologies, and predefined cutoff scoring criteria strengthen VS's position in pharmaceutical research.

Specificity of extended O-aryloxycarbonyl hydroxamates as inhibitors of a class C β-lactamase

Class C β-lactamases have previously been shown to be efficiently inactivated by O-aryloxycarbonyl hydroxamates. O-Phenoxycarbonyl-N-benzyloxycarbonylhydroxylamine (1) and O-phenoxycarbonyl-N-(R)-[(4-amino-4-carboxy-1-butyl)oxycarbonyl]hydroxylamine (2), for example, were found to be effective inactivators. The present paper describes a structure-activity study of these molecules to better define the important structural elements for high inhibitory activity. The results show that a well-positioned hydrophobic element (which may interact with the Tyr221 residue of the enzyme) and a negatively charged element, e.g. a carboxylate group (which may interact with Arg204), are required for high reactivity with the enzyme. The new compounds were found to inactivate by forming a carbonyl cross-linked enzyme (probably Ser64OCONHLys 315) as for 1 rather than the inert hydroxamoyl derivative observed with 2.

Emerging drug development technologies targeting ubiquitination for cancer therapeutics

Development of effective cancer therapeutic strategies relies on our ability to interfere with cellular processes that are dysregulated in tumors. Given the essential role of the ubiquitin proteasome system (UPS) in regulating a myriad of cellular processes, it is not surprising that malfunction of UPS components is implicated in numerous human diseases, including many types of cancer. The clinical success of proteasome inhibitors in treating multiple myeloma has further stimulated enthusiasm for targeting UPS proteins for pharmacological intervention in cancer treatment, particularly in the precision medicine era. Unfortunately, despite tremendous efforts, the paucity of potent and selective UPS inhibitors has severely hampered attempts to exploit the UPS for therapeutic benefits. To tackle this problem, many groups have been working on technology advancement to rapidly and effectively screen for potent and specific UPS modulators as intracellular probes or early-phase therapeutic agents. Here, we review several emerging technologies for developing chemical- and protein-based molecules to manipulate UPS enzymatic activity, with the aim of providing an overview of strategies available to target ubiquitination for cancer therapy.

Ultra-large library docking for discovering new chemotypes

Despite intense interest in expanding chemical space, libraries containing hundreds-of-millions to billions of diverse molecules have remained inaccessible. Here we investigate structure-based docking of 170 million make-on-demand compounds from 130 well-characterized reactions. The resulting library is diverse, representing over 10.7 million scaffolds that are otherwise unavailable. For each compound in the library, docking against AmpC β-lactamase (AmpC) and the D4 dopamine receptor were simulated. From the top-ranking molecules, 44 and 549 compounds were synthesized and tested for interactions with AmpC and the D4 dopamine receptor, respectively. We found a phenolate inhibitor of AmpC, which revealed a group of inhibitors without known precedent. This molecule was optimized to 77 nM, which places it among the most potent non-covalent AmpC inhibitors known. Crystal structures of this and other AmpC inhibitors confirmed the docking predictions. Against the D4 dopamine receptor, hit rates fell almost monotonically with docking score, and a hit-rate versus score curve predicted that the library contained 453,000 ligands for the D4 dopamine receptor. Of 81 new chemotypes discovered, 30 showed submicromolar activity, including a 180-pM subtype-selective agonist of the D4 dopamine receptor.

Emerging and Re-Emerging Warheads for Targeted Covalent Inhibitors: Applications in Medicinal Chemistry and Chemical Biology

Targeted covalent inhibitors (TCIs) are designed to bind poorly conserved amino acids by means of reactive groups, the so-called warheads. Currently, targeting noncatalytic cysteine residues with acrylamides and other α,β-unsaturated carbonyl compounds is the predominant strategy in TCI development. The recent ascent of covalent drugs has stimulated considerable efforts to characterize alternative warheads for the covalent-reversible and irreversible engagement of noncatalytic cysteine residues as well as other amino acids. This Perspective article provides an overview of warheads-beyond α,β-unsaturated amides-recently used in the design of targeted covalent ligands. Promising reactive groups that have not yet demonstrated their utility in TCI development are also highlighted. Special emphasis is placed on the discussion of reactivity and of case studies illustrating applications in medicinal chemistry and chemical biology.

First virtual screening and experimental validation of inhibitors targeting GES-5 carbapenemase

The worldwide spread of beta-lactamases with hydrolytic activity extended to last resort carbapenems is aggravating the antibiotic resistance problem and endangers the successful antimicrobial treatment of clinically relevant pathogens. As recently highlighted by the World Health Organization, new strategies to contain antimicrobial resistance are urgently needed. Class A carbapenemases include members of the KPC, GES and SFC families. These enzymes have the ability to hydrolyse penicillins, cephalosporins and carbapenems, while also being less susceptible to available beta-lactam inhibitors, such as clavulanic acid. The KPC family is the most prevalent. It is mostly found on plasmids in Klebsiella pneumoniae, meaning that great amounts of attention, in terms of inhibitor design and structural biology, have been dedicated to it, whereas no efforts have yet been dedicated to GES-type enzymes, despite their ability to rapidly and horizontally disseminate. We herein report the first in silico screening against GES-5, which is the most dangerous GES-type beta-lactamase, using a library of 800K commercially available candidates that all share drug-like properties, such as their MW, logP, rotatable bonds and HBA/HBD atoms. The best screening results were filtered to enrich the number of different chemotypes, and then submitted to molecular docking. The 34 most promising candidates were selected for in vitro validation in biochemical assays against recombinant GES-5. Six hits acted as inhibitors, in the high micromolar range, towards GES-5 and led to the identification of the first, novel chemotypes with inhibitory activity against this clinically relevant carbapenemase.

Be Aware of Aggregators in the Search for Potential Human ecto-5'-Nucleotidase Inhibitors

Promiscuous inhibition due to aggregate formation has been recognized as a major concern in drug discovery campaigns. Here, we report some aggregators identified in a virtual screening (VS) protocol to search for inhibitors of human ecto-5′-nucleotidase (ecto-5′-NT/CD73), a promising target for several diseases and pathophysiological events, including cancer, inflammation and autoimmune diseases. Four compounds (A, B, C and D), selected from the ZINC-11 database, showed IC₅₀ values in the micromolar range, being at the same time computationally predicted as potential aggregators. To confirm if they inhibit human ecto-5′-NT via promiscuous mechanism, forming aggregates, enzymatic assays were done in the presence of 0.01% (v/v) Triton X-100 and an increase in the enzyme concentration by 10-fold. Under both experimental conditions, these four compounds showed a significant decrease in their inhibitory activities. To corroborate these findings, turbidimetric assays were performed, confirming that they form aggregate species. Additionally, aggregation kinetic studies were done by dynamic light scattering (DLS) for compound C. None of the identified aggregators has been previously reported in the literature. For the first time, aggregation and promiscuous inhibition issues were systematically studied and evaluated for compounds selected by VS as potential inhibitors for human ecto-5′-NT. Together, our results reinforce the importance of accounting for potential false-positive hits acting by aggregation in drug discovery campaigns to avoid misleading assay results.

Reviewing Hit Discovery Literature for Difficult Targets: Glutathione Transferase Omega-1 as an Example

Early stage drug discovery reporting on relatively new or difficult targets is often associated with insufficient hit triage. Literature reviews of such targets seldom delve into the detail required to critically analyze the associated screening hits reported. Here we take the enzyme glutathione transferase omega-1 (GSTO1-1) as an example of a relatively difficult target and review the associated literature involving small-molecule inhibitors. As part of this process we deliberately pay closer-than-usual attention to assay interference and hit quality aspects. We believe this Perspective will be a useful guide for future development of GSTO1-1 inhibitors, as well serving as a template for future review formats of new or difficult targets.

Probing the Binding Affinity by Jarzynski's Nonequilibrium Binding Free Energy and Rupture Time

Binding affinity of a small ligand to a receptor is the important quantity in drug design, and it might be characterized by different quantities. The most popular one is the binding free energy, which can be estimated by several methods in conventional molecular dynamics simulation. So far in steered molecular dynamics (SMD), one can use either the rupture force or nonequilibrium pulling work as a measure for binding affinity. In this paper, we have shown that the nonequilibrium binding free energy Δ G_neq^Jar, obtained by Jarzynski's equality at a finite pulling speed, has good correlation with experimental data on inhibition constants, implying that this quantity can be used as a good scoring function for binding affinity. A similar correlation has also been disclosed for binding and unbinding free energy barriers. Applying the SMD method to unbinding of 23 small compounds from the binding site of β-lactamase protein, a bacteria-produced enzyme, we have demonstrated that the rupture or unbinding time strongly correlates with experimental data with correlation level R ≈ 0.84. As follows from the Jarzynski's equality, the rupture time depends on the unbinding barrier exponentially. We show that Δ G_neq^Jar, the rupture time, and binding and unbinding free energy barriers are good descriptors for binding affinity. Our observation may be useful for fast screening of potential leads as the SMD simulation is not time-consuming. On the basis of nonequilibrium simulation, we disclosed that, in agreement with the experiment, the binding time is much longer than the unbinding one.

Assay interference and off-target liabilities of reported histone acetyltransferase inhibitors

Many compounds with potentially reactive chemical motifs and poor physicochemical properties are published as selective modulators of biomolecules without sufficient validation and then propagated in the scientific literature as useful chemical probes. Several histone acetyltransferase (HAT) inhibitors with these liabilities are now routinely used to probe epigenetic pathways. We profile the most commonly used HAT inhibitors and confirm that the majority of them are nonselective interference compounds. Most (15 out of 23, 65%) of the inhibitors are flagged by ALARM NMR, an industry-developed counter-screen for promiscuous compounds. Biochemical counter-screens confirm that most of these compounds are either thiol-reactive or aggregators. Selectivity panels show many of these compounds modulate unrelated targets in vitro, while several also demonstrate nonspecific effects in cell assays. These data demonstrate the usefulness of performing counter-screens for bioassay promiscuity and assay interference, and raise caution about the utility of many widely used, but insufficiently validated, compounds employed in chemical biology.

A substantial obstacle in basic research is the use of poorly validated tool compounds with purported useful biological functions. Here, the authors systematically profile widely used histone acetyltransferase inhibitors and find that the majority are nonselective interference compounds.